7 enterprise data strategy trends

Today’s CIOs not only understand the value of data but also that actionable business intelligence, fueled by high-quality data, leads to better business decisions and more efficient operations.

Yet data collection is far from a static process. Gathering accurate, actionable data requires careful planning, access to relevant sources, and strong yet flexible management capabilities. To stay on top the latest information management approaches and techniques, check out these leading data strategy trends.

1. AI transforms the data value proposition

Artificial intelligence (AI) represents today’s most pivotal data strategy trend, given its profound transformative potential, says Craig Muir, a partner at investment bank Solomon Partners, serving as the firm’s software, data, and analytics leader.

AI gives organizations the ability to unlock insights from extensive datasets, facilitating informed decision-making and cultivating innovation, Muir says. “Its automation capabilities can streamline operations, thereby enhancing resource allocation and efficiency.”

Muir notes that AI also opens new opportunities for enterprises that don’t currently sell data as a primary product. “An example of this is the recent Google/Reddit licensing deal,” he says. The $60 million deal will allow the search giant to train AI models on human Reddit posts. “An enterprise’s data strategy should prioritize investing in and harnessing the power of AI, either internally or through external commercialization.”

Muir believes that failing to proactively engage with AI both internally and externally would “be a missed opportunity at best, and potentially a Kodak-like extinction event at worst.”

2. Data democratization gains momentum

Data democratization is playing an increasingly crucial role in forward-looking enterprises, making data accessible and usable to everyone, not just data scientists and other experts. “By allowing for different perspectives, it can help round-out conclusions and foster collaboration across the organization,” says Portia Crowe, Accenture Federal Services’ chief data strategist for defense and applied intelligence.

Democratization works by breaking down silos. “It enables a data-driven culture by providing access to the data and tools that encourage usability,” Crowe explains. “The rise of user-friendly self-service tools empowers users with minimal expertise to explore and analyze data.”

3. Data quality takes center stage

Data quality encompasses accuracy, completeness, consistency, validity, and timeliness — and it’s becoming a chief IT concern as AI and other data-driven initiatives take hold in the enteprise.

“Overall, fit-for-purpose data, as well as trust in the data, is essential for an organization and should be well-governed,” Crowe says. “Data quality also acts as the fuel for sound use of artificial intelligence, as it directly impacts its ability to perform and generate reliable results.”

As enterprises grow ever-more data-driven, the need for high-quality data to fuel decision-making will only intensify, Crowe predicts. “So-called ‘dirty data’ leads to poor choices and hinders an organization’s ability to compete and perform effectively,” she warns. “Inaccurate or biased data leads to flawed AI outputs, highlighting the need for clean, reliable data.”

While the specifics of how data quality is implemented may evolve over time, the core principles behind them are likely to remain important for organizations in the long run.

4. Data strategies shift direction

There’s a growing directional shift in the way some enterprises implement internal data-driven initiatives, says Jayaprakash Nair, head of analytics at Altimetrik, a data and digital engineering company.

Nair observes that traditional data strategy is primarily driven from left to right. This means that data collected from various sources is funneled into a single location, such as a data lake or warehouse, and then cleaned to create a single source of truth (SSOT). While this approach is generally successful, he notes that some organizations struggle with the time required to build an SSOT, as well as how to get the most value out it.

Going forward, the data strategy direction will increasingly follow a right to left approach, Nair predicts. Using this model, the business team will define the priorities they need solved using available data.

“The IT team will collect and clean out enough data to meet those business priorities, thereby generating business value in a relatively short span of months or even weeks,” he says. “The business team will then define the next set of priorities.”

Nair believes that a right-to-left approach will build the SSOT organically over a period of time, generating tangible business value along the way.

5. Rethinking data strategies from the ground up

The most important data strategy trend today is revisiting the enterprise’s current plan or creating a new one, says Stephen Bailey, director at security consulting firm NCC Group.

To squeeze the greatest value out of rapidly growing data pools, Bailey says that enterprises need to take a focused approach that includes all business areas. “Whether it’s for improving internal processes and procedures or understanding more about clients and customers, a good data strategy defines governance, ownership, and desired outcomes for all data,” he says.

A data strategy reassessment should begin by defining and agreeing on the need for doing so, and then ensuring that the initiative gets top-level support. The next step should be appointing a project lead along with a steering group that includes representatives from all relevant business areas. “Align the data strategy and your AI governance framework to reduce any conflicts between them,” Bailey recommends.

Bailey notes that new requirements created by AI-related legislation will likely result in many enterprises without a formal data management strategy, creating bits of a plan without developing a comprehensive policy.

6. Data heads to the edge

As enterprises grow increasingly data-driven, edge computing provides real-time data analysis, reducing the latency issues that are commonly associated with cloud computing, says Javier Muniz, CTO at Colorado-based law firm LLC Attorney.

The significance of this trend lies in its potential to revolutionize how data is handled, processed, and delivered, Muniz says. By turning to the edge, enterprises can analyze data closer to the source, enhancing efficiency and allowing quicker insights. “This holds particularly important implications for industries such as manufacturing, where real-time data analysis can streamline processes and improve decision-making.”

To maximize benefits, enterprises should consider implementing a comprehensive edge strategy, identifying the key areas where data latency most affects operations. “Engaging strategic partners with expertise in edge-computing infrastructure and architecture is also invaluable,” Muniz adds.

Communicating the edge’s value to management colleagues requires a clear illustration of its potential benefits and efficiencies, Muniz says. “It’s crucial to convey how moving data analytics closer to the source can drive better results, expedite decision-making processes, and ultimately boost the bottom line.”

7. Data-as-a-service rises

Data-as-a-service (DaaS) is a pivotal trend in enterprise data management, offering on-demand access to data, an attribute that’s becoming increasingly essential for global enterprises, says Gloria Flynt, a senior research analyst with Straits Research. “Its cost-effectiveness lies in eliminating the need for on-premise infrastructure, thus reducing capital and operational expenses.”

DaaS’s agility enables adopters to swiftly integrate new data sources, adapting to market changes with little or no delay. “Additionally, DaaS enables the easy monetization of data, creating new revenue streams,” Flynt says. “It also ensures high data quality by standardizing data across business units and enhances analytics and business intelligence by streamlining data virtualization and automation, leading to better decision-making.”

To take full advantage of DaaS, Flynt advises enterprises to integrate the approach into their existing data ecosystems, ensuring seamless access to external data sources. “This integration can also enhance analytics, improve decision-making, and drive innovation.”

Flynt says that IT leaders can communicate DaaS’s significance to management colleagues by highlighting its strategic value and alignment with business goals. “They can further emphasize the agility it offers, allowing the enterprise to rapidly integrate new data sources and adapt to market changes.” She notes that this approach not only conveys DaaS’s practical benefits, but also frames the service as a forward-thinking investment that supports long-term growth and innovation.

Given the growing volumes of data enterprises must handle and the need for flexible data access, Flynt believes that DaaS is likely to attract a deep customer base. “Its alignment with cloud computing and the increasing demand for real-time data analytics suggest that DaaS will continue to be relevant for the foreseeable future.”

A final thought

Enterprises hold more data than ever before — first and third party, structured and unstructured — via the cloud, says Murli Buluswar, Citi’s analytics head for US personal banking. “Advanced companies will set themselves apart by becoming more sophisticated in their ability to store, query, derive, and stitch together insights at scale from these data.”