Harness The Power Of A Data Flywheel For Your Business

Data fuels the business insights that drive innovation and strategic decision-making. While most businesses buy into the value of data, many struggle to effectively extract the meaningful insights they need to build a cohesive strategy. Mobilizing data can be an overwhelming challenge, particularly for businesses that don’t employ a team of data scientists. Fortunately, a data flywheel can simplify the process and provide a step-by-step guide to creating a strategic data culture that starts small and builds momentum as you grow.


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What Is a Data Flywheel?

What Is a Data Flywheel?

A data flywheel is a concept for developing a data strategy based on continuous growth and improvement. The idea is simple: As you collect data, you use it to improve your processes or products. Then you collect more data, which you use to make more improvements. The more data you collect, analyze, and act on, the better you become at using data to drive improvement and innovation.

A physical flywheel is the basis of a data flywheel, meaning the idea is to gain momentum over time due to small efforts applied continuously. A data flywheel is effective because you don’t have to begin with a fully fleshed-out data culture. Just start where you can by collecting data and taking action. You’ll learn as you go.

Steps in a Data Flywheel Strategy

Steps in a Data Flywheel Strategy

Every business is different, and your organization will develop a data flywheel strategy that suits your needs over time. While the particulars may vary, most data flywheel strategies include steps for collecting, analyzing, and storing data.

Data collection

The first step is collecting data from relevant sources. Again, you’ll gain experience identifying the most valuable data sources as your data flywheel speeds up. When you’re first starting, you can collect data from customer interactions, social media, forums, and your competitor’s websites, in addition to many other sources.

Data management

After you collect data, you’ll need to securely store it to avoid cybersecurity threats. You’ll also need to organize it for easy access and analysis. As your data flywheel grows, the amount of data you need to store will increase exponentially.

Data analysis

Once you have your data, you can analyze it to find patterns and trends that can answer critical business questions. You can do this with tools such as statistical analysis, data visualization, and machine learning. The type of analysis you need to do will depend on the type of information in your data flywheel.


This is where the value of your data flywheel becomes apparent. You might discover that your customers spend more if your website uses blue as a background color instead of yellow. Or maybe they don’t care about that feature you planned to roll out but are clamoring for one you hadn’t considered. Whatever you find out from your data flywheel, respond to it by improving your business practices.


Now it’s time to continue the cycle to build your data flywheel’s momentum. Collect feedback and measure the results of your change. Use this feedback to refine your data strategy further. You might find you need to make more improvements, or you may need a different data set to move forward.

Either way, you’ll start the iterative process over again, leading to a cycle of continuous improvement and growth. Momentum increases at an accelerating rate, so you may start slowly, but you’ll notice increasing speed and advancements as you continue.

Getting Started With Data Flywheels

Getting Started With Data Flywheels

The good news is you don’t have to have all of the people, processes, and tools in place and fully equipped before you can begin implementing a data flywheel. However, taking the time to strategize before you start can help you avoid fumbling around so much in the beginning and increase the value of your initial data.

Figure out your problem

Starting with the right question will help you collect the best data and make the biggest improvements. While collecting data on your website traffic can be valuable, it’s not the best metric if you need to understand your customers’ motivations.

If you have an area where you’re significantly underperforming, then you have an obvious question to ask. If you know that you could be doing better with a data strategy but are unsure how to start, it’s worth investing some time upfront to figure it out.

Define your goals

Start by thinking about what you want to achieve. What’s the one thing that could move the needle forward on your overarching business goals? Do you want to increase sales, grow your email list, or develop a good marketing strategy? The clearer you are on your goals, the more value a data flywheel will provide.

Identify your KPIs

Determine what key performance indicators will best measure the success of your goal. Measure these at the beginning of the process.

Conduct a gap analysis

Look at the data you currently have to determine what insights you already have and what data you need to answer your key question. For instance, if you’re a retail store and want to improve customer retention rates, you may already have data on customer demographics and purchases. However, you may not know what else you need to determine why your customers aren’t returning.

Conducting a gap analysis can show you that you need more data on customer satisfaction levels, feedback on customer service interactions, and more information about the customer journey from initial purchase to churning. The data flywheel will highlight areas you need to work on to improve customer retention rates.

Develop a hypothesis

Based on your gap analysis, you can develop a hypothesis about what data you need to answer your key question. In the example above, you might need more information about customer satisfaction with their purchases.

Collect the right data

Once you’ve identified your problem and determined what data you need to answer it, you need to ensure you’re collecting the right data. These winnowing processes will help you focus your resources. The people, processes, and technology you need will vary based on the questions you need to answer and the type of data you need to collect.

Instead of investing in an overly broad data environment, you can scale as your data flywheel matures. Knowing what data to capture is vital to assembling your data strategy’s elements. When you solve the problem in front of you instead of trying to predict all possible directions and outcomes, you’ll get results that will speed up your data flywheel and make the next steps easier.


As your data flywheel starts spinning, you’ll see new connections and opportunities for experimentation. Industry leaders such as Jeff Bezos have attributed their success to constant experimentation. Although one of the frequently-touted benefits of a well-developed data strategy is the ability to make data-driven decisions, the truth is that data eliminates the need to make decisions.

When you combine effective data analysis with frequent experimentation, the right decisions become obvious. “Fail fast” has long been the mantra of agile development teams, but it also applies to businesses. The more often you experiment and fail, the faster you can find solutions that work.

Your data flywheel facilitates this process as each iteration speeds up and generates more connections, insights, and hypotheses. No matter how innovative a company is, it won’t survive stagnation.

Some of the ways you can interrogate your data to promote experimentation include:

  • Identify areas for improvement
  • Generate hypotheses
  • Select variables to manipulate
  • Optimize experiment design
  • Determine the generalizability of your findings

Expand on your initial results

Though it can be tempting to hop on every issue you see, jumping from one issue to another is often counterproductive. While that may sound contradictory to the above advice to experiment, focusing on one point can drive more impactful results.

Though there’s little doubt that every area of your business can benefit from a data flywheel, working outward from your original problem lets you concentrate the data flywheel effect. You already have the people, places, and technology to address your starting problem, so you’re ready to tackle the next issue by building on the foundation you’ve laid. Prioritizing problems that are categorically related to your original problem will prevent you from having to reinvent your data flywheel.

Flywheel Data Types

Flywheel Data Types

Part of the design of your data environment will depend on the types of data you’re collecting. You’ll need different tools for collection and analysis, which is an important consideration in choosing your people, processes, and technologies. The two main types of data are qualitative and quantitative.

Quantitative data

Quantitative data includes anything that can be expressed as a number. Many types of quantitative data can be useful for your business. Quantitative data is often available in a structured format and is comparatively easy to analyze.

Sales data

Sales data such as sales revenue, number of units sold, and cost per lead can help you identify what pricing strategies are effective, what your most popular products and services are, and where you should target your marketing budget.

Website traffic data

Tracking the number of visits to your website, how long visitors spend on each page, and what pages they visit most often can give you valuable information. You can discover how customers interact with your website and develop a strategy to increase traffic and sales.

Competitor data 

To be competitive, you need to understand your competition. You can do this by collecting data on your competitors, such as price points, sales, and other financial data. While you don’t necessarily want to be the lowest price point, knowing what your competitors are charging will help you target your marketing message to demonstrate why your product is a better value regardless of price.

Qualitative data

Qualitative data is nonnumerical data. It’s often in unstructured formats, so collecting and analyzing can be much more challenging. However, the insights you gather can be richer and more detailed, so it’s usually worth the effort. The types of qualitative data your business can benefit from are almost endless.

Customer sentiment

Finding out what customers think about your brand (or your competitor’s) can drive many high-value strategies. If you uncover a weakness in your competition — maybe their customers complain about a feature that’s not user-friendly — you can capitalize on it in your product design and marketing.

On the other hand, if you notice a negative trend in your own data, like an uptick in complaints about your latest product, you can quickly course-correct and address it.

Trend analysis 

Identifying emerging trends is tremendously valuable for business. When you know how to track and analyze consumer behavior, you can gain a competitive advantage by identifying unrealized opportunities for growth and expansion.

You can also find areas that are ripe for innovation and brand differentiation. Getting ahead of the competition can be the difference between selling out of a hot new product and being stuck with crates of unsold inventory.

Lead generation 

You can collect public information to identify potential customers and create a targeted marketing or outreach campaign. You’ll need to comply with data protection regulations, but if used ethically, lead generation can be a powerful tool for customer acquisition.

Market research 

Qualitative data can provide a rich source of market research without the need for expensive interviews and focus groups. You can identify customer needs, preferences, and pain points to guide product development.

Additionally, you can use social listening — monitoring channels such as social media platforms, forums, and blogs — to determine the language that resonates with your customers the most. You may discover that what you thought was hip, relevant language marks you as passé and misses the mark with your target audience.

Primary vs. secondary data

All types of data can be either primary or secondary, and you will probably use a mix of both. Primary data is data that you collect specifically for your research. It can take the form of interviews, surveys, or direct observation. You can tailor your collection methods to suit the question you’re trying to answer. The biggest drawback to primary data is that it can be expensive and time-consuming to collect.

Secondary data already exists in vast quantities on the internet and is fairly cheap and easy to collect through web scraping. You’re mining it for insights and patterns. It’s also the type of data that can generate unexpected insights and opportunities for innovation.

Internal vs. external data

Internal data is all of the data that your business and customers generate. It includes sales data, revenue, inventory, customer feedback, financial statements, labor costs, and employee performance evaluations. Internal data provides important metrics for evaluating the health and success of your business.

External data is information that originates from outside sources, and it can include market reports, industry benchmarks, and population statistics. While internal data is most helpful for tracking your progress and evaluating the results of experiments, external data often provides insight and ideas that aren’t available through internal sources.

Flywheel Data Science Collection

Flywheel Data Science Collection

You can only power your data flywheel by feeding it massive amounts of data. You probably have data collection methods in place that make internal primary data collection fairly painless. However, you’ll also need access to external data sources for your data strategy to realize its full potential. Collecting primary external data on such a scale would be prohibitively expensive for most businesses. Fortunately, web scraping provides an inexpensive alternative to collecting external data for your data flywheel.

Web scraping

A web scraper is a program — or bot — that automatically collects, parses, and extracts data from a website. You can buy a web scraper or, if your technical skills are up for the challenge, program one yourself.

Using a web scraper is a little technical but nothing overwhelming. You need to understand a little about HTML but don’t need to know how to code. Once you have one, tell it where to find the data you’re looking for on your target website.

You’ll need to examine the website and determine where the data you want is stored. For example, if you want to analyze your competitor’s prices, you’ll go to the website and determine exactly where that information is stored by examining the HTML attributes associated with it.

Then you’ll tell your scraper where to find the data. The scraper will analyze the website, extract all the data stored in that location, and export it to a usable format such as a JSON or CSV file.

Effective web scraping with proxies

Web scraping is highly cost-efficient, but you will need to invest in a few tools. In addition to a web scraper, you’ll need proxies. A proxy is an intermediary between your computer and the internet to disguise your IP address.

When you send a request to a website using a proxy, it goes to the proxy server first. The proxy attaches a new IP address to the request and then sends it to the website. When the website responds, it sends the response to the proxy server, which sends it back to you.

That may seem like a lot of extra effort, but it’s necessary when using a web scraper. Most websites have anti-bot technology that automatically blocks your IP address if it detects bot-like activity.

While there’s a good reason for this — many nefarious actors use bots for malicious purposes, and even benign bots can overwhelm a server — you’ll need to get around the anti-bot measures to scrape data. A proxy IP address can help you avoid being detected as a bot.

However, swapping out one IP address for another is not enough. If you do that, you’ll just get your new IP address blocked. You’ll need to use a rotating pool of proxies, which uses a different IP address for each request. Instead of your scraper looking like it’s sending 1000 requests from one IP address, it will look like 1000 different users are involved.

Ethical web scraping

As with all aspects of data collection, it’s important to engage in ethical web scraping. You can cause unintentional harm by being careless. Web scrapers can act so quickly that they can overwhelm a server with requests. You can also expose people to unnecessary risks by collecting too much data. To scrape data with a clean conscience, follow these best practices:

  • Always use the API if one is available and has the data you need
  • Build in some delays to slow down your web scraper
  • Read the robots.txt file and follow the instructions
  • Collect only the data you need
  • Make sure any sensitive data you collect is properly stored
  • Use an ethical proxy provider such as Rayobyte


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Next Steps in Building a Data Flywheel

conclusion on flywheel data

The data flywheel concept is a powerful tool for businesses that want to build an effective data strategy. By implementing a continuous improvement process for collecting, processing, and analyzing data, you can create a virtuous cycle that will lead to better products, services, and operations.

At Rayobyte, we’re committed to ethical proxy practices that will help you gather the data you need without causing undue risk or harm. We go beyond being your proxy provider and partner with you to reach your business goals. Our proxies — from data center to residential to mobile — are the most reliable on the planet. We own our infrastructure, have the best uptime guarantee, and can create customizable solutions to suit your use case. You’ll have fewer bans and greater success with data scraping — allowing you to use the power of data to drive your business strategy. Reach out today to learn more about how our proxies can help you create an effective data flywheel.

The information contained within this article, including information posted by official staff, guest-submitted material, message board postings, or other third-party material is presented solely for the purposes of education and furtherance of the knowledge of the reader. All trademarks used in this publication are hereby acknowledged as the property of their respective owners.

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