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7 enterprise data strategy trends

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

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

Here’s a quick look at seven major trends that are likely to reshape your organization’s current data strategy in the days and months to come.

1. Real-time data becomes real, and so does the complexity of processing it

CIOs need to prioritize their investment strategy to cope with the growing volume of complex, real-time data flowing into the business, advises Lan Guan, head of global data and AI at Accenture.

Guan believes the ability to leverage data is non-negotiable in today’s business environment. “The unique insights derived from an organization’s data are an inherent competitive advantage of its business that is difficult for competitors to copy,” she observes. “Failing to meet these needs means being left behind and missing out on the many opportunities made possible by advances in data analytics.”

According to Guan, the next step in every organization’s data strategy should be to invest in and leverage artificial intelligence and machine learning to derive more value from their data. “Initiatives such as automated predictive maintenance on machines or workforce optimization through operational data are just a few of the many opportunities made possible by combining a successful data strategy with impactful deployment of artificial intelligence.”

2. Internal data access requests take center stage

CIOs and data leaders face increasing demand for access to internal data. “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 and marketing to HR and operations – needs access to data to make better decisions.”

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

Consequently, a growing number of IT managers are looking for data strategies that will allow them to manage the massive amounts of disparate data located in silos without introducing new risks and compliance issues. “As the need for internal data access increases, [CIOs] must also keep pace with rapidly changing regulatory and compliance measures, such as the EU’s Artificial Intelligence Act and the White House’s new plan for an AI Bill of Rights,” says Nirmal. .

3. Sharing external data becomes strategic

Sharing data between business partners is becoming much easier and much more cooperative, observes Mike Bechtel, chief foresight analyst at business consultancy Deloitte Consulting. “With the significant adoption of cloud-native data warehouses and adjacent data insight platforms, we’re starting to see some interesting use cases where companies are able to braid their data with data from counterparties to create new salable digital assets,” he said. said.

Bechtel envisions a radical change coming in the sharing of external data. “For years, people in boardrooms and server rooms have talked abstractly about the value of all this data, but the geeks among us know that the ability to monetize this data requires it to be more liquid” , he said. “Organizations can have petabytes of valuable data, but if it’s calcified in an aging on-premises warehouse, there’s not much you can do with it.”

4. Adoption of Data Fabric and Data Mesh is increasing

Data fabric and data mesh technologies can help organizations extract maximum value from all elements of a technical stack and hierarchy in a convenient and usable way. “Many companies are still using legacy solutions, old and new technologies, policies, processes, procedures or approaches, but struggle with having to mix everything into a new architecture that allows for more agility and speed” , says Paola Saibene, principal consultant at Resultant IT consulting firm.

The mesh allows an organization to pull the information and insights it needs from the environment in its current state without having to drastically change it or massively disrupt it. “This way, CIOs can take advantage of [tools] they already have it, but add a layer on top that allows them to use all these assets in a modern and fast way,” says Saibene.

Data fabric is an architecture that enables end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. The fabric, especially at the active metadata level, is important, notes Saibene. “Interoperability Agents will make everything look incredibly well-connected and intentionally designed that way,” she says. “That way you’re able to get all the information you need while avoiding having to redesign your environment.”

5. Data observability becomes business critical

Data observability extends the concept of data quality by closely monitoring data as it flows in and out of applications. According to Andy Petrella, founder of data observability provider, Kensu, and author of Fundamentals of Data Observability (O’Reilly, 2022).

A key attribute of data observability is that it acts on metadata, providing a safe way to monitor data directly in applications. When sensitive data leaves the data pipeline; it’s collected by a data observability agent, says Petrella. “With this insight, data teams can resolve data issues faster and prevent their spread, reduce maintenance costs, restore trust in data, and increase value creation from data,” he adds. -he.

Data observability is creating a whole new class of solutions, says Petrella. “CIOs must first understand the different approaches to data observation and how they differ from quality management,” he notes. They should then identify their data team’s stakeholders, as they will be responsible for adopting the observability technology.

A failure to improve data quality will likely hamper the productivity of the data team while diminishing data confidence across the data chain. “In the long term, this could push data activities into the background, impacting the organization’s competitiveness and ultimately its revenue,” says Petrella.

IT managers face growing complexity and unfathomable volumes of data spread across the technology stack, observes Gregg Ostrowski, CTO of Cisco AppDynamics. “They need to integrate an expanding set of cloud-native services with existing on-premises technologies,” he notes. “From a data strategy perspective, the biggest trend is the need for IT teams to get clear visualization and insight into their applications, regardless of domain, whether on-premises, in the cloud or in hybrid environments.”

6. “Data as a product” is starting to deliver business value

Data as a product is a concept that aims to solve real business problems through the use of combined data captured from many different sources. “This capture and analysis approach provides a new level of business intelligence that can have a real impact on bottom lines,” said Irvin Bishop, Jr., CIO at Black & Veatch, a global engineering, d supply, consulting, and construction company.

Understanding how to harvest and apply data can be a game-changer in many ways, says Bishop. He reports that Black & Veatch works with clients to develop data product roadmaps and establish relevant KPIs. “One example is how we use data within the water industry to better manage the physical health of critical infrastructure,” he notes. “The data allows our water customers to predict when equipment is likely to need replacing and what kind of environmental impact it can sustain based on past performance data.” Bishop says the approach gives participating customers more control over service reliability and their budgets.

7. Cross-functional data product teams are being created

As organizations begin to treat data as a product, it becomes necessary to build connected product teams across IT, business and data science, says Traci Gusher, chief data and of analytics at business consultancy EY Americas.

Data collection and management shouldn’t be categorized as just another project, Gusher notes. “Data should be viewed as a fully functional business domain, no different from HR or finance,” she asserts. “Shifting to a data product approach means your data will be treated like a physical product would be – developed, marketed, quality controlled, enhanced, and with clearly tracked value.”