How An Effective “Data Flywheel” Can Boost Your Revenue, Marketing, And Product-Led Growth

Companies are often overwhelmed by the amount and variety of data available and don’t know how to leverage it to enhance their revenue, marketing, and return on investment (ROI).

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Creating a data-driven “flywheel” is one way to boost your company’s ability to make data-driven decisions. Here’s some info on the data flywheel effect and some tips for getting your data flywheel to spin faster. You can also use Scraping Robot’s application programming interface (API) and web scraper for your data flywheel.

What Is a Data Flywheel?

What is the meaning of a data flywheel? It refers to when the momentum of a process or product increases at an accelerating rate due to strategic data usage.

To understand how a data flywheel works, think of a flywheel — a wheel attached to a rotating shaft that uses the conservation of angular momentum to store rotational energy. As the flywheel rotates, it builds up energy. Eventually, it becomes self-sufficient. A data flywheel works similarly. As a company uses more data to make business decisions, the company will start creating better products. The data flywheel will also lead to faster learning curves, higher customer retention and acquisition, and higher revenues.

Netflix’s artificial intelligence (AI)-powered recommendation feature is an example of a data flywheel. Initially, the feature suggested the most popular videos. Over time, Netflix collected user viewing and rating data and utilized a “recommendation engine” to analyze this information. As a result, each user was provided with personalized “recommended for you” suggestions, increasing viewership. Netflix now has over 232.5 million paid subscribers.

How To Use a Data Flywheel

Here’s a breakdown of how to implement and use a data flywheel:

1. Pick the right problem: First, choose a problem to solve. The problem should be simple, easy to explain, and involve objects or services people need.
2. Capture and store the data you need: The next step is to gather the data you need to solve your problem. Think about where you need to get the data, how to get it, and how to manage it. This will narrow your data environment’s vast catalog of processes, tools, and people. Examples of technologies for capturing data include manual data capture, Google Forms, web scraping, barcodes, QR codes, and magnetic ink character recognition (MICR). After capturing the data, store it in forms accessible by various APIs and query languages. This will make it easy for your data analytics team to derive actionable insights from the data.
3. Pay attention to new data and opportunities: As you capture more data, your flywheel data center will spin faster, and more data and opportunities will crop up. Suppose you start a podcast to promote your publishing business, and podcast guests are encouraged to submit responses about their interview experiences through Google Forms. As you gather more and more podcast guests, you will have more information about other people’s companies, industries, and interests. You can use that information to discover opportunities, such as potential partnerships and collaborations.
4. Expand from your original problem: You will have more problems to choose from as your flywheel spins faster. To expand your scope of operations, consider focusing on new issues that are valuable, feasible, and thematically consistent with your original problem. This will ensure you can leverage the momentum you’ve built. If you choose a problem that isn’t consistent with your original problem, you must build momentum from scratch.

Tips for Improving Your Data Flywheel Strategy

Getting a data flywheel to spin faster can be challenging, especially if you’ve never done it before. Here are some tips.

Implement a semantic layer

Before you implement a data flywheel, you should create a semantic layer — a representation of corporate data that converts complex corporate data into familiar business terms such as revenue, customer, or product.

A semantic layer aims to create a consolidated and logical view of data across your company. It makes your flywheel spin faster by making data consumable by everyone in your company, not just analytics experts and data engineers. For example, a hospital can use a semantic layer to predict who will be affected by an ailment and when and where these ailments may happen. This helps the hospital determine how and where to allocate resources and time, improving patient care.

You can implement a semantic layer by:

• Leveraging a business intelligence (BI) tool’s semantic layer: Traditionally, companies used semantic models built by dashboard creators in their BI tools, such as Tableau and PowerBI. This works for data products built in the same BI instance. However, this approach leads to inconsistent definitions when you use different instances and data products.
• Building the business logic into the data warehouse: Companies can also implement semantic layers by building business logic into the data warehouse. Although this methodology gives you tight control over updates and data and one centralized hub for governing and securing access, it has several major drawbacks. For one, it can be challenging to enforce conformance and consistency. It also forces analysts to be data engineers.
• Using data pipelines: Data engineers can build data pipelines sourced from raw data assets to embed semantic layer logic. However, this approach can be time-consuming to manage. Data engineers may also find it challenging to ensure consistency as the pipeline scales.
• Using a universal semantic layer: A universal semantic layer is an independent layer between data consumers, such as BI and AI tools, and raw data assets, such as data lakes and warehouses.

Create a data literacy program for your flywheel data type

Data literacy refers to the ability to:

• Read data or understand what data is and what it represents.
• Work with data, which refers to the ability to create, acquire, clean, and manage it.
• Analyze data, which involves organizing, filtering, aggregating, comparing, and performing other operations on data.
• Argue with data, which means using data to support a larger narrative that communicates a story or message to a particular audience, such as stakeholders, the C-suite, or clients.

The more digitally literate your team is, the more insights they can glean from data. Digitally-literate staff can also use these insights to make data-driven decisions for your company.

To increase data literacy, you can implement a data literacy program. You can do this by:

1. Creating a data literacy plan: Create an approach for boosting data literacy throughout your company. Use this plan to assess the company’s current data literacy rate and develop a program to increase the rate to desired levels.
2. Pick educational tools: Next, pick education methods that fit your budget and your teams’ learning styles. For instance, you can rely on online videos if you have a small budget. With a larger budget, you can partner with an educational facility to provide in-person courses and workshops.
3. Encourage questions about data interpretation: As your staff boosts their data literacy skills, you should encourage them to ask questions about data interpretation. This will give them opportunities to apply their data literacy skills.

Use web scraping bots and web scraping APIs to gather data

Many marketers still use manual data extraction methods to gather data for their flywheels. For example, they may collect competitor data by searching for competitors online and inputting information about them into an Excel sheet. Such extraction methods are time-consuming, costly, and exhausting.

To save time and energy, you can leverage web scraping bots and web scraping APIs to gather data for your flywheel. These technologies empower you to gather more data faster and more effectively.

Web scraping bots or scrapers fetch information from a designated website and saves it as a data file. Many businesses use them to gather data. Here are some common use cases:

• Market research companies use web scraping bots to pull data from social media and forums for sentiment analysis.
• Search engine bots crawl a site to analyze its content and rank it in search engine results pages (SERPs).
• eCommerce websites use web scrapers to scrape competitor sites and identify potential customers.

Web scraping APIs are also used to gather data from sites. However, unlike web scraping bots, they don’t extract all the data on target sites. They give you only the data website owners want you to access.

Scraping Robot API and Web Scraper for Your Data Flywheel

The term “data flywheel” describes a process in which the momentum of a product or process gains speed rapidly due to the strategic use of data. You can use a data flywheel to your advantage by picking the right problem, capturing and storing the data you need, paying attention to new data and opportunities, and expanding from your original problem.

One of the best ways to implement flywheel data science is through Scraping Robot’s code-free API and web scraping bot. To use our API for data flywheels, just use our Postman documentation to plug and play. Our web scraping bot makes scraping easy by letting you bypass anti-scraping technologies, traps, and CAPTCHA. It also comes with JavaScript rendering and metadata parsing. Sign up today to get 5,000 free scrapes per month.

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