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Why Your Company Should Consider Data as a Product

Data is more than just information. With the right approach, it’s also an asset — and one that companies can monetize in exciting ways through the data-as-a-service business model.
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Sean Beard
Why Your Company Should Consider Data as a Product

AT A GLANCE

  • Companies think of data as valuable, but many have never turned data into a viable product. This inaction has led to everything from data silos to disconnected information systems. 
  • As a company begins developing data products, it will require extensive experimentation. Developers and stakeholders must draw on information from across an organization. 
  • The best way to foster a data product is to collect as much data as possible. Machine learning algorithms can monitor how a product performs, search for peaks and valleys in performance, and address critical issues.  

Data is more than just information.

With the right approach, it’s also an asset — and one that companies can monetize in exciting ways through the data-as-a-service business model.

Are you one of those companies? The answer is “yes” more often than you’d expect. Companies have grown accustomed to thinking of data as valuable, but many have never actually turned data into a viable product. This inaction has led to everything from data silos to disconnected information systems. In fact, about half of organizations surveyed by Syncsort report they’re only “somewhat effective” at deriving value from data. A mere 9% report they’re “very effective.”

Unfortunately, whatever potential exists in data — whether it’s a product, service, or another revenue stream — is often lost. This is because it’s often disorganized or difficult to access; silos obscure its true value and make it harder to leverage data insights to elevate a company’s bottom line. This means turning your data into a product starts with the right data-management strategy.

Building a foundation for data as a service

Realistically, most companies aren’t ready to embrace the data-as-a-service model. To understand why, consider this: Accenture found that 90% of information technology leaders at enterprises report being satisfied with their cloud initiatives. However, only about one-third have achieved all their objectives in terms of speed, business enablement, levels of service, and cost. This illustrates a common problem: Companies often leverage emerging technologies without proper preparation.

The key to that preparation is integrating and centralizing as much enterprise data as possible. As your company begins developing data products — or simply exploring their potential — it will require extensive experimentation. Developers and stakeholders must draw on information from across the organization, meaning data needs to be democratized across the organization rather than segmented in silos.

It should also be available as deeply as possible. After all, developing data products is an iterative process. Ideas must go through cycles of testing and refinement, taking a loose concept from “possible” to “production-ready.” It isn’t easy, which is why it’s so important to have in-depth data accessible throughout. At Pariveda, for example, we embrace this iterative life cycle by helping clients create “concept cards” that outline potential use cases for the data that new technologies uncover.

Trying to bring a product to market without enough insights is a big risk — even when the company already has the insights it thinks it needs. Avoiding failure is as simple as making data as accessible as possible. 

Supporting a product built on data

Perhaps unsurprisingly, the best way to foster a data product is to continue collecting as much data as you can. Machine learning algorithms can monitor how a product performs, search for peaks and valleys in performance, and proactively address issues that could bring the product offline or compromise user experience.

Users are the other data source to monitor. In fact, social media is a gold mine for identifying how users truly feel about a specific product. Harvesting and analyzing that data can give suggestions about how the product can be improved (and whether it’s truly worth improving). The truth might be harsh, but by tracking how users actually feel, you can fully realize your product’s revenue potential.

All this data is meant to facilitate ongoing product improvements, but it’s difficult to work on something that’s actively in service. Companies are increasingly turning to digital twins, or digital product replicas that enable companies to test product tweaks in hypothetical scenarios with no consequences if something goes wrong, which enables them to foster cultures of experimentation.

In a data-driven future, other assets will pale in comparison. Data is more than just a lucrative product: It’s also the best resource for developing and optimizing that product. No matter what industry you operate in, data will determine your fate. Whether that’s positive or negative is up to you.

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By Sean Beard
Vice President
Enterprise Architect, technology pundit, professional hobbyist, amatuer woodworker, retired DJ, and lifelong Houston Astros Fan. Entertainer to many and entertained by life's mysteries.

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