The CIO Imperative: Is your technology moving fast enough to realize your ambitions?

The destination: what data centricity will mean to your enterprise

In a few short years, data and analytics will drive and predict the most important decisions, processes and interactions of the enterprise. What will this data centricity mean to the company?

Instead of siloed, restricted sets of facts, a data fabric will be integrated across the enterprise. Data expertise will expand from the IT department into business group, internal operations, and customer relationships. Employees across the entire enterprise will manage, routinely access and use the data necessary to improve their decisions and processes. This is a migration from data management to data centricity.

Mundane tasks will be automated, allowing executives and workers to focus on value. Rather than fall back on hunches and spreadsheets, your enterprise will be empowered to query, correlate and analyze in real time. Customer feedback will be instantaneous and highly accurate. 

Setting the foundation

A data strategy should clearly connect how data will inform, support and drive the realization of the organization’s short-and long-term strategic business plans, reduce threats identified in their enterprise risk management plan, and ultimately help to capitalize on top opportunities. Organizations need to empower a dedicated data team with the leadership, resources, and the executive support it needs to achieve this strategy.

A well-developed data strategy should include several key areas:

1. High priority use case identification: This helps to set the North Star goal for the data strategy, including clear expectations on data monetization (i.e., how data will create value) and informs the transformation of data as an asset. It also includes the development of a data supply, which includes data both internal to the organization as well as from external sources.
2.  A data governance plan: This outlines how data will be managed, including the policies, stewardship and operating model needed to orchestrate fit for purpose data management.
3. High level architecture that will enable the execution of use cases and governance: This should be informed by types of technology needed to integrate, transform, enable and consume data. The consumption layer is often underestimated but must be a key focus.
4. A plan to increase data literacy: This action is crucial because democratizing data across the enterprise and getting data in the hands of decision-makers at strategic and tactical levels is imperative to drive value.

Embracing new technologies to govern data and new tools that support the creation of new and repeatable insights is key to a successful data strategy. Because of the complexity of data and the disparate data sources many organizations have today, firms should plan within a time frame of three to five years, while continuously evolving the strategy. Authorize and empower a dedicated team to drive to success.

The technology: serving the transformation vision

Once you have a compelling vision for the data-centric organization, how do you use technology effectively to set up for success? Our study identified four technologies that companies across a range of industries are investing in to build the right digital foundation (Figure 2).

Data and Analytics

Data and analytics form the core – presenting the number one investment priority with 53% of respondents identifying it as their top investment priority, up from 35% in 2020. It combines with three other key technologies to form the basis for transformation.

Cloud

This is the foundational layer for data-centric organizations. Many companies have already completed an initial cloud migration, yet cloud remains a top priority for companies. In fact, the cost of infrastructure, including cloud, is seen as the top obstacle to digital transformation (according to 35% of respondents). Most companies understand the importance of the cloud – it has been in the top two priorities in the 2020 and 2022 Tech Horizon surveys. Leaders are using cloud to leverage more of the native Platform as a Service (PaaS) capabilities, taking advantage of speed and agility to innovate where they could not have done so with traditional technologies, while reducing costs.

Internet of Things (IoT)

A data-centric organization has an endless thirst for data. IoT is the mechanism to understand operational behavior patterns. Leveraging this data is leading to operational breakthroughs that drive profitability but also create new forms of value from services.

Artificial intelligence (AI) and machine learning (ML)

While data and analytics is at the core, extending this value comes from graduating beyond foundational analytics and utilizing more advanced methods of driving insights from AI or ML. AI and ML allow companies to not only rapidly react to customers’ expectations, but also to anticipate and predict them.

Transformations that exceed companies’ expectations are prioritizing more sophisticated uses of AI compared to those who are falling short. They are more focused on strategic applications such as predicting trends and creating innovative new offerings (63% vs. 45%), and more likely to include AI and ML as a top area of investment over the next two years (39% vs. 32%), compared to companies who did not meet their transformation expectations (Figure 3). The combination of these four technologies creates a symbiotic effect in performance and impact.

The destination: data that learns and evolves

For many companies, data exists as isolated bits of information. Only a fraction of structured data is used, and only a sliver of unstructured data. That which is used – quarterly financials and monthly sales reports – is often out-of-date. Siloes not only block interoperability and integration – they block insight at the enterprise level and create conflicting data. Many firms ask if they can trust the insights they generate from data because all too often they see conflicting data due to poor governance and management.

So what will the transformation of data look like?

The transformation of data

How will AI and ML change the nature of data, and how we use it?

Data will no longer remain static. AI systems, combined with ML, are transforming data so that it will learn, cleanse itself, and pull in additional data as customers and market conditions change.

“It’s happening today” according to Stephanie Nashawaty, SAP, Chief Customer Innovation Officer. “Historically, C-suite leaders found themselves in the pitfall of having data that was confined to organizational silos, with each business unit leader viewing their data as proprietary and believing that sharing insights would result in them losing organizational power. The early days of the pandemic revealed just how many businesses were hamstrung by their disparate technology stacks and siloed decision-making capabilities.”

Data will move from static to real-time across a vast array of devices, internal sources and external sources. This isn’t just making reports more current – the low latency of 5G systems and IoT will open the gates to a flood of innovations. As examples, autonomous cars used to be something in a science fiction movie and remote, technology assisted surgery was thought of as mind bending. Both are now in practice.

Finally, like cloud, AI serves as a launchpad for innovative new offerings – emerging technologies such as natural language processing, image recognition, and the recommendation and prediction engines used in today’s cutting-edge analytics. “We are finding, within Microsoft, that AI is going to be the key” says Andrew Wilson, the Chief Digital Officer at Microsoft. “It is the foundation for data that learns and evolves. It is the key to the next generation of analytics.”

The challenge: a new generation of data management

While the benefits will be profound, there will also be important challenges. 99% of companies report a significant data and technology barrier to executing their transformation. The executives surveyed cited the high cost of technology as the number one challenge (35%) to achieving transformation (Figure 4). Cost drivers include the greater scale of data, the need for higher computational power, and increased consumption commitments to cloud service providers.

While costs are rising, they are also being mitigated by greater efficiencies in the hyperconvergence and virtualization of existing infrastructure. By adopting modern data platforms and progressively decommissioning the old legacy systems, companies gain a significant cost reduction in their IT infrastructure.

The great accessibility of data centricity – by employees, suppliers, customers and others – also presents the challenge of building complex security and privacy requirements, cited as the second greatest challenge (27% of respondents). The data-centric organization must not only deepen its cyber-security offensive and defensive measures, but it must also spread the net to cover a diverse set of players.

A key operational challenge of data centricity is the complexity of connecting and integrating diverse data systems (the third greatest challenge, cited by 25% of respondents) – a key contributor to the cost of technology.

It goes beyond simple cost metrics. To develop true data centricity, it is necessary to aggregate and curate data from thousands of enterprise information systems, suppliers, customers, markets, and regulators, as well as internal control systems, IoT devices and sensor networks.

Furthermore, an increasing number of organizations are developing data strategies that offer opportunities for new revenue-generating operating models including those where they are commercializing their data. One of the emerging opportunities in this area are those where data ecosystems are created across organizations to sell curated data sets and models or insights that have been trained using an ecosystem of data.

The technology: managing the data deluge

This scale will demand a reset in the fundamentals of how data is managed. “Many companies remain in the experimental stage with their data,” says Vaibhav Jajoo, Head of Data Engineering at DoorDash. “They are not prepared for the scale of what is coming at them to convert data into actionable insights.”

Data is only valuable when it is transformed into insights and drives informed decision-making. Given the expanded user base of data, data strategists should place a strong priority on democratizing data – making it more user-friendly and accessible through a wide range of devices or through citizen developers.

Finally, there are emerging technologies that are focused on rationalizing flows of data and improving its management. Software defined storage (SDS), which decouples storage resources for the underlying hardware platform, can increase efficiency and scalability. Flash array, which is now becoming more affordable, can provide the speed and volume to manage data at scale. These are only a selection of technology-based solutions designed to enable data management.