5 Steps to Building a Successful Data Product

Every successful organization has valuable data that has fueled growth, innovation, and achievements. Many of those successful organizations are realizing that there is insatiable demand for that exact data that they have been automatically generating and that there is opportunity to turn that data into a brand new source of revenue.

However, packaging your organization’s data into products that will generate revenue requires some forethought and strategy. To get your data in front of interested buyers and procure sales, you need to build data products that your buyers truly need and are actively searching for.

What is a data product?

A data product is data that has been cleaned and packaged into a usable and buyable product that data consumers can purchase. Data products are built in order to provide insights and illuminate solutions to specific problems or questions. They can be one of the most powerful products that your company offers. A good data product is capable of generating new revenue sources, enhancing customer experiences, offering new solutions, and even revolutionizing entire industries.

1. Identify your customer’s questions**

The first step to creating a successful data product is to determine who needs your data and why. No organization wants a bunch of unusable data taking up space in their systems–they are going to make a strategic decision to purchase your data in order to solve a particular problem or answer a specific question.

Identify what questions your buyers might be asking and what problems they want to solve. If you are a collector and supplier of weather data, for example, you would consider what questions different industries and departments are asking that your weather data could answer. For instance, agricultural industries might be searching for the best time of year in a particular region to grow different crops. Hospitality industries might be interested in the weather patterns of high-tourism locations. How can the unique capabilities of your data solve for the needs of those data consumers?

2. Give your customers the answers**

A good data product needs to be a solution. You must be the answer to your buyers’ questions if you want your data products flying off their virtual shelves. If you are anticipating agricultural industries might be purchasing your weather data to determine the best time to grow a particular crop, you might create a data product that includes historical information about precipitation, humidity, and temperature in a variety of farmland regions. If you want to create a product that provides solutions for hospitality industries that are looking to build new locations, you might consider creating a data product that includes information on natural disasters, rainfall, and snowfall in locations with high tourism rates.

Once you have formed an objective and focus, you will have a definite foundation to build a useful data product. To build your data product around that central objective effectively, ensure that every dataset you feed into the finished product is there for a purpose and contributes to your objective.

3. Set high standards for your data**

Buyers don’t want a messy pile of questionable data, they want trustworthy insight and solutions. To ensure you’re delivering a high quality product, you’ll want to make sure that you are providing data that is:

  • Deep: Deep data refers to data that is high quality, relevant, and actionable. Your data should be proven to be valid, collected reliably, and as accurate and relevant as possible. Make sure that all unnecessary, unusable, or inaccurate information has been removed.
  • Extensive: Your data should cover a large area of interest, meaning it should contain a decent amount of records. Buyers will want the most bang for their buck, so they won’t be enticed to acquire data that is small in scope. The more information you offer (as long as it is relevant and actionable) the better.
  • Complete and Consistent: Make sure that all possible data that is required is present and that there are no conflicts in information. You want your buyers to trust your data and not be frustrated by missing or conflicting information. Building trust with your buyers makes it more likely that they’ll return to purchase more of your products in the future.

4. Create and customize your product**

When it comes to actually creating your data product, there are two directions you can go. The first option is to create your data product from scratch - that means getting multiple internal teams to collaborate on producing a final product. Your workforce will need to manually and painstakingly validate, clean, and organize all of your datasets, which could take up a decent amount of time and labor.

The second (and much better) option is to use a platform that automates the process for you. You should have a single platform that enables you to build an entire data product from start to finish. That means all of that annoying validating, cleaning, storing, and organizing is done faster, cheaper, and with less room for error. Using Narrative’s data collaboration platform, you can ingest large amounts of data, clean it, assemble it, and package it all in one place.  A data product which requires manual execution at multiple points is often ineffective. The less human interference throughout the process, the less room for error, the less delay, and the less busywork for your human employees.

With cutting-edge software like Data Shops, your raw data is automatically validated, cleaned, and transformed into standardized data sets that can be easily packaged. Using that same software’s advanced filters and tools, you’ll be able to create custom data products to meet your customers’ exact needs. And, you can do all of this without having extensive technical expertise–the platform is designed to be a no-code, intuitive, point-and-click interface that anyone can master.

5. Find and deliver to your buyers**

Once you’ve created your data product, you need to get your product in front of interested buyers. You, once again, have the option to do this all manually, which is an outdated and rigorous process that could take months or even years. Traditionally, you would have to seek out organizations to negotiate with and get your legal teams and data engineers in gear to finalize contracts and make your data transferable.

However, the easiest and most effective way to start selling your data products today is by standing up an ecommerce data store. This is a relatively new option that is lowering the barriers to entry in the data commerce space and making it possible for companies to sell first-party data to multiple buyers with just a few clicks.

Data Shops makes it easy to build an ecommerce storefront and sell your data products directly to multiple interested buyers. All you have to do is set up a self-service storefront using the tools provided within the platform, make your data discoverable with the help of tagging and basic SEO practices, and sit back and relax while your data products are automatically delivered to buyers.

Start creating and selling your data products as soon as today! Our team of experts can help you get started.

< Back

Hi! I’m Rosetta, your big data assistant. Ask me anything! If you want to talk to one of our wonderful human team members, let me know! I can schedule a call for you.