7 enterprise data strategy trends | 7wData

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Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices.

As with just about everything in IT, a data strategy must evolve over time to keep pace with evolving technologies, customers, markets, business needs and practices, regulations, and a virtually endless number of other priorities.

Here’s a quick rundown of seven major trends that will likely reshape your organization’s current data strategy in the days and months ahead.

CIOs should prioritize their investment strategy to cope with the growing volume of complex, real-time data that’s pouring into the enterprise, advises Lan Guan, global data and AI lead at business consulting firm Accenture.

Guan believes that having the ability to harness data is non-negotiable in today’s business environment. “Unique insights derived from an organization’s data constitute a competitive advantage that’s inherent to their business and not easily copied by competitors,” she observes. “Failing to meet these needs means getting left behind and missing out on the many opportunities made possible by advances in data analytics.”

The next step in every organization’s data strategy, Guan says, should be investing in and leveraging Artificial Intelligence and Machine Learning to unlock more value out of their data. “Initiatives such as automated predictive maintenance on machinery or workforce optimization through operational data are only a few of the many opportunities enabled by the pairing of a successful data strategy with the impactful deployment of Artificial Intelligence.”

CIOs and data leaders are facing a growing demand for internal data access. “Data is no longer just used by analysts and data scientists,” says Dinesh Nirmal, general manager of AI and automation at IBM Data. “Everyone in their organization — from sales to marketing to HR to operations — needs access to data to make better decisions.”

The downside is that providing easy access to timely, relevant data has become increasingly challenging. “Despite massive investments, the data landscape within enterprises is still overly complex, spread across multiple clouds, applications, locations, environments, and vendors,” Nirmal says.

As a result, a growing number of IT leaders are looking for data strategies that will allow them to manage the massive amounts of disparate data located in silos without introducing new risk and compliance challenges. “While the need for data access internally is rising, [CIOs] also have to keep pace with rapidly evolving regulatory and compliance measures, like the EU Artificial Intelligence Act and the newly released White House Blueprint for an AI Bill of Rights,” Nirmal says.

Data sharing between business partners is becoming far easier and much more cooperative, observes Mike Bechtel, chief futurist at business advisory firm Deloitte Consulting. “With the meaningful adoption of cloud-native data warehouses and adjacent data insights platforms, we’re starting to see interesting use cases where enterprises are able to braid their data with counterparties’ data to create altogether new, salable, digital assets,” he says.

Bechtel envisions an upcoming sea change in external data sharing. “For years, boardroom and server room folks alike have talked abstractly about the value of having all this data, but the geeks among us have known that the ability to monetize that data required it to be more liquid,” he says. “Organizations may have petabytes of interesting data, but if it’s calcified in an aging on-premises warehouse, you’re not going to be able to do much with it.”

Data fabric and data mesh technologies can help organizations squeeze the maximum value out of all the elements in a technical stack and hierarchy in a practical and usable manner.