Approaching Data as a Product can Unlock Its Hidden Potential

Data AnalyticsData ScienceInternet of Things

The power that data holds to transform business is already acknowledged to be vast, and we’re constantly on a quest to manage this data better in order to achieve more efficiency. Escaping the necessity of data is impossible because it’s a key asset. Therefore, we have to find ways to turn data into a manageable asset instead. One approach is to treat data like a product. 

When companies develop products they do so with a clear picture of the ideal customer in mind and of what they want. The same thing can be done with data as a product: it can be packaged into sets that serve specific sectors in a business. In this article, we’re going through the current approaches taken on data and how businesses can tailor their approach to derive more value out of their data.

The Current Approaches to Data 

There are two main approaches to data: the big-bang and grassroots data strategy. Both of them come with major handicaps and limitations that keep the value of data locked. Let’s see how each approach works. 

The Grassroots Approach

The costs of building, managing, and maintaining the architecture for the grassroots approach are pretty high. Individual teams come together to assemble the technologies and data required to work together. The grassroots approach results in duplication of efforts which requires more time and resources.

Big-bang Strategy

As the name implies, the big-bang strategy deals with vast amounts of data. When a team applies this strategy, it extracts, cleanses, and gathers massive amounts of data. The approach reduces the amount of rework needed in the process but it’s rarely aligned with business use cases which makes it less valuable in supporting the users’ needs. 

Both of these strategies provide a clouded prediction for the current and future use cases that reveal actual value for the end users and the business. 

The Newest Approach: Data as a Product

This approach has benefits for companies that have implemented it for multiple reasons. For companies that treat and manage data as a product, it has been easier to realize the value of their investments and forecast with more value extracted from their data. Three of the key reasons for this are: 

1. Data Products Deliver All Data About Specific Entities 

The beauty of working with data as a product stands in the fact that it can encapsulate all the required information about a specific entity such as employees, branches, customers, or product lines in one space. The set of data can be used by people in the organization to solve particular problems

2. Standard Types of Consumption Are Excellently Delivered with Data Products 

For companies that provide diverse business systems, data products are wired in a manner that allows these systems to consume and utilize data according to their needs. For example, these can be reporting systems or digital apps, and each of them has its own “consumption archetype”. Data products provide data in its most suitable form for the particular consumption archetype.

3. Data Products Provide Increased Efficiency and Speed

The main problems we faced with traditional approaches to data (namely grassroots and big-bang) were concerning efficiency and speed. Data products provide enhancements in this aspect because the work process is different. Teams won’t struggle to search for data, convert it, and then build bespoke data pipelines if they’re working with data products. 

This way they’ll save time on the governance and architectural challenges that arise with it. Furthermore, data products offer other benefits, such as: increasing the use cases delivery speed up to 90%, decreasing total costs by 30%, and reducing the data-governance risks that arise. 

How to Get Started with Data Products? 

The factors that ensure success in product development are similar to those that indicate success in the development of data products as well. These factors include: 

  • Specific funding and management. Data products are managed by specific teams which include data platform engineers, data architects, site reliability engineers, and a product manager. These teams should be equipped with proper funding to enhance the product and be informed of the customer’s feedback.  
  • Established best practices and standards. Companies built successful products when they follow standards and proven practices. These are tasks that are taken care of by the Data Center of Excellence (DCoE) and include processes such as: audit data use, data provenance, data quality measuring, and establishing principles upon which essential technologies should be designed for versatility in usage.
  • Performance tracking. The success of data products depends on their performance so it makes sense to keep track of the end-user activity. This includes product use cases, survey statistics, and the ROI achieved.
  • Quality assurance. After doing the hard work of building data products, it’s important to ensure customer satisfaction by actively monitoring their quality. For this matter, data teams should monitor data definitions, availability, and access control as a few examples.

Final Thoughts

Unlocking the full potential of data requires expertise and commitment from your data teams. The obstacles seem oftentimes endless and the future loses its brightness unless you leverage data wisely today. At Blue Orange Digital we’ve helped companies reinvent their relationships data for more revenue and efficiency. Schedule a call with us today to learn more about our services.