The impact of data-driven design on how people experience and interact with websites is huge. It lets designers see how users engage with the site, helping them create easy-to-use and engaging interfaces. This method relies on data to show which buttons get the most clicks, helping designers decide where to put key buttons and arrange the site’s navigation for a better user experience.

Embedding data into the design journey paves the way for unparalleled personalization, elevating the user experience. Websites adapt everyday to their user’s needs, becoming more interesting and fitting.

At its core, data-driven design morphs websites from mere digital showcases into vibrant, user-focused entities. It harmonizes every component, from eye-catching design to the underlying code, ensuring a fluid, captivating user journey.

Using data at every step helps web developers create sites that speak to their audience, increasing interaction and achieving business goals.

Creating User- Centric Designs

User-Centric Design is the hearbeat of creating web pages that not only look good but feel intuitive and effortless to navigate. It’s all about putting the user first, making every click, swipe, and scroll a seamless journey through your digital domain.

User Experience (UX) Design takes this concept to the next level by deeply understanding who the users are, what they need, and how they interact with your website. Solid data grounds this understanding, not guesswork—analytics show what works and what doesn’t, enabling the tailoring of your design to perfectly meet user expectations and needs.

In the realm of UX Design, “user friendly” isn’t just a buzzword; it’s the foundation of every decision made. A user-friendly website is easy to navigate, loads quickly, and presents information in a clear, digestible format. It means designing web pages that users find enjoyable and straightforward to use, which, in turn, enhances their overall experience. This approach not only increases user satisfaction but also fosters loyalty and encourages return visits, thereby driving up engagement and, ultimately, conversion rates.

But how do you achieve a user-centric design?

Creating a user experience (UX) that truly resonates with visitors starts with a deep dive into understanding what they need and how they feel. This journey begins with empathy: putting yourself in your users’ shoes. Designers undertake research to build personas, which are detailed profiles of ideal users. These personas help paint a vivid picture of who the website is for, guiding the design process with real-world considerations.

Next, designers chart out user journeys, which are maps of potential paths a user might take on the website. These maps actively spot where users might get frustrated or lost, clearly indicating where improvements are necessary.

At this point, the power of data becomes clear. By analyzing how real users interact with the site—what they click on, what they ignore, and how they navigate—designers can spot trends and preferences. This insight is gold, shaping decisions to align with user expectations. For instance, if most users visit your site on smartphones, focusing on responsive design becomes a no-brainer.

Responsive design ensures that no matter the device, the user experience is smooth and engaging.

Visual design then takes center stage, transforming abstract concepts into tangible elements that draw users in. It’s not just about what looks good—it’s about using visuals to guide users naturally to what they need. Strategic use of colors, contrasts, and spacing can direct attention and encourage actions, whether it’s clicking a button or reading a piece of content.

The choice of colors isn’t random; it’s a calculated decision to evoke specific emotions and build a connection with the brand. High-quality images and a sleek design aesthetic further enrich the experience, inviting users to not just visit, but to stay and explore.

Adding UX into web design is also about leveraging the best tools and technologies. Today’s design tools and coding languages offer unparalleled possibilities for creativity and functionality. This is where art meets science, blending visual appeal with technical performance to craft websites that are not only beautiful but also powerful engines for user engagement.

By weaving these elements together, designers and developers create more than websites—they create experiences. Experiences that users don’t just see or click through, but feel and remember. This approach ensures that every digital journey is not just a visit but an exploration, making each interaction not only user-friendly but truly memorable.

At its core, User-Centric and UX Design focus on grasping and predicting your users’ needs and behaviors. This approach is dynamic and continuous, adapting and improving through ongoing user feedback and data analysis.

By emphasizing user-friendly designs, ensuring your web pages respond effectively to different devices, and skillfully adding visual elements, you craft digital experiences that connect deeply with your audience, encourage engagement, and stimulate growth. In the digital world, your website frequently serves as the initial interaction between your brand and potential customers. Therefore, it’s crucial to make this first impression memorable by adopting a user-centric strategy that places user experience at the heart of all your efforts.

Adopting Responsive Design for Every Screen Size

Responsive Web Design is a critical component in crafting websites today. It ensures that your site offers a seamless and user-friendly experience across various devices, from the vast landscape of desktop monitors to the compact screens of smartphones. This flexibility is not just a luxury; it’s a necessity. With the diversity of devices in use today, your website must perform flawlessly on any screen size to meet the expectations of all users.

Web pages with responsive design automatically adjust their layout, images, and content to match the screen they appear on. This flexibility enhances user experience (UX) by ensuring that visitors always encounter a site that’s easy to navigate, read, and interact with, regardless of the device they’re using.

The benefits of responsive web design extend beyond user satisfaction. From an SEO perspective, search engines like Google prioritize mobile-friendly websites in their search results. This means that responsive web design is not just about providing a better user experience; it’s also a key factor in driving more traffic to your site.

By embracing responsive design principles, web developers and designers can create websites that not only look high quality and feature a visually appealing design but are also practical and accessible for the entire target audience. It requires a deep understanding of various design tools, coding languages, and the latest web development techniques to ensure that your website meets these standards.

Ultimately, responsive web design is about ensuring that your digital presence is versatile, accessible, and user-friendly. It’s a cornerstone of modern website design that directly contributes to the visual hierarchy, color scheme, and overall visual elements of your site, making sure that your first impression on any device is a positive one. In the digital age, where screen sizes are as diverse as the users behind them, adopting a responsive web design is essential in designing websites that stand out and truly connect with your audience.

Implementing Visual Hierarchy and Data Driven Web Design

Implementing visual hierarchy and design is crucial for creating an engaging user experience on any website. This involves carefully arranging visual elements so that they lead the viewer’s eye across the page in a way that aligns with the site’s objectives, boosting both user engagement and satisfaction. By making data-driven decisions, designers can effectively prioritize what content should stand out, thereby enhancing the site’s look and its usability.

To make informed layout choices, it’s essential to analyze how users interact with the site. Tools like heatmaps are invaluable here, as they can highlight which parts of the page attract the most attention through views or clicks. This insight directs designers on where to place critical information or compelling call-to-action buttons. The goal is to make the site not just visually pleasing, but also easy to navigate, ensuring that users can find what they’re looking for without hassle.

Creating a visual hierarchy in web design is about deciding where to put things, how big to make them, and what colors to use. Elements that are bigger and have bolder colors tend to grab our attention first. This makes them perfect for highlighting the most important messages or for placing calls to action that you don’t want users to miss.

By thoughtfully applying these visual signals, designers can lead visitors on a smooth journey through the website. This method ensures that navigating the site feels straightforward and intuitive for the user, enhancing their overall experience. Such careful planning not only makes the site more enjoyable to explore but also more effective in achieving its goals, whether that’s to inform, entertain, or prompt action.

Personalizing User Experiences (UX)

Color’s role in website design is undeniable—it powerfully creates emotional connections with the audience and improves availability. A well-chosen color scheme makes your website not just visually appealing but also intuitive and user-friendly. It contributes significantly to the site’s visual hierarchy, guiding users’ attention to key elements and actions seamlessly.

Color influences perception and behavior. For instance, blue evokes trust and security, making it a popular choice for tech and finance websites, and many choose green for eco-friendly and health-related sites to signify growth and vitality.

The choice of colors should align with the brand’s values and the emotional response it aims to evoke in its target audience. This alignment is crucial in creating a cohesive brand identity that resonates with visitors.

Data plays a crucial role in selecting the appropriate color scheme. Through A/B testing, analytics, and user feedback, web developers and designers can gather insights into how different colors impact user engagement and conversion rates. This data-driven approach allows for informed decisions that match the target audience’s preferences and expectations, ensuring the website’s design elements contribute to a positive user experience (UX).

Furthermore, accessibility is a key consideration in color selection. Designers must ensure that color contrasts are strong enough for users with visual impairments to navigate the site effectively. Tools and guidelines, such as the Web Content Accessibility Guidelines (WCAG), offer valuable resources to achieve an accessible design.

In essence, integrating the right color scheme into your website’s design elements is a strategic decision that enhances user experience, reflects your brand’s identity, and ensures accessibility. By leveraging data to inform these decisions, designers can create web pages that are not only high quality and visually striking but also deeply resonate with the target audience, making every visit a step closer to achieving the site’s goals.

Technology and Tools for Design

In today’s digital world, the tech and tools for web design are key to making websites that are dynamic, efficient, and easy to use. Data-driven decisions heavily influence the choice of design tools, coding languages, and technologies, helping web developers create websites that exceed user expectations.

An analytical approach, guided by data insights, determines the selection of tech. For example, knowing if a site should be mobile-first depends on understanding what users want, leading to the choice of specific design tools and frameworks. Data about browser use and device sizes also plays a big part in picking the right coding languages and tech, ensuring the website works well for everyone.

Tools like Adobe XD, Sketch, and Figma are now essential for UX and UI design, offering features shaped by user feedback. These tools’ collaborative and testing capabilities let designers and developers fine-tune their work to real user needs.

Web developers are crucial in this process. With solid coding skills and a knack for design, they make sure websites look great and function perfectly, providing smooth navigation, quick loading, and easy access on any device.

In short, blending data-driven methods, modern design tools, and the latest coding languages results in websites that are attractive and practical. By keeping users in mind at every design and development stage, developers can craft sites that truly connect with their audience, meet business objectives, and stay ahead in the digital space.

Conclusion

In the realm of web design, integrating data into every decision is not just beneficial; it’s essential. Data-driven insights provide a foundation for creating user-friendly, engaging, and effective web pages.

From start to finish, data shapes every decision in web design, from the visuals and user interface to the coding languages web developers choose. This method makes sure every part of the site matches what the audience wants and likes, improving the user experience (UX).

Data’s role is crucial in all parts of website design, like the layout’s visual order, color themes, and the tools designers use. By looking into how users act on the site, designers craft elements that grab attention and lead users smoothly, meeting the website’s goals. Picking colors based on what users prefer not only makes the site look good but also connects emotionally with viewers.

Understanding the devices your audience uses allows for optimized design across platforms, ensuring a seamless experience for every user.

As we advance in the digital age, we cannot overstate the importance of data-driven design. It’s a powerful method for enhancing user satisfaction, engagement, and ultimately, the success of the website. I encourage all web developers and designers to embrace data-driven insights. Let them inform your creative process and watch as they transform the user experiences on your websites into something truly remarkable and user-friendly.

By focusing on data throughout the design process, you make sure your website reaches top web design standards and raises the bar for user experience. This method results in not just visually appealing sites but also in platforms that truly connect with viewers, increasing engagement and growth.

The post 5 Amazing Elements of Data Driven Web Design appeared first on Fullfunnelservices.

Here are five reasons your business should partner with a Data Marketing Agency to elevate your marketing efforts.

1. Unlock the Power of Customer Data

Understanding your audience is the cornerstone of any successful marketing strategy. A Data Marketing Agency excels in extracting valuable insights from customer data, enabling businesses to tailor their offerings and communications precisely.

This deep dive into your audience’s preferences, behaviors, and feedback illuminates the path to a more personalized and effective marketing approach. By leveraging customer data, businesses can enhance the customer journey, making each interaction more relevant and impactful.

At fullfunnelservices, we ensure every marketing decision stands out by being informed and targeted, maximizing engagement and conversion rates.

2. Optimize Your Marketing Efforts with Data-Driven Insights

The marketing realm is vast and complex, with endless variables to consider. Data-driven marketing cuts through the guesswork, allowing businesses to allocate resources wisely.

A Data Marketing Agency brings sophisticated data analysis tools and expertise to evaluate the effectiveness of marketing efforts across channels. This approach ensures that we invest every dollar spent toward achieving specific, measurable goals. By understanding which strategies resonate with your audience, you can fine-tune your campaigns for better results.

Fullfunnelservices uses data to improve your marketing efforts, making them always changing and getting better with useful insights.

3. Upgrade Campaign Performance through Expert Analysis

The success of a marketing campaign lies in its ability to engage and convert. Analyzing campaign performance is a critical step in this process, offering a clear view of what works and what doesn’t.

A Data Marketing Agency uses advanced metrics and tools to dissect each campaign’s performance and identify opportunities for optimization. This process ensures that your marketing strategies are reactive and proactive, predicting market trends and customer needs.

Fullfunnelservices helps businesses reach revenue goals by always improving campaign strategies with strong analysis skills.

4. Achieve Growth and Efficiency with a Data Marketing Agency

Growth and efficiency are often the biggest challenges for SMBs. A Data Marketing Agency provides the expertise and tools to scale marketing strategies efficiently. Businesses can grow by focusing on effective strategies and cutting ineffective ones without spending more on marketing.

This lean marketing approach, grounded in data review, ensures businesses grow sustainably.

Fullfunnelservices uses data and experiments to help businesses grow without spending too much money or resources.

5. Forge a Path to Innovation and Continuous Improvement

In a rapidly changing market, staying ahead means innovating constantly. A Data Marketing Agency not only helps businesses keep up with trends but also anticipates future shifts.

By analyzing market data and consumer behavior, agencies like fullfunnelservices identify emerging opportunities and recommend innovative marketing strategies. This forward-thinking approach ensures businesses are always one step ahead, ready to adapt and evolve.

Moreover, emphasizing data-driven decision-making fosters a culture of continuous improvement, where we test, analyze, and refine every strategy for better results.

Conclusion

In today’s world, full of information everywhere, teaming up with a Data Marketing Agency isn’t just a fancy option but a smart move for small and medium-sized businesses (SMBs).

Data helps companies make better decisions, improve marketing, and increase customer satisfaction, leading to growth and success.

Fullfunnelservices demonstrates the power of partnership by effectively using data, improving ad campaigns, and creating innovative marketing strategies. Companies using data for decisions are successful now and preparing for future growth.

So, if you’re thinking about improving your marketing, growing smartly, or trying something new, a Data Marketing Agency is the secret door to making the most of your business in this digital playground.

Ready to unlock your business’s full potential and catapult into a future of growth? Contact fullfunnelservices today, and let’s turn data into your superpower!

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Here, we break down some of the most important similarities and differences between the two and help you decide which one is best for your needs.

What is Cloud Computing?

Before we discuss the three most popular cloud computing software options, we must first explain what cloud computing is.

According to Microsoft, “cloud computing is the delivery of computing services—including servers, storage, databases, networking, software, analytics, and intelligence—over the Internet (“the cloud”).” Cloud computing can help you store assets and data, back up your data, and create more streamlined business continuity. Gone are the days of huge server rooms, traditional IT departments, and needing to invest in more computing power or storage at your office. Now, you can use a cloud computing platform to do all of that for you.

There are many benefits of cloud computing, including:

• Global scale – grow your business quickly without having to invest in physical storage. Plus, you can invest in more or less computing power from the right geographic location (aka almost anywhere in the world!). This is a huge advantage in our remote working environment.
• Performance – The top names in computing services, like the ones we list here, run on the fastest, most secure datacenters and are always being upgraded as needed. This means that you are always running on the most efficient computing hardware without having to update your single corporate datacenter.
• Security – By using a reputable cloud computing service, you can strengthen your company’s cybersecurity because these providers utilize the leading security policies, technologies, and controls. Keep your data safe and secure.
• Speed – Because most cloud computing services are provided on-demand, you can have your data when you want it, without having to plan for extra capacity. It’s amazing how fast data can travel through the cloud!
• Productivity – Save time and resources with a cloud computing service versus an on-site datacenter. No more having to set up hardware, patch through software, and hire an IT team to management your data. With cloud software, you can spend more time focusing on your business goals instead of stressing about IT.

Now that we understand some of the basic functions and benefits of cloud computing software services, let’s look at three of the top names in the industry: Amazon Web Services, Microsoft Azure, and Google Cloud.

What is Amazon Web Services (AWS)?

Amazon Web Services (AWS) is Amazon’s cloud platform and it offers over 200 services, including data archiving, backups and restorations, blockchain, cloud migration, and much more. They also have solutions for a number of industries, including advertising and marketing, financial services, game tech, media and entertainment, and many more.

Huge names choose AWS for their cloud computing, including Coca-Cola, Expedia, Netflix, and Volkswagen, among others. It is one of the most widely-adapted cloud platforms worldwide.

Pros of AWS

AWS is a great option for cloud computing for a number of reasons. Here are a few of the platform’s most important benefits:

Breadth & Depth of Services

As we mentioned above, AWS has more than 200 services, meaning that there’s a solution for every business. AWS launched in 2006 and has continued to expand as the e-commerce giant grows.

Pay as You Go Model

With AWS, you only pay for the services you use, meaning that it can be more affordable (if you know what you need). This also means that it’s easy to scale your resources if and when it’s needed.

Trust

Many consumers trust AWS because they have compiled the most compliance certifications in the industry.

Cons of AWS

While there are a lot of benefits for AWS, there are also some considerations. Here are a few:

Less Transparent Pricing

While AWS’ pay-as-you-go payment structure can be beneficial for some businesses, this also means that you might not know exactly how much you are paying or you may pay for services you don’t actually need. You can enable monitoring on your account, meaning that you get alerts when your billing changes, but even with these notifications some billing items can easily be missed.

Country-Specific Services

Depending on your location, every AWS service might not be available where you or your employees are. Amazon is a US-based company and will advertise their services that are available in the US, but these top services might not be available in other countries.

Data and Intellectual Property Protection

With all cloud computing services, you need to be aware of your data protection, as well as any intellectual property protection. AWS could allow foreign companies to access your data. In addition, AWS removed a clause in their agreement protecting intellectual property and replaced it with the following:

“AWS will defend you and your employees, officers, and directors against any third-party claim alleging that the Services infringe or misappropriate that third party’s intellectual property rights, and will pay the amount of any adverse final judgment or settlement.”

In addition to these considerations, all of the cloud computing options on this list face a few of the same considerations, including potential data mining, cloud computing glitches, and insecure services.

What is Microsoft Azure?

According to Microsoft, the Azure cloud platform contains “more than 200 products and cloud services designed to help you bring new solutions to life—to solve today’s challenges and create the future.” According to the site, you can “build, run, and manage applications across multiple clouds [and] on-premises…with the tools and frameworks of your choice.

Pros of Azure

Azure is an extremely popular cloud computing service choice for many companies and executives. Here are some of the top benefits of the software:

Secure

Azure is generally a very secure cloud computing choice. They follow a security control system called DADSC that has five steps: detect, assess, diagnose, stabilize, and close. Azure was also recognized as an industry leader thanks to its cloud infrastructure.

Scalable

In times when you need more data packets, Azure makes it easy to purchase what you need, when you need it and scale back when needed. You aren’t stuck into a one-size-fits-all service and can scale up and down as your business changes.

Cons of Azure

Of course, there are considerations with Microsoft Azure as well. Here are some to keep in mind:

Difficulte to Migrate From Competitors

Microsoft makes it easy to move from a competitor to Azure through their Azure Migrate tools, but they don’t make it as easy for the other way around. If you are looking to migrate your cloud software services fairly often, this might be a consideration before working with Azure

Potential Data Loss

Azure is a single solution, meaning that all your data is stored in one place. This means that if you can’t access it, you’re out of luck. In addition, you run the risk of Azure losing your data, which has happened in the past, like during this 2018 outage. While Azure does have some options for data backup, they cost extra.

Location-Based Services

Just like with AWS, not all solutions will be available in all markets. If you have employees around the world, make sure the services that you need are available in their home countries.

What is Google Cloud?

Lastly, we have Google Cloud. According to their website, “Cloud Storage is a service for storing your objects in Google Cloud. An object is an immutable piece of data consisting of a file of any format. You store objects in containers called buckets. All buckets are associated with a project, and you can group your projects under an organization.” The structure of the cloud storage is pretty straightforward.

Pros of Google Cloud

Here are some of the main benefits of Google Cloud storage options:

Secure Storage

Security and access is super simple with Google Cloud Platform, especially if you integrate it with Google Drive.

Many Regions Available

As we talked about with AWS and Azure, some features might not be available in other areas. But Google Cloud offers data storage in a number of regions including North America, South America, Europe, Asia, and Australia.

Cons of Google Cloud

While Google Cloud is a great option for many businesses, there are a few things to consider. Here are some of the cons of the platform:

Expensive

Depending on your needs, Google Cloud may be a more expensive option than Azure or AWS. Both of those options have a pay-as-you-need model, but Google Cloud has a fixed pricing model and you may also have to pay extra for their support fee.

Outdated Services

When it comes to machine learning and SQL, Google Cloud may fall behind the other competitors and only offers limited or outdated versions of some managed services.

AWS vs Azure vs Google Cloud: Which Option is Right for You?

As you can tell, the cloud software that is right for your business will depend on your unique needs. You need to consider your priorities, including:

• Quanity and quality of data
• Security concerns
• Location
• Machine learning needs
• Cost

In addition, many businesses are choosing to implement a hybrid model and work with multiple cloud software options for their different needs. These platforms listed can be combined together, use individually, or used with other software options as well. As our virtual world continues to grow, so do our data solutions.

Do you have questions about your data storage or how to incorporate data into your marketing stack, we would love to chat! Contact us today to learn more.

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Let’s think about the evolution of data storage this way: Apple’s very first iPhone came with 4GB of storage capacity back in 2007.

Today, you can purchase an iPhone 12 with up to 512 GB2 of storage to handle thousands of photos, videos, texts, documents, and applications, with the option to add even more cloud storage through third-party apps and iCloud.

Storing data has become a basic need because it’s these data points that define who we are in the 21st century. 

It’s essential to focus on privacy, security, and accessibility when it comes to data, and the same goes for your business. As businesses move online and many shift to entirely remote operations, centralized data storage and sharing are what allows your business to run smoothly.

Keep reading to learn why you need all your data kept in one place as a business owner and an individual. 

What is Data Storage?

By storing your data, you’re making a digital copy of your photos, documents, and files and saving them for future use. It’s what you’re doing when backing up a smartphone or laptop to keep yourself safe from hacking or the event of lost or stolen property. Primary forms of data storage include hard drives, USBs, or the cloud. 

Types of Storage

Data storage can be divided into two distinct categories, and each option meets different storage needs and abilities:

1. Direct Attached Storage (DAS) defines a type of storage that’s physically connected to your computer but acts as an external storage device; this includes hard drives, CD drives, flash drives, and solid-state drives (SSD). Laptop computers typically come with direct-attached storage capabilities, and as you build up data on your device, it’s stored there. DAS is excellent for personal day-to-day storage, but sharing data between devices is difficult. 
2. Network Attached Storage (NAS) allows you to transfer data across multiple devices on the same network. Companies often implement NAS through a shared server or cloud services like Google Drive, Dropbox, or iCloud. An administrator can adjust access permissions from the centralized data hub. 

Storing Data for Your Business

Centralized data storage is a crucial factor in running any business. Whether you’re a sole-proprietor and run your operations or you’re part of a 200-person team, you should be storing your data in one location where everyone can easily access it. As of 2020, we found that cloud storage accounts for half of all corporate data due to its heightened security and accessibility. 

Here are a few types of cloud services that can improve your business operations:

Google Drive

Google allows for easy file sharing and real-time document and presentation editing on Google Docs and Google Slides. Companies using Google Workspace (formerly called GSuite) have a handful of document storage options, ranging from 30GB to 5TB. Like all other cloud-based Internet services, Google Drive allows you to access your documents anywhere you have wifi or cellular data. 

Amazon Web Services

In addition to its retail services, Amazon offers cloud-based storage to business account holders. Web Services is Amazon’s most profitable sector and quickly growing, with over 1 million accounts currently online. It has the most features of any cloud provider other than storage. 

Dropbox

Dropbox allows for easy document and file sharing between devices. Your stored data will be accessible anytime online, but it can also be easily integrated with iOS to appear in your desktop folders and Finder (if you own a Mac). Dropbox’s integration capabilities set it apart from other cloud providers. Businesses and individuals have a variety of plans to choose from when starting with Dropbox. 

iCloud

iCloud has excellent data storage capabilities for personal use but not for business. iCloud will automatically backup your devices on your Apple products, making all of your data accessible and centrally located in the cloud. Data sharing is mainly optimized for photos to be shared by devices using the same Apple ID.

CRM Software

Businesses can store some data through their CRM, making it easily accessible to an entire company in seconds. Large companies use CRMs as a hub for their employees to stay connected and up-to-date on the latest announcements and sales data updates. Read more about CRMs, specifically in the construction industry, by clicking here. 

The Benefits of Having all Your Data in One Place

Centralized and organized data storage is essential to your success. You want to ensure that your data is accessible and easy to find anytime you might need it. Otherwise, there’s a high chance of losing it. No longer are we in the days of filing physical paperwork. If you lose a digital file and it’s not in the cloud, it may be lost forever. That’s why accountability is so important. Misplacing even small pieces of data can lead to more significant problems for your business in the future, so your data storage protocol should be well-defined and straightforward. 

Convenient Access to Data

When all of your data is in one place, you’re able to interpret it much more efficiently. There is no question about where a file might be, so you’ll spend less time looking for the things you need. It makes your life easier for personal data when it comes to finding old photos and documents since you know they’ll be in one place.

Your Data Stays Current

You can easily remove outdated or duplicated data that’s being replaced. For businesses, this is a great way to ensure that your employees are consistently up-to-date and use the correct information when conducting data science research. 

Create Multiple Backups from One Place

Unless you’re storing through the cloud, you will want to backup your data pretty regularly. That’s because if your hard drive crashes or your USB breaks, there’s a good chance you won’t be getting your data back. When you store your data in one centralized location, it’s easy to backup – no need to pull from various sources and outdated CD drives. 

Your Data Stays Secure

Choose one place for your data, and make it secure. As a business owner, choosing a single centralized location allows you to monitor your data more closely; this gives you the chance to implement stringent security measures to access that data. Keeping your data spread out across various hard drives leaves you more susceptible to data loss or hacking. 

Effective and efficient data storage is critical to your success in business, as well as your success in life. The days of living “off the grid” are no more – by 2022, 70% of the globe’s GDP will have undergone digitization, and we must recognize data as the valuable asset that it is. If you’re looking for a way to streamline your data pipeline and assess your needs in business, fullfunnelservices can help. 

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The new pop-up intends to protect iPhone users from the unwanted, and perhaps covert, collection and sale of their data through apps like Facebook, Snapchat, and more. It does so by alerting users that certain apps may track user data on different apps. This feature is part of the iOS 14 update, which features a new look and layout for iPhone and iPad home screens and default app icons, better-optimized organization, compact calls, and more. 

To read about all of the newest features, take a look at Apple’s marketing graphic. 

A Brief History of Apple’s Data Collection

Apple has become somewhat of a crusader for data privacy advocates. In 2016, they famously denied the FBI access to a suspect’s iPhone, with Apple CEO Tim Cook referring to that decision as part of a “civil liberties issue.” A similar controversy arose after the launch of Apple’s Siri, leading users to wonder when their iPhones were actually listening. 

Over the years, Apple has shown continued commitment to ensuring the safety and security of user data, so the new transparency pop-up comes as no surprise to many. 

However, it has caused quite an uproar over at Facebook, a social media platform that has profited off of user data for years. In 2015, Facebook’s CEO and Founder Mark Zuckerberg even admitted to giving data away to dozens of companies. 

Facebook launched its Ad sector back in 2007, and one can only guess how long they’ve been involved in user data collection. It wasn’t until December 2015 – in another famous case involving Cambridge Analytica – that most people were even made aware of the accusations against Facebook regarding the sale of data. Before that, and even today, Facebook’s data collection activities have been carried out mostly behind the scenes. 

This blog will discuss what the newest Apple feature means for Facebook, and how the feud between two tech giants can, and likely will, affect you as a consumer or a business owner. 

Why Did Apple Do This?

As previously stated, the Apple brand has committed itself to the protection of user data. 

When you buy an iPhone, you’re also getting ultra-secure, total device encryption and data sharing settings, and Apple is constantly coming up with new ways to keep its user information safe. 

However, there are still roughly 1.96 million third-party applications in the App Store with data collection and privacy standards of their own. This means that users can download an app and unknowingly agree to have their activities tracked across different iPhone apps and sent back for use. 

The Pros of Data Collection

Of course, there are many benefits to data collection. First and foremost, many third-party apps collect data to improve their user experience. 

Advertising companies mine that data to create personalized ad experiences instead of generalized ads that seem totally irrelevant to certain individuals. Overall, data tracking does improve an individual’s experience on the World Wide Web by making it more personalized

The Cons of Data Collection

The biggest problem with data tracking? We, the users, don’t have much choice when it comes to sharing our data. A recent study revealed that 72% of surveyed consumers don’t trust other companies with their information, which is why Apple wanted to give its users a choice: to allow data tracking across apps or to refuse. 

What does the Update Look Like?

If you haven’t seen Apple’s recent ad about App Tracking Transparency, picture this: when you first open an app after updating to iOS 14, you’ll be prompted by a pop-up that reads, “Allow [App] to track your activity across other companies’ apps and websites? Your data will be used to deliver personalized ads to you.” 

You’ll then have the choice between “Ask App Not to Track” and “Allow”. Once you make a selection, Apple changes your data collection settings for that specific application. If you opt-out of tracking, the app only has permission to track activity that happens while on the app. If you allow for tracking, you’re permitting the app to track your activity while on other apps as well. 

Facebook’s Response

This year, Apple estimates there are over 1 billion active iPhones worldwide. If the new Tracking Transparency feature reduces the amount of app tracking, it could have a massive effect on Facebook’s profits. As a result, Facebook is urging its users to allow activity tracking, reminding us that it improves the personalized experience. 

Zuckerberg has also warned that the update will affect “millions of businesses around the world,” and responded with a matching feature in hopes of combatting the effects of Tracking Transparency. Now, when users open Facebook, they’ll see a pop-up screen containing information about tracking and why it’s important to tap the “Allow” option when prompted. 

How Does This Affect Me?

This iOS 14 update is a pretty big deal when it comes to data privacy in 2021. As a consumer, your privacy is now being highlighted anytime you download a new app – you no longer need to jump through hoops to change your data tracking settings. 

By opting out of app tracking, you’re agreeing to a less personalized ad experience (based on iPhone and iPad data), but for many, that’s a price worth paying to avoid non-stop targeted ads. 

If you’re a business owner who uses Facebook Ads, Tracking Transparency could have a much larger effect on you. Facebook relies on the data it collects from users to optimize ad services. Targeted ads are more effective than a standard approach, so the initial downfall for businesses could be huge. 

It may take time, but Facebook’s algorithm will need to readjust for the lost data. Lucky for you, Apple hasn’t implemented this update on laptops and computers and it’s unlikely they will. As long as a user’s cookies are turned on, you’ll still have access to some valuable user data that can be optimized for ads. 

What Can I Do?

First, assess your needs as a consumer. If you don’t care much about data tracking, allowing it can improve the user experience of various apps. Whenever possible, read through an app’s privacy settings before downloading. Decide what you’re comfortable with and make decisions there. If you’re running Facebook ads, optimize your data tracking to in-app activity and continue analyzing the iPhone and iPad data that comes in. Standardizing your ad strategy to some degree also can’t hurt! 

It’s no surprise that the largest operating system in the world and the largest social media platform in the world often butt heads. Apple and Facebook are at the mercy of one another when it comes to data privacy, and it’s impossible to say whether this feud will end soon. As the operating system, Apple holds a lot of power when it comes to decisions over data collection and consent from consumers. 

To learn more about Facebook Ads and how to adjust your marketing strategy in light of Apple’s recent update, send us a message. 

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Depending on your industry, product or service, goals, and budget, you can integrate data science marketing into your business strategy to keep up with your customers’ needs and wants. Here are a few reasons to get started: 

Why Data Science in Marketing is Important

The benefits of data science are somewhat clear-cut when it’s broken down. Unlike data analytics, which pulls various metrics from existing data to better understand consumers’ current base, data science is intended to predict future behaviors. Once you’ve adopted an effective data science strategy, it can help your business in the following ways:

Finding the Optimal Audience

You want to make sure you’re targeting the right audience so you can get the best value for your marketing dollars. Data science can help you determine who fits in that target market and whether or not that audience is expected to change. Data scientists often pull from cultural and trend analysis to determine how the market will change in the future. The same strategies can be used for lead generation for sales companies. 

Smart Insights

Data science can help businesses target which marketing endeavors are the most effective, and it’s the job of a data scientist to find causal relationships that an analyst might not otherwise have noticed. 

Data scientists use something called Smart Insights, a machine learning feature, to pick up on minute data that points to hidden patterns and complex relationships. Once they’ve identified such relationships, they can pass that information to growth marketers, who are equipped to reverse engineer it into an effective marketing campaign. 

The use of Smart Insights can help marketers be more unique and therefore more effective with their campaigns.

Increase Customer Experience and Customer Retention

Curating an excellent customer experience may directly impact your profits. Data scientists can identify the features of your business that are drawing in customers and those that might be pushing people away. 

Using that information, you can make improvements to your online store or business, knowing those changes are backed by data. Something as small as a 5% increase in customer retention correlates with a 25% increase in profits, so an investment into data science could be a game-changer for those struggling with one-time customers. 

High-Performance Omnichannel Campaigns

Omnichannel marketing is expected to become the new standard in digital marketing. Using user data profiles and pretty intuitive data science, omnichannel marketing takes a customer-centric approach to create a unified brand message across digital channels. 

For an online store, that could mean sending promotional emails or texts while a customer is looking at a product; it could also be an ad on an individual’s Facebook page for that same product. The idea is to keep that product on your customer’s mind until they finally press the “Complete Purchase” button, and it’s only possible through the use of data science. 

The list of benefits goes on, but we’ll move on to the “how-to’s.”

First, Track your Website and your Customers

Now that you understand some of the inherent benefits of data science marketing, let’s talk about how it’s done. Start by using a website analytics tool and a marketing analytics tool. The website analytics tool provides insight into your website’s performance and can tell you how customers engage with your website. 

A marketing analytics tool will provide a more holistic view of your customers and tell you what the customer journey looks like. For example, how much time is each customer spending on your site? What type of products are they looking at? Having the ability to track a potential customer’s use of your site can be a precious tool and tells you what is working and what isn’t. 

Google Analytics

Google Analytics is a popular website analytics tool that tracks your website’s performance. It pulls important metrics which can help you develop an informed marketing strategy. A few of these key metrics are website traffic, bounce rates, new visits, time spent on a site, and other user behavior variables. 

Google Search Console

Search console can help you monitor your site’s presence on Google Search results. If you’re experiencing issues with search rankings and spam, you can troubleshoot those problems and set up alerts to be notified when they happen. Google Search Console also shows you which sites are linking to yours. 

Bing Search Console

The Bing Search Console can keep you up-to-date with your rankings on Bing Search. Maintain your site’s presence on Bing Search results and troubleshoot issues with indexing or spam.

Audience Analytics

Social media advertising tools like Facebook Ads and LinkedIn Ads can bridge the gaps between all marketing channels. They collect people-centric and closed-loop data on the following metrics:

• Traffic-based metrics like CTR, Quality Score, Clicks, and more
• Conversion-based metrics like cost per action
• ROI-based metrics 

According to Facebook, an average user clicks on about 12 ads per month, giving it huge potential as an advertising platform. But maybe Facebook isn’t the place to reach your target audience and you’re better suited to Twitter or Instagram. 

If you decide to run an ad campaign on social media, be sure to utilize the insights available to business users. Facebook, Instagram, LinkedIn, and Twitter all provide useful audience analytics on their Insights pages. Learn about when your target audience is online, where they’re located, what their online activity looks like, and much more.

If you decide to use paid ads for your product or service, be sure that you’re using them effectively and avoiding mistakes. A successful ad is a smart ad. 

Web Analytics and Marketing Analytics Together

Used in unison, both analytics types provide a more thorough understanding of a customer and their journey on your site. Use this data to do more informed marketing and answer deeper questions. 

Transactional Data

Transactional data can tell you a lot about your business. Compared to user data, transactional data tracks purchases, returns, payments, signups and subscriptions. Internally, it includes other parts of your business like payrolls, invoices, contracts, and anything else having to do with an exchange or agreement. 

If you run an online store, transactional data should be at the forefront of your marketing analysis. You can dig into the who, what, when, where, and “how much” for each purchase to try and perfect your customer experience. Are customers buying more when you offer bulk discounts? Are you losing money on returns? Ask yourself these questions and develop marketing campaigns around customer purchase patterns.

Utilize Predictive Analytics

Now that you have a better understanding of your site and customers, you can use that data to create a model to predict future outcomes by utilizing predictive analytics. 

What is Predictive Analytics?

Predictive analytics use data to make predictions around unknown future events. Doing effective predictive analysis involves data mining, statistical analysis, and machine learning methods to find and analyze patterns in data to identify the likelihood of future outcomes. 

After collecting data, consolidating it, cleaning it, and combining it with other marketing data, it’s used to build a predictive model. Here are the 5 most common predictive analytics models:

1. Classification answers yes or no questions based on historical data. 
   1. “Is this customer going to cancel their subscription?”
2. Clustering groups your target market into clusters based on shared attributes, then develops subtypes of marketing strategies for each.
3. Forecast uses historical data to make numeric value predictions on future data.
4. Outliers model identifies data anomalies that may be relevant. 
5. The Time Series model looks at historical data trends over time and tries to predict future iterations of that data.

Now imagine how beneficial the use of one of these predictive models can be to your marketing strategy. Predictive marketing can help inform future marketing decisions by determining the likelihood of certain events in your business. 

You can use a predictive analytics tool online, such as ones available from IBM, SAP, and Oracle. These are relatively complex and expensive, and there are quality alternatives such as Tableau, Domo, and Marketo. 

Data Science and Marketing

Data science marketing is more popular than ever before because it offers a much higher ROI than other marketing analysis forms. It is a valuable tool that should be utilized in your marketing strategy because it can help you understand your customers more. The more information you have on them, the more informed your marketing decisions will be, and the more successful you can be in business. 

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Data Mining: Mining in the 21st Century

Data mining is the mining technique of the 21st century. But what is it exactly? We’ll define the meaning of ‘mining’ first. 

Mining is extracting valuable minerals or other materials like coal from a mine or placer deposit. Similarly, data mining is extracting potential information and insights from big data. 

Data mining uses statistics, mathematics, machine learning algorithms, database knowledge, and AI to analyze and extract insights and predict future events. The insights derived from data mining apply to various fields like marketing, banking, credit fraud detection, and health care services.

Data mining plays a critical role in the day-to-day activities of a data scientist or machine learning engineer. It is a step by step procedure to be implemented. Miners can use various techniques like regression, classification, clustering. Next, we will discuss the implementation process and mining techniques.

Data Mining Implementation Process

The data mining implementation process consists of 6 steps. Let’s discuss the whole process in detail.

Business Understanding

Before getting your hands dirty with data, the main job is to understand business objectives and the client’s perspective. Ask the questions below first:

• What are the client’s objectives and expectations?
• What are the business requirements that need to be met?
• What are the various resources available, different assumptions, and constraints?
• Have you prepared a good data mining goal and approach plan in detail?

Data Understanding and Preparation

After defining data mining goals, you’ll need to understand the data and prepare to mine. This activity consumes 90% of your time. The following steps are:

• Data collection from various sources.
• Merging of all data in a single format.
• Data needs to be cleaned and formatted.
• Removal of noisy data.
• Handling of missing data.
• Data outliers need to be taken care of at this step.

Data Transformation

Data transformation is crucial after this process. Miners use transformed data for modeling. Transformation techniques include:

Normalization

Data miners will use normalization to bring all the features on the same scale. This scaling technique will shift and rescale all features and get their values between 0 and 1.

Standardization

Standardization puts different variables on the same scale. To standardize a variable, you will need to calculate the mean and the standard deviation of said variable. Next, subtract the mean and divide by the standard deviation.

Feature Engineering

Feature engineering is crucial for accurate results of ML modeling. New features are created to ensure the algorithm works for the best based on domain knowledge and experience.

One-Hot Encoding

Many algorithms don’t work on categorical data or textual data. So, to convert categorical data to numerical data, use one-hot encoding.

Modeling

Complete modeling using a prepared dataset and various data mining techniques. Data modeling is a process to find a relationship between the input features and their relation to the output feature. The method of modeling includes:

• Selecting the appropriate ML algorithm according to the data-set and expected output.
• Training an ML model on training data using the ML algorithm.
• Meeting the data mining objectives as per the business.

Evaluation

At this stage, appropriate parties will evaluate the model. Complete the evaluation as follows:

• Evaluate the model according to the business objectives and requirements.
• Verify results against test data.
• Decide whether the model will go to the deployment stage or not.

Deployment

In this phase, deploy the data mining model to business operations. Keep the following in mind:

• We create data mining models to add value to the business only when deployed to its environment.
• Even non-technical people can easily use deployed models for the predictions of future events.

Data Mining Tools

Two tools for data mining are Python and R.

Python is a widely-used programming language that helps a programmer to work quickly and effectively. Python is in great demand for Machine Learning Algorithms. Many feature libraries like NumPy, pandas, SciPy, Scikit-Learn, Matplotlib, Tensorflow, Keras makes machine learning task very easy to implement with Python.

R is another tool that is popular for data mining. R is an open-source programming tool developed by Bell Laboratories (formerly AT&T, now Lucent Technologies). Data scientists, machine learning engineers, and statisticians for statistical computing, analytics, and machine learning tasks prefer R. R libraries like Dplyr, Caret, Ggplot2, Shiny, and data.table is used in machine learning and data mining.

Data Mining Techniques

Eight data mining techniques are used extensively. We will discuss these techniques in detail with Python and R code snippets.

Regression

Regression is a data mining technique used to find the relationship between two or more variables. All the variables need to be continuous. Regression is a supervised learning technique of data mining used to predict the value of a dependent variable(Y) based on other independent variables(X’s). 

When a single independent variable is used to predict a dependent variable’s value, it’s called simple linear regression. In cases where two or more independent variables indicate a dependent variable’s value, it’s called multiple linear regression.

The best fit straight line is called a regression line. Linear regression is represented by the equation Y=b*X + a + e, where a is the intercept, b is the slope of the line, and e is the error term.

Python Code:

Let’s implement linear regression in Python on a Boston house-prices dataset to determine housing prices at various Boston locations for 13 features of the dataset.  We will create a linear regression model on train data first and then complete the test data prediction.

The graph to the right shows the relationship between actual target values and predicted target values. The best-fit regression line is drawn to show the difference between the actual and predicted value and is used to find regression loss.

R Code

Let’s implement linear regression in R on a Boston housing-price dataset to determine housing prices at various Boston locations for 13 features of the dataset. Create the linear regression model on the train data using R, and then predict the test data.

The graph to the left shows the relationship between actual target values and predicted target values. The best-fit regression line is drawn to show the difference between the two values and help find regression loss.

Classification

Classification is also a supervised learning technique used to classify input items in different classes. In the classification technique, the output variable (Y) is categorical.

Classification is like classifying dogs and cats’ images. It predicts which picture belongs to class ‘cat’ and which photo belongs to class ‘dog.’

Classification can be of two types: Binary classification has two output classes, and multi-class classification has multiple output classes.

Python Code:

Let’s implement logistic regression (a machine learning algorithm for classification) on Iris Dataset in Python. It is a flower dataset that has three output classes: setosa, versicolour, and virginica. 

Implement the logistic regression model on train data. Then, prediction will be done on test data. We will also draw a Confusion Matrix for performance analyses between actual and predicted values.

The image to the right shows a confusion matrix drawn for logistic regression. The confusion matrix is a performance analysis metric that defines the algorithm’s ability to predict the output classes accurately. 

We can see from the above Confusion matrix, out of 50 (= 16+14+3+17) test values, three are not predicted correctly, and 47 are predicted correctly. We can get the model accuracy = 47/50  = 0.94

R Code:

Let’s implement a decision tree classifier (a machine learning algorithm for classification) on Iris Dataset in R. It is a flower dataset with three output classes: setosa, versicolour, virginica. We will do modeling, prediction, and performance analysis using the confusion matrix for the dataset.

The above image shows a confusion matrix drawn for the decision tree classifier. 

We can see from the above Confusion matrix, out of 50 (= 14+19+7+10) test values, 7 are not predicted correctly, and 43 are predicted correctly. Model accuracy = 43/50  = 0.86. 

Clustering

Clustering is an unsupervised learning technique. The technique’s use is to classify or cluster similar kinds of data from the dataset. In clustering, output class or value is not predicted.

Use clustering to group similar products, customers, and businesses.

Python Code:

Let’s implement k-means++ clustering (a machine learning algorithm for clustering) on Iris Dataset in Python. We will also draw a clustering graph of different clusters.

The graph to the left shows the dataset divided into 3 clusters; setosa, versicolour, and virginica, with their centroids. Three groups are easily distinguished using the clustering technique. 

R Code:

Let’s implement k-means clustering on the Iris dataset in R. We will also draw a clustering graph of different clusters.

In the graph to the right, the dataset is divided into three clusters setosa, versicolour, virginica with their centroids. Three clusters are easily distinguished using the clustering technique. 

Prediction

We have learned different techniques of data mining like regression and classification. When a machine learning model is created, values are predicted on test data. 

Prediction is a fantastic data mining technique used to predict trends, patterns, forecasting, values, and classes. Past data or train data is analyzed and modeled correctly to predict future events.

Let’s implement Python code for logistic regression. In Python, predict values for test_x, and compare actual and predicted values using a scatter plot.

We can see actual values as a blue dot and predicted values as a red dot in the above scatter plot. It can be easily observed, except from 3 values, that the miner predicted all other data points correctly.

Association Rules

Association rule mining is a classical data mining technique used to identify interesting patterns in data. Association rule mining is used only for categorical data and is not suitable for numeric data. 

‘An association rule is a pattern that states that when X occurs, Y occurs with a certain probability.’

Association rules determined the relationship or association between two items. E.g., how frequently we brought two things like bread and milk together or milk and chocolates together. A man who went to bought ‘milk’ is likely to buy ‘chocolates,’ so stores placed them close to each other to increase sales.

Outer

Data mining’s outer technique is related to identifying data-set data, which behaves not as expected. Some data points might be noise points or some fraud data points. Detection of such data is known as outlier analysis or outlier mining. 

The outlier is a data point that lies outside or diverges too much from the rest of the dataset. In other words, we can say an outlier is a data whose value is much smaller or much more extensive than different values in the dataset.

The real-world datasets are not noise-free. They consist of outliers. Outlier detection is valuable in numerous fields like credit or debit card fraud detection, detecting network interruption identification, etc.

Let’s try to find some outliers in the Iris dataset using Python code.

In the boxplot on the right, we can observe that in the Iris dataset, Virginica data-point have outlier(Point market in red). Outliers are points that fall outside the box-plot. Finding outliers from the dataset and pre-processing them is a fundamental data mining technique.

Sequential Patterns

The sequential patterns for data mining are specialized for periodicity analysis for sequence data. In real-world data, there are a considerable number of sequential patterns hidden. Mining techniques help to find them and analyze them.

There are many sequential pattern mining applications like music notes sequence, DNA sequence and gene structure, telephone calling patterns, or shopping experience.

Let’s understand with an example: A lady buys an expensive red color dress some days before Christmas. Then most likely, she will purchase matching footwear, purse, accessories, etc., in sequence. Predicting this shopping sequence is sequential pattern data mining.

Correlation

Correlation is a data mining technique used to define the relationship between two variables. Commonly used measurements are Pearson Correlation, Jaccob Similarity, Cosine Similarity, and Distance Correlation. We can determine the correlation between variables using a correlation coefficient. 

Statisticians typically write the correlation coefficient with R. The correlation coefficient value lies between 1 and -1. However, the closer R is to +1 or -1; the more closely the two variables are related. 

The closer R is to 0, the weaker the linear relationship. Positive R values indicate a positive correlation, where the values of both variables tend to increase together. Negative R values indicate a negative correlation, where one variable’s values tend to increase when the values of the other variable decrease. 

Let’s see the correlation between 4 features of the Iris dataset using Python code.

In the heatmap on the left, we can observe that petal length and petal width are highly positively correlated with a coefficient value of 0.96. petal length and sepal width are negatively correlated with the coefficient value of -0.43. Similarly, we can extract the correlation between other features too.

Data Mining Applications

Data mining is beneficial in various domains. Let’s review some applications below:

• Banking: Data mining helps detect fraud cases in the credit card industry, fraud profiling, and fraud investment cases in banks.
• Manufacturing: With mining, it is easy to predict products that should increase or reduce their manufacturing based on market data.
• Insurance: Data mining helps insurance companies optimize the price of their most profitable products and predict which offers to promote to different types of customers.
• E-commerce: E-commerce sites, products price, product recommendations, and offers are all possible because of data mining.
• Education: Data mining can detect learning patterns in students using their background and achievement records to indicate who needs more attention and who should focus on a particular field.
• Service Providers: Data mining can be used in areas of the service industry like billing analyses, customer interaction, handling complaints, and detecting fraud in businesses.

Takeaway

Data mining is all about learning past data and predict the future.  Following the data mining implementation process with the various mining techniques, you can quickly discover, analyze, and forecast data. 

This article is not just for reading but also for implementing and practicing. Get your hands dirty and try data mining with Python or R, any language of your choice. Have fun with your data and its mining.

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If anyone wants to pursue a career in one of these areas, elaborating on the difference helps push them to the right decision. While there are some similarities between data analysts and data scientists, there are many ways in which they differ.

As the name suggests, a data analyst analyzes and reports on datasets. A data scientist conducts research and examines every aspect of data to understand it to its core.

Let’s examine the skill sets, roles, and responsibilities of data scientists and data analysts to understand their similarities and differences.

What is a Data Scientist?

Data Science is the hottest job of the decade and is the most widely used technique among machine learning and artificial intelligence. Data science is a combination of machine learning, artificial intelligence, mathematics, and statistics. The job consists of mining a large amount of structured and unstructured data to solve complex problems and identify patterns, insights, and correlations within the datasets.

Roles and Responsibilities

The central role of a data scientist starts where the responsibility of a data analyst ends. Day to day roles and key responsibilities of a data scientist are:

• Cleansing and pre-processing the data.
• Identifying new features that can add value to data.
• Analyzing the requirements of the business and identifying ways to reach their goals.
• Utilizing machine learning algorithms to identify patterns or trends while predicting future events.
• Optimization of predictive models and hyper-tuning them appropriately.
• Creating stories out of data and producing interactive visualizations to translate findings with your team and stakeholders.

Skillset and Tools

There are many skills required to become a data scientist. From programming to linear algebra, statistics to ML algorithms, deep and vast knowledge are necessary to fulfill the role of a data scientist.

Let’s explore the skill set and tools required to become a data scientist.

Programming skills

Programming skills are a must to become a data scientist. Python and R are two languages that are in demand for this role. But experienced data scientists often say that businesses and experts favor Python. There are many free sources like Kaggle and DataCamp, where you can quickly learn Python and R.

SQL

SQL, or structured query language, is an interface to access databases. Software stores data in databases, so accessing these interfaces is a necessary skill to learn. Refer to practical and straightforward platforms like w3schools or Kaggle to learn SQL skills.

Mathematics and statistics

There are areas of mathematics that data scientists need to be proficient. Hands-on experience in linear algebra, matrices, probability, and statistics is a must. Look to your 11th, 12th, graduate, and post-graduate mathematics books to practice and understand these concepts.

Machine learning algorithms

Many machine learning algorithms like random forest, logistic regression, support vector machine, linear regression, and more are used by data scientists daily. They also use ML algorithms for modeling and predictions of unseen data. Machine learning algorithms can be self-taught using platforms like Coursera, Kaggle, and DataCamp.

Deep learning

Neural networks, NLP (natural language processing), and computer vision are advanced methods of deep learning implemented by data scientists. Advanced libraries of neural networks make modeling and predictions easy and fast.

For example, BERT is a pre-trained open-source machine launched by Google AI in 2018. It is widely used for advanced NLP problems. More recently, Google unveiled a 2020 machine learning model for Indian languages named MuRIL.

What is a Data Analyst?

A data analyst’s job is to analyze and visualize data. Data analysts create data reports that are to be understood by non-technical workers in layman language. Companies of various backgrounds, from healthcare providers to retail brands, can benefit from the work of data analysts.

Data analysts spend their time gathering data, preprocessing data, and converting their findings into beautiful insights that help businesses improve.

Roles and Responsibilities

A data analyst plays a vital role in a company. They sometimes wear the hat of business analysts as well. Core responsibilities of a data analyst are:

• Querying data from databases using SQL.
• Data cleansing, formatting, and processing.
• Exploratory data analysis and interpretation of data using statistical techniques.
• Developing actionable insights in the form of visualizations and reports.
• Working with teams and stakeholders to define business goals and requirements.

Skillset and Tools

Hands-on reporting and visualization tools are the trademarks of data analysts. Basic knowledge of programming languages like R and statistics knowledge are valuable additions. Let’s examine the skill set and tools required to work as a data analyst.

SQL

Data analysts’ first job is to gather data from multiple databases and translate it into a structured format. Data collection is achievable using SQL. However, data analysts have more advanced knowledge of SQL than the intermediate-level knowledge possessed by data scientists.

Statistics

Knowledge of statistics is a must if you are working as a data analyst. Statistical analysis is critical in understanding data, finding relationships within data, and developing hypotheses and theories. When forecasting sales and segmenting a potential customer base, statistics skills come in handy.

Excel

Microsoft Excel is a potent tool used by data and business analysts. It might feel old school, but its capabilities are vast. Users need to invest some time to master the software. Excel is also an excellent tool for creating data visuals.

Visualization tools

Experience with tools like Tableau, Google Data Studio, or any other visualization tool is a pre-requisite in the data analyst profile. They are platforms that can paint pictures of the insights developed from data analysis.

There are a variety of visualization formats like bar plots, pie-charts, and scatterplots that are used by analysts to showcase data in a meaningful manner. To learn more about data visualization and different visualization tools, click here.

Similarities Between Data Analysts and Data Scientists

We discussed the skills and responsibilities of data analysts and data scientists in the last section. Now let’s examine the similarities between the two.

Skills like statistics, SQL, data visualizations, analyzing data meanings, and deriving data insights are common among data scientists and data science analysts. Both of these roles aim to find insights from data that are beneficial to business goals.

Key Differences Between Data Analysts and Data Scientists

Predictive modeling, advanced tools, and programming are the difference between data scientists and data analysts. Data analysts work on past and current data, while data scientists work on predicting scenarios for the future. Here’s a summary of each role:

Salary Difference

The salary range of a data scientist vs. a data analyst is a significant point to consider when choosing your career path. For a future aspirant, skillset and salary go hand in hand.

Glassdoor reports that the average salary for a data scientist is $122,875 per year in the United States, while the average salary for a data analyst is $75,549 per year.

Let’s compare the average salary positions with the help of a bar plot:

Understanding Data Scientists and Data Analysts

By now, you should be able to distinguish between these two roles that work with data. Although data scientists and data analysts share some commonalities, their functions, skill sets, methodologies, and business insights make them different.

Data analytics and data science are growing, interrelated fields. Develop and assess your skillset to end up in the role that’s right for you.

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Data is the foundation for data science and machine learning, and machine learning builds its algorithms and models from data. And the use of data is only increasing!

This year, due to the COVID-19 pandemic, we saw an increase in digital data generation. According to Domo’s infographic, Data Never Sleeps 8.0, the internet reaches 59% of the world’s population and now represents 4.57 billion people in April 2020. As per EMC, the data produced by humans in 2020 reached 44 zettabytes. 

So what does all this data mean, and how can you get access to it? Let’s look at the different types of data, the current state of data collection, and how you can collect data for your business.

The Four Types of Consumer Data

In today’s modern digital era, gathering large amounts of data about existing and potential consumers is a common goal for many businesses. Consumer data is essential for predictive analysis, sentiment analysis, or targeting potential customers.

For example, Facebook acquired WhatsApp for a whopping $19 billion, mainly for its data. By purchasing the messaging app, Facebook received access to user information that they didn’t have before. WhatsApp’s data now belongs to Facebook and is used to improve its services. 

We can divide consumer data into four main categories: personal data, engagement data, behavioral data, and attitudinal data. 

1. Personal Data

As its name suggests, personal data includes personal information such as names, genders, birthdays, social security numbers, and some online information like IP addresses, devices, web browser cookies, and more. 

2. Engagement Data

Engagement data refers to the way consumers interact with emails, websites, mobile apps, social media pages, paid ads, and customer service. 

3. Behavioral Data

Behavioral data shows the behavior and buying patterns of consumers. It includes transactional details such as purchase histories, browsing patterns, product usage, brand affinity, or repeated purchases.

4. Attitudinal Data

Attitudinal data is collected based on customers’ attitudes toward products, such as purchase criteria, product desirability, customer satisfaction, and more. 

The State of Data Collection

There are many ways to collect data, and the two primary sources of data are current customers and potential customers.

Here are a few of the most common ways companies collect data:

Asking consumers for their data

The simplest way to collect data is to ask consumers through forms, filling out details on websites, providing credential details, collecting subscription data, sending out surveys, and more. All of this data is derived directly from the consumer.

Indirectly tracking customers

Many indirect tracking methods assist in gathering information about customers. These methods include tracking their social accounts, tracking purchase history, transactional data tracking, and online tracking. You can track online activities like sites they visit, products they are looking for, or the average amount of money the customer spends. These activities are all relatively easy to monitor.

Other sources of customer data

Other customer data sources include location-based advertising, which utilizes tracking technologies such as an internet-connected device’s IP address. Their IP address interacts with other devices like mobile phones, laptops, etc., to build a personalized data profile. Marketers and brands may use this information for personalized advertisements or landing pages.

Types of Data Sources

Now that you know how to collect data let’s discuss the different types of data sources. Traditional data sources include:

Websites

Customers visit various websites, and many ask for a subscription or form fill. These data submissions allow companies to, directly and indirectly, fetch data. The types of sites a user regularly visits are critical data points for any business. Optimize your website to collect accurate user data.

Social Media Profiles

Consumers use various social media platforms. These platforms gather personal information from users and collect data based on their posts, likes, follows, etc. Marketers can use data collected from social media to build buyer personas and predict customer journeys. 

Location-Based Advertising (IP addresses)

Many apps and other advertisers are utilizing tracking based technology. Location-based advertising can be helpful to determine where your customers live and travel.

Customer Service Records

When users subscribe or register on a site, they provide a lot of personal information, such as their email, birthday, and full name. This information becomes consumer data for companies.

Transaction Data

E-commerce sites are gaining popularity every day. With every online shopping purchase, transaction data is automatically collected by companies.

Third-Party Data Collection 

Companies that sell personal information and other consumer data to third-party sources are regularities in today’s business world. Once information is collected, it gets passed around in a data marketplace of its own.

Here are some examples: 

1. You are looking for a property to buy or rent and contact a broker. Suddenly, you start getting many leads from different real estate companies. It may seem like a coincidence, but it is third-party data sharing.
2.  You are browsing an online store for a particular product and start to see ads related to the product pop-up. Nothing remains under the curtain nowadays. Most of your online data can be easily tracked and shared by companies.

How Companies Use Consumer Data

Companies collect so much data from their current and potential customers that it must provide a high return-on-investment (ROI). Otherwise, why would anyone invest in the price data collection? Companies use their consumer data to:

1. To improve user experience (UX)

Companies analyze customer data by exploring consumer feedback and their experiences with their product. Based on the data, companies can resolve any glitches and undesirable outcomes for their customer experience.

Here’s an example:

A user is exploring an e-commerce site but abandoned their cart when a discount coupon did not apply to their order. Tracking the buyer’s journey can help brands resolve issues like this by monitoring where they exited their site. 

2. To refine marketing strategy

Based on consumer data, likes, and interests, companies can refine their marketing strategy. Marketing nowadays is more personalized based on consumer data analysis.

Social media plays a critical role in personalized marketing. A user’s journey is mapped through various social platforms like LinkedIn, Facebook, Instagram, Snapchat, Twitter, YouTube, and other websites. Mapping this journey is essential, as it is imperative to engage the user—personalized marketing channels the user’s sentiments for their marketing strategy.

3. To sell products

Every business is hoping to sell its product or service to the consumer. It’s how a business grows and generates revenue. Selling the right product to the right customer is simple with analysis of consumer data and a user experience that attracts returning customers.

4. To predict future trends

Future trends and patterns continue to change and adapt to the customer’s wants and needs. Companies use consumer data to predict what consumers will like or dislike. This data points to the product they are most likely to buy in the future and what they are likely to spend money on.

Data Security

Data security is fundamental to the user. Generally, users won’t submit their information to a site that does not seem trustworthy. Once trust is lost, it is hard to earn it back. So, data security is a significant concern. Only collect as much data as you can keep secure, or you may end up losing more customers than you gain.

Now it’s time to determine if you want to collect data or buy a large database. Let’s see the pros and cons of both.

Collecting Data 

Data collection is the process of gathering and collecting information from countless sources. It is a major bottleneck in machine learning and deep learning activities. Sometimes, there is a lack of cleaned and properly labeled data. When a large amount of data is required for deep learning systems, collecting your information is the only solution. 

But, data collection itself is a tedious task. Data collection mainly consists of data cleaning, data acquisition, and the data labeling process. Only an expert should attempt these processes. However, data collection and data management combined with machine learning and artificial intelligence can provide information that makes the hard work pay off.

Pros

• Collecting data can be more accurate as you know the data source.
• Collecting data ensures that you are the first party to use the set of data.
• Collecting data guarantees the availability of adequately cleaned and labeled data. 

Cons

• Data collection can be time-consuming.
• Data formatting like data cleaning, data labeling, and data acquisition adds to the data collection process’s length.
• Companies will need to hire workers who can use machine learning to analyze it correctly.

Purchasing a Large Database

Purchasing a large dataset is also an option. Collecting data requires expertise and analytical insights. Companies often make common mistakes in data analysis without an extensive background in data science. Also, the internal data collected by companies is often not enough when a large dataset is required. 

Aggregating data is a time taking process and needs resources as well. For companies who want access to information quickly without hiring or training employees, purchasing data is an easy way to generate leads.

Clean and labeled datasets may be necessary for specific applications. There are many quality sources for large datasets, training data science models, and machine learning models like DataStock, Kaggle, and KDnuggests.

Pros

• Purchasing data provides massive amounts of data points.
• Purchased data can provide new insights that you might not be able to get otherwise.
• Purchasing data saves time and resources.

Cons

• Many data privacy regulations may interfere with the data you can purchase.
• The quality of purchased data varies based on the source and price of the data.
• Finding a quality database provider can be difficult.
• Purchasing data can be expensive.

The Final Verdict

If you have time and expertise, collecting data on your own will ensure it is unique and valuable. If you have less time and are not an expert in data collection, buying a dataset from trusted sources may be a better solution. 

Remember, the dataset you’re purchasing must suit your requirements and be compatible with your organization’s technology. Both methods are practical and equally utilized, and it’s up to you to determine the best solution for your company. Let fullfunnelservices help; contact us today!

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With a median salary of $107,801 and a flourishing future, a lot of job-seekers are looking into the field of data science. 

With the increasing demand for data scientists, there is a shortage of quality data science skill sets because becoming a data scientist is no easy task. The role is like a detective job as you try to detect meaningful information from raw data and come out with the best predictive models.

Data scientists need to be skilled in mathematics, statistics, machine learning, and data visualizations with Python or R programming. Various courses and online videos don’t teach all of the industry requirements. So, there are some common mistakes new data scientists can make. 

10 of the Most Common Data Science Mistakes

Let’s look at some of the most common data science mistakes to learn from them and help people interested in the field grow in their career.

1. Analysis Without a Question/Plan

Analysis needs a direction and plan to proceed. Data science problems begin with a well-defined objective. Sometimes, data scientists jump directly into analysis and modeling without thinking about the question they’re trying to answer first.

For data scientists, the question they are trying to answer isn’t “what” but “why.” To answer “why” questions, data scientists need to be clear on what they want to achieve with their analysis.

Here’s an example:

As a data scientist, you need to know if the given problem is a supervised or unsupervised ML problem because further analysis and planning are done accordingly. Analyzing without knowing the type of problem first will lead nowhere.

Data scientists who don’t know what they actually want end up with analysis results that they don’t want. Always be ready with well-defined questions to hit your data science goal.

2. Lack of Data Annotations and Continuing to Use Corrupted Data

60% of a data scientist’s time is spent preparing and cleaning data. Although this task is the least enjoyable, it’s an important step. All future operations need to be performed on clean data, which is the base of a machine learning problem.

Data annotation is labeling data correctly and happens in the pre-processing step for supervised machine learning. Data scientists need a large amount of correctly-annotated data to train machine learning models, especially in image and video data.

Working on corrupted data without data annotations is like baking a cake without the correct ingredients. Will your cake come out fluffy, soft, and tasty? No!

Below is an example of corrupted data:

Corrupted data leads to incorrect model building as we can see in the above image. Data needs to be cleaned without errors and outliers for correct model building.

3. Not Focusing on Analysis

Data visualization and analyses are the most interesting parts of being a data scientist. In competitions, some data scientists will jump directly to predictive modeling, but this approach won’t solve any machine learning problems accurately in real world scenarios. Data scientists need to dig deeper into the data insights. 

By spending more time with the data analysis, studying trends and patterns, and asking questions, we can create beautiful stories out of the data.

Here’s an example:

Let’s say we have a dataset with many features, but all the features are not always important for modeling. The analysis helps in selecting important features for modeling and dropping features that don’t impact the output predictions. With this information, it will be easy to implement machine learning models on the important features and the predictions will be more accurate.

4. Assuming Correlation Implies Causation

Correlation does not imply causation. Correlation is a statistical technique that refers to how two variables change together (ex: if there is a change in variable x then there will be the change in y). When x increases, y increases, which means that x and y are correlated.

But, it doesn’t always mean that x causes y or y causes x. Sometimes, illogical analysis says x causes y because x and y are correlated, but this isn’t always true.

Let’s look at an example:

After launching a new feature in an app, the customer retention of the app increases. New features and customer retention might be correlated, but that doesn’t mean that the new feature caused customer retention. There might be many other factors for customer retention but both results are related.

Don’t jump to the conclusion of causation too quickly after finding correlation. 

5. Not Considering all the Useful Datasets While Building the Model

There might be multiple datasets to a problem and a good data scientist needs to consider all different datasets and try to link the information between them. Sometimes information is divided among various datasets to make it more readable. It’s a data scientist’s job to create a link, understand, and draw a correct picture of the data set while building the model.

Let’s look at an example:

Generally, in time series analysis, we have yearly or monthly datasets. Machine learning models need to learn from all datasets for accurate predictive modeling. Similarly, there are datasets with lots of text data in one dataset and other features related to the text in other datasets. Consolidating these datasets to gain a correct understanding of the data is important.

Never neglect or ignore any information. In the case of the small dataset, upsampling and augmentation work well to increase the size of the dataset. On bigger datasets, machine learning models and deep learning models work very well.

6. Using the Same Functions for Different Problems

Doctors don’t prescribe the same medicine for every disease, we don’t use the same key to unlock every door, and we can’t solve every problem in life using the same solution.

We also can’t apply the same functions for different problems because this would only be theoretical knowledge. Some amateur data scientists may be tempted to implement the same functions, tools, and courses to every problem.

Every problem is unique, so its solution should be, as well. There are so many types of data we have like text data, voice data, image data, numeric data, time-series data, etc and they all need to be handled differently. Just like we have specific libraries in machine learning, we have different NLP libraries like NLTK, Spacy, and more. For images and videos, we use a Convolutional neural network. For time-series analysis we have ARIMA. 

Similarly, there are many functions and algorithms in the Scikit-Learn library. Data scientists can’t use NLTK libraries for computer vision problems and vice versa. 

7. Focusing Too Much on Accuracy

Accuracy is not the only measure for a good model. The client doesn’t want a black-box model that only gives good accuracy. Accuracy is good, but it’s not everything.

Data scientists should explain how the model reaches accuracy, which features play an important role, why they chose a particular algorithm, the behavior of some different algorithms, and more. Otherwise, the client will reject your model.

Also, parameters of the live production unit should be taken into consideration while developing a model. Otherwise, the work will be a waste and it may need to be redone to match the live environment configurations. 

For example, we have an imbalanced dataset of 1000 data points, out of which 900 are negative and 100 are positive. And, our model predicts all data points to be negative. It means out of 1000, 900 points are predicted accurately. So, our accuracy will be:

The model is achieving 90% accuracy, but still, the model is a dump model. High accuracy does not always mean a good model.

8. Not Optimizing Your Model with Time and Data Changes

To become a better data scientist, optimizing your model is a must. You won’t get good results with a single go because the model needs to be optimized with time and data changes. 

Optimization is at the heart of every machine learning model. Setting optimized values to hyperparameters gives peak performance, which is why you should revisit and optimize your model from time to time based on new data. This data could include customer behavior changes, trends, seasonality, and more. Data science is a cycle of training, evaluating, optimizing, and re-training.

Let’s look at a simple example in our daily lives:

You have a fixed route to get home from the office every day. Your fixed route has a stadium in-between. Switching to a new route to get home on a game day will save you time. This new route is an optimization that you made.

9. Lack of Consistent Model Validation

It is a big problem for data scientists when a model works well on training data but it doesn’t fit unseen/new data. This might be a problem of overfitting or underfitting, poor predictions, and poor performance.

These problems happen due to a lack of model validation. Model validation means validating your model on some part of training data which is not being used for training. With the model validation approach, a data scientist can validate model accuracy and optimize the model accordingly to reach its peak potential.

Validation helps reach a balanced training model as shown in the image above.

There are two ways to approach model validation: cross-validation and hyperparameter tuning. Model validation consistency has a crucial impact on the results and helps maintain a bias-variance tradeoff, which is an important property of the model.

10. Not Choosing the Right Tools Considering the Problem at Hand

There are so many algorithms available and sometimes data scientists implement different algorithms without actually understanding the problem. Choosing the right algorithm for a given problem is crucial.

There are many tools, including statistics and probability, exploratory data analysis, data-preprocessing steps, and feature engineering, to understand the problem correctly. Without understanding the requirements and problem at hand, just implementing various algorithms is like driving on the road without knowing the destination. You’ll go nowhere!

Here’s an example: 

Data scientists need to segment potential mobile phone customers on the basis of their income. The income brackets are below $800 per month, between $800-$1,500 per month, and above $1,500 per month. For the given problem, clustering algorithms will work. If a new data scientist tries to implement a classification algorithm, it won’t help.

So, choosing the right tools for the problem at hand is very crucial.

Conclusion

Being a data scientist is a journey of learning and evolving with every new problem. Don’t get frightened by these mistakes when starting your career. They will definitely teach you how to handle different machine learning problems practically.

There is popular saying: “mistakes are the stepping stones to learning’. Keep making mistakes, but keep learning from them and never repeat the same mistake twice!

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