How to get more value out of enterprise data
A new report points to DataOps as an overlooked path to business growth
Let’s take a look at why DataOps is the success in the current data economy.
Of course, not all data is equally valuable. In addition to its source, nature, and format, data’s usefulness depends on external factors, including the business objectives of the company that has the data (what it wants that data to accomplish); any customer restrictions on data’s use; laws and regulations governing the use of data, and more. Taking these considerations into account, a company can classify various types of data—and put them to use accordingly.
Value that Keeps on Giving
For today’s winning enterprises, this kind of intentionality about data is not an option. Data-driven companies understand how to use data to create value and to lower costs.
Many currently ascendant and emerging verticals understand this (smart manufacturing, autonomous vehicles, etc.). Data is their value proposition by design. An AV company understands that more data means more insights. Other, more traditional, enterprises have tended to treat data as a necessary evil, something to manage defensively, with regard to risk prevention and compliance, spending as little as possible. This approach fails to see data as having inherent value that keeps on giving.
If your enterprise is not digitally transformed, it’s going to be disrupted. The key question is not whether a company will be digitally disrupted, it’s how.
Digital transformation—sped up by the ongoing global pandemic—is here to stay. Humanity’s most daunting problems are being solved through tech. A current example is the search for patent-free treatments for Covid-19 through the distributed computing project Folding@home. Doubtless, data can be used for evil. But, from medicine through communications, tech solves more problems than it creates.
DataOps to Data Value
So how can companies ride, rather than become swallowed up by, the wave of digital transformation?
Business owners should watch out for top five challenges in putting data to work. According to the Rethink Data survey, they are: making collected data usable; managing data storage; ensuring that needed data is collected; ensuring the data security; and making the silos of data available.
The good news: the 1,500 global enterprise leaders queried for Rethink Data identified the solution to these challenges: DataOps. IDC defines DataOps as the discipline of connecting data creators with data consumers. Gartner describes DataOps as the more tangible “hub for collecting and distributing data, with a mandate to provide controlled access to systems of record for customer and marketing performance data, while protecting privacy, usage restrictions and data integrity.”
While the majority of respondents say that DataOps is “very” or “extremely” important, only 10% of organizations report having implemented DataOps fully. DataOps means, first of all, having cross-company conversations about data, leading to classifying types of data and designating purposes for various sets of data. It’s about figuring out what we want data to tell us. It’s about relying on tech, like automation and virtualization tools, to access data and ensure its visibility. It’s about preempting stagnant data swamps and making way for vibrant, insightful data lakes.
The even better news? DataOps invariably leads to better business outcomes. It boosts customer loyalty, revenue, profit, cost savings, employee retention, and other benefits.
DataOps is therefore both the means of digital disruption and the way to prevent falling victim to it. For these reasons, DataOps should not be relegated to IT, and forgotten. Far from it: it should be a responsibility of all C-suite executives and business owners—and maybe even a Chief Data Officer.
The way I see it, the tech implementation of DataOps is essential—and relatively easy. Savvy IT architects as well as virtualization tools go a long way. There are useful blueprints out there, including “The Seven Steps of DataOps” from DataKitchen and “The Definitive Guide to DataOps” from StreamsSets. You can buy the tech and put it to work.
The human part is tricker. Like any successful human endeavor, DataOps starts and ends with communication—conversations about what we know, and what we want learn. It’s time for enterprise leaders to stop overlooking the value that DataOps unlocks.
Erez Baum is the head of Lyve Labs Israel