How to Build a Robust Data Product Using Your Organization’s Internal Data

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Sometimes the materials for innovation are already within your grasp, or in this case, within your organization. Likewise, your internal data may have untapped potential to become a robust data product, much like Post-it Notes became a staple in offices worldwide.

In 1968, a scientist at 3M, Dr. Spencer Silver, was trying to develop super-strong adhesives. Instead, he ended up with a ‘low-tack’ adhesive that could stick to objects but also be easily lifted off. This internal ‘failure’ sat unused for years until another 3M scientist, Art Fry, saw its potential. Fry used this internal knowledge to invent Post-it Notes, turning a seemingly unremarkable discovery into a universally used product. 

Companies have a treasure trove of internal data they can turn into a killer data product. One that actually solves problems and maybe even pulls in some cash. Done right, a data product becomes more than just a dataset; it’s a tool that gets the job done, helping you and your team make smarter decisions.

What is internal data?

Internal data refers to the information generated within an organization. Internal data can’t be generated or acquired from an external source because it’s proprietary and unique. It encompasses:

  • Sales data
  • Finance data
  • Internal marketing data
  • Human resources data

This data is gold. It can give you a close-up look at how your business is doing, how your customers behave, and so much more.

Data products and the importance of using internal data

If you know your internal data well, you can create data products tailored to your business. Data products built with internal data can help you make better decisions and even grow your business. For instance, Netflix used its internal data about customer sentiment and their desire for political dramas to create “House of Cards.” Netflix looked at viewer data like watch history and ratings to figure out that a series starring Kevin Spacey would be a hit. And, well, they nailed it. The show was a huge success and kicked off their foray into original content.

The real-world value of using internal data in data products

Your internal data is like a backstage pass to your own business. It helps you spot patterns, measure how you’re doing, and make smarter calls. For example, looking at your cash flow data can show you where you’re losing money or where you could be making more, helping you allocate resources like a pro

But the real magic happens when you start using this internal data in your data products. Imagine using your customer behavior data to fine-tune your app’s recommendation engine. Or using employee performance metrics to create a more balanced workload, which could boost overall productivity. In short, integrating internal data into your data products turns good decisions into great strategies, elevating your whole game.

Set objectives

Building a data product requires clear goals. Know what you want to achieve and make a game plan. It’s best to team up with data scientists, business analysts, product managers, and other roles that help you understand what customers need. When you’re on the same page as your customers (even if they’re internal data consumers), your data product is more likely to hit the mark.

The importance of setting clear goals prior to developments

Successful products are rarely built by accident. Having clear objectives for your data product sets the stage for you to:

  • Make something that people need and want to use
  • Differentiate your product from competitors
  • Create a transparent roadmap for development
  • Ensure that the development process is repeatable and dependable
  • Provide consistent and accurate results

Once you set clear goals around what kind of data you need and how you should organize it, you’ll find it easier to manage your data and get useful insights. You’ll also find that everyone can make smarter choices based on the data product’s purpose.

Assemble your team

Building a data product requires a mix of experts like data scientists and business analysts. A balanced team makes sure the product works well, covers all business angles, and gives you useful tips for growth and performance. It should include a diverse cast of pros in the mix to ensure the data product works. 

Data scientists dig into the numbers, and business analysts figure out what it means for the business. Software engineers and product managers make sure the product is easy to use and fits the company’s goals. Throw in some sales know-how, and you’ve got a team that covers all the bases for a solid, user-friendly product.

Data collection and cleaning

If you’re building an internal data product, getting your internal data right is a must. Make sure it’s current, accurate, and actually useful for what your company needs. Spending time collecting and cleaning up your data pays off—you’ll get info you can really use to grow your business.

To ensure clean and accurate internal data, it is essential to:

  • Identify and eliminate or update legacy systems
  • Utilize technology tools that guarantee data accuracy and currency
  • Establish business use cases that enhance data quality

Keep your data in line by making sure everyone enters it the same way, fixing any errors, and writing down how you clean and change it. Follow these steps and your data’s set to help you make smarter business decisions.

Data analysis

Dig into your internal data to spot trends and patterns that can shape your data product. Use different ways to look at the data so you can pull out key info. Obtaining this info can help you make smarter choices, fine-tune your methods, and run things more efficiently.

Effective data analysis techniques include:

  • Descriptive analytics
  • Predictive analytics
  • Prescriptive analytics
  • Machine learning

Each of these data analysis techniques will help you extract valuable insights from internal data. By using these methods, you’ll gain a better grasp of how you’re doing, spot where you can do better, and make smarter choices that make everything run smoother.

Build the product

To build a data product, you’ll need the right tools and a game plan that lines up with what your company needs. Stick to a solid process and you’ll get a product that not only makes sense of your data but also helps your business grow. Just remember to know your data, test it out, and keep an eye on how it’s doing to make sure it stays on point.

Consider using a platform like Revelate, which is tailor-made for building and deploying data products.

Test and validate

Before you roll out a data product in your company, make sure it works well. Test it a lot so you know it gives you the right info for making smart business choices. Use different kinds of tests like unit tests and user tests. Keep an eye on how it’s doing over time, and tweak it when you need to. This approach keeps your data tool useful and up-to-date.

Products are only successful when people use them. Be sure to test the product and your assumptions by paying attention to who is using the product, how they’re using it, and whether it meets their requirements.

Deploy

To get a data product off the ground in your company, you need a good game plan. Here are the steps involved in the process: 

  1. Define the scope of the data product
  2. Develop an implementation plan
  3. Evaluate the product
  4. Train users
  5. Monitor and maintain the product

Stick to these steps, and your launch will be smooth. Your data product will be ready to go and help you make smarter decisions. But make sure you plan and act carefully.

User training and adoption

To really make your data product count and help your business grow, make sure your team knows how to use it. Training sessions, demos, and workshops can show them how to make their jobs easier and make better choices using the product. They’ll learn not just the basics but also how to understand online search techniques better. Once you get everyone on board with being data-savvy, your team will know how to use the new data product well, helping the business grow and get better.

Monitoring and maintenance

Regularly monitor and maintain your data product to ensure that it will remain effective and relevant. Here are some key steps to follow:

  1. Update the data as necessary to ensure it is current and accurate
  2. Implement a data quality monitoring solution to identify and address any issues or errors
  3. Iterate and develop the data product based on user feedback and changing requirements 

By following these steps, you can ensure that your data product remains up-to-date, accurate, and aligned with your organization’s needs and goals.

How Revelate helps advance data productization

Revelate makes it a breeze to turn your company’s internal data into something useful. Its platform sorts and cleans up your data, making it ready for action. No need to stress about if your numbers are reliable; Revelate’s got that covered. It’s like spring cleaning for your data, allowing your team to focus on making better decisions, not fixing messy data.

But it doesn’t stop there. Revelate also offers killer analytics tools that can help your team find trends and insights you didn’t even know were there. It’s basically giving you a cheat sheet for growth. The platform is user-friendly, too. So, your team can dive right in, get comfortable, and start making the most of your new data product.

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