The steady increase in the business value of data has spurred data-centric transformation affecting every industry and sector. Version 1.0 took companies from data centers to data lakes but, data like water, can lack trust, ownership, and traceability. While data lakes will continue to exist, they are yet another “silver bullet” to solve the world’s data problems that have not managed to live up to the hype.
Version 2.0 has many enterprises moving data to the cloud as business leadership starts to demand a “data for all” approach. But given the many characteristics of data that need to be considered, those that are shifting to the cloud are not doing so with all data assets.
So what is different here? This is not a technical problem anymore. What must be solved is how we are thinking about data and organizing ourselves around it.
A recent roundtable discussion with IT executives from a range of industries examined the status of data today and the challenges that come with making data more integral to the business. These insights and actions address the key topics discussed.
Turning data into insights: 4 key actions for IT executives
Data teams commonly comprise both advocates and resistors.
“Collect and store” has been the prevalent data management method for thirty or forty years. Employees' careers have focused on this method; it’s what they know and how they are accustomed to work. Many of these individuals are not interested in a big change to their workflows, and therefore will resist any extensive transformation to the data management function. On the other hand, and often on the same team, there are employees who see the potential of data transformation—and are excited about it.
Action
Harness the vision and enthusiasm of advocates on your team by putting them on small or pilot projects that involve data in the cloud. Their success will energize them even more, and may even help loosen up the resistors and encourage acceptance of changes to workflows and tools.
Data-as-a-Product is becoming a prevalent mindset.
There is a trend to move beyond dashboards and scorecards to applications that can leverage data on their own or in tandem with human users. This trend shifts focus to the customer for the data. Requiring teams to become product-focused and make data accessible and usable directly by the business.
Essentially moving from a reactive capability to a proactive stance, where data significantly enables the business processes that are already in place. Embracing a Data-as-a-Product mindset democratizes data, making it owned and easier to discover, consume and trust.
Action
IT organizations are proven performers when it comes to projects, and they now need to shift to a product approach. Use product language, work on agility and systems that are iterative, design approaches that are focused on the end user.
Over time, your team will move toward the role of utility provider with a system in place to spawn products that have a tangible effect on the business at a regular cadence. Consider creating a “data product manager” role, filled by a person who has mastered the product mindset and can guide the team in the right direction.
It’s important to find ways to move in the direction of version 2.0.
How to get started? A major program may not be the right route. Technology will continue to make advances, which means that projects with long time horizons can have things change under them while in flight. Similarly, customers will change as the market and external environment impacts the business.
While some industries can readily move to private, public, hybrid, or multi cloud environments, others may not be as flexible, such as the finance and healthcare sectors, which are both closely regulated with regard to data.
Action
Focus in on a specific project that has strong business benefits. For example, develop an application that solves a specific business need, where the results are clear. Because it is data driven, it can be quantified and measured, which offers a solid proof of concept. Grow from there.
This approach has been more successful than large, complex wholesale platform development because each project is focused and completed in a short space of time.
Even in closely regulated industries, technology executives can generate extensibility and flexibility and also stay inside mandated confines and structure. Find the sweet spot, where changes can be made that enable the organization without transgressing regulatory requirements.
Governance is key.
It is not unusual for a business to be at sea regarding the types and extent of data they have. Different departments may buy the same data sets without knowing about it. The actual content of the data stores can be something of a mystery, especially if the data has been in house for a long time. Format, trustworthiness, integrity—it can be easy to lose control of data assets.
Action
Include line of business leaders and move toward accountability for data on their part. They will not become data owners overnight, but with a program in place that includes them and mandates their participation, business ownership will become a reality.
This will facilitate knowledge of assets as well as trust in the data. Identifying critical data elements and putting a process in place to track them as a way of tagging and resolving issues with data quality and accuracy.
A theme that runs through all of these insights is around organization culture. Getting to data transformation version 2.0 is likely to require culture shift. Actions that have been noted here can be part of that shift, like leveraging the advocates on the team, moving people to a data product mindset, and fostering business ownership of data.
Another approach that can be effective is the framing of “new” things in language that people understand—adages used in the business. Putting the transformative aspects of data strategy into a familiar context can smooth the way.
These action items from our discussion are a great starting point for executives looking to become insight-driven businesses.
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