Is your technology moving fast enough to realize your ambitions?

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?

Data centricity is the use of data as a shared asset to create intelligence and insights for customers and stakeholders as well as continuously improve decisions, processes, products and services. Only 16% of APAC organizations say that they are data-centric today, although that percentage appears to be increasing.

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.

Case study: using IoT to start a new line of business

Climate controllers have traditionally been stand-alone, disconnected units on top of office towers. A leading European manufacturer set out to digitize its entire fleet, building an Internet of Things (IoT) structure that didn’t just change the product line, but also the company.

Using cloud-based apps, hundreds of units were connected to transmit real-time feeds of fuel use, climate conditions, customer behavior and carbon dioxide (CO2) emissions. Highly accurate product data was generated on maintenance, faults and depreciation. The data streams were aggregated up to a real-time diagnostic of the products and customers. Finally, the IoT data was so valuable that the company started an energy management business, earning revenues from energy savings and the reduced emissions of its customers.

Employees will make more informed decisions on the spot — a customer better analyzed or a supply chain problem unraveled. These tactical decisions will aggregate up into insightful predictive analytics — a more accurate market entry plan or a complete supply chain overhaul. Data will no longer look backward — it will predict the future.

That is the data-centric organization, and it is not far away.

Not a step forward, but a leap upward 

How does the enterprise get there?

The first stage is adoption of a visionary core data strategy — a transformation plan that sets a bold vision of the enterprise’s future. This is not about a single functional responsibility. It is about a growth mindset of technology and business leaders, placing data as a critical asset that can be a differentiator for an enterprise. This is built on a foundation of relevant data skills, both domain-specific and tech-specific.

This is a time to be bold — 55% of APAC respondents in the EY study whose transformations are exceeding expectations are committed to creating new products and services or building new experiences to meet changing customer needs (so-called “greenfield” solutions). “In the past, I would consider our company to be risk-averse,” said Sempra Infrastructure’s CIO, Chantale Rondeau. “Now, there’s been a mindset change, and there’s more of an appetite to explore innovative technology.”

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

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? The EY study identified four technologies — data and analytics, cloud, IoT, and artificial intelligence and machine learning — that companies are investing in to build the right digital foundation

While the most successful digital transformations will continue to have humans at the center, it is their relationship with the technology that will change.

A major utility faced two challenges — it needed to digitize its field networks, and it was losing key, experienced personnel.

Part of the answer was an aggressive digital upskilling for workers. Field personnel — line crew, inspectors and sub-unit mangers — were empowered with data and personal technology to use data for decisions. They became experts on broader fields of energy transmission. They moved from being repairers to digital experts, from skills to a career.

First, digitization of workflows will automate repetitive and low-value tasks, enabling workers to take on more strategic roles. In these data-centric organizations, the ranks of knowledge workers will expand across the organization. Dispatchers on loading docks, manufacturing-production crews, salespeople in front of clients and office workers at their desks will become both data users and data engineers creatively applying data to improve their specific domains. Robust change management programs will be required to drive these changes in employees.

Data accessibility will enable more fluid governance, but it comes with greater responsibility. This will drive a gradual migration from rigid command and control structures to local decisions — closer to the work and closer to the customer.

“Data will be the bread and butter of our organization moving forward,” said Mondelez’s VP Data Management, RD and CIO MEU, Rossana Rizzotto. “The ability to interpret data will be essential to careers, whether that’s on the business side or as a data scientist.”

The horizons will extend to customers as well. Through data flows, customers will be able to give input on product designs and production orders. Internal stakeholders will also be able to better understand customer preferences through real-time feeds on purchasing preferences, pricing and post-sale customer service, creating a personalized experience.

Finally, effective digitization can have a positive effect on employee morale and retention. According to the EY Work Reimagined Survey 2021, 64% of global employees want to see better tech in the office, and 48% want to see increased investment in their home technology. As noted above, over half (54%) will leave their jobs if they do not have the flexibility that technology can give them.

Building the digital talent that enables data centricity 

The defining constraint on the next generation of data and analytics is the skills shortage — both within the technology function and increasingly across the entire workforce. The challenge is compounded by current economic conditions driving the great resignation. Never has it been so important to keep employees — particularly the digitally skilled — engaged and fulfilled. 

Forward-looking companies understand the dilemma. The EY study found that 70% of APAC companies are focusing on alleviating technology skill gaps of existing employees. Companies are rethinking their employees’ career paths and roles to fit a more digitized world, blending both business and technology skills.

Technology organizations have long struggled to fill their requirements for digitally capable workers. But as data centricity increases skill requirements across the entire business, chief operating officers, chief marketing officers and other non-IT executives face a special challenge — sourcing the digitally proficient who also understand the business.  Examples include a warehouse operator who can structure inventory regressions or an aircraft engineer who can design using a digital twin.

Bridging the talent gap

Data-centric organizations will recognize that there is no single answer to their talent shortages. In the EY study, APAC executives pointed out the challenges of high compensation requirements (28%), successful upskilling (27%) and the struggle to retain existing skilled talent (26%) as leading barriers to acquiring digital skills . No one answer will suffice.

The EY study also shows that companies are emphasizing more on upskilling of existing employees. Some key actions that APAC companies are undertaking include assessing skill gaps (31%), providing mandatory training programs (26%) and offering incentives to encourage learning of desired skills (24%) 

Therefore, enterprises should adopt a talent ecosystem approach — a portfolio of hiring new recruits, upskilling employees, adopting third-party solutions, automation and gig workers — to meet their needs. Technology will play a role in supporting all these solutions.

Automation of routine and repetitive processes can increase productivity and worker engagement. It frees workers from mundane tasks to concentrate on more strategic and rewarding tasks. It also expands the data generated in enterprise operations with inheritable high quality.

Cloud-connected managed services are now being designed to focus on disciplines with high skill gaps, such as cybersecurity or consumer data regulatory compliance. Used selectively, they can address some of the most critical digital skill gaps faced by the enterprise.

As the requirement for digital skills expands across the workforce, the key criteria for new technology deployments will be ease of use and self-service by the user. This is increasing the attractiveness of software as a service and third-party support, which are getting more user-friendly.

Finally, given the structural talent shortage and need to provide digital and data skills, upskilling and retention will be at the center of a data-centric workforce. Seventy percent (70%) of the APAC executives in the EY study said that they are focusing on re-skilling rather than hiring, up from 46% in 2020. Whether this is as a data engineer or back-office worker, re-skilling is becoming a key tool in future-proofing the workforce.

Build your enterprise’s vision of a data-centric future 

This is an end state in which data is integrated into every major decision and process. This is not just the chief information officer’s responsibility, but also the job of the entire leadership team. It presents a high-level blueprint of data flows and usages. It is an imperative for how data and analytics will support the strategic vision of the firm. It captures the size of the prize. It serves as your business case.

Build from the use cases 

With the data-centric vision as the goal and aligning to your corporate strategy, explore use cases that create value while moving you toward data centricity. Put it on a sprint timeline and be prepared to scale fast. Establish an embedded and empowered team that blends business and IT skills. Give the team the mission, leadership and support it will need to disrupt and transform. 

Map the gap: architect and build the technology foundation 

Look for gaps in your technology based on what is needed to enable your use cases. Assess your stack today — the infrastructure, cloud, AI, data and analytics — and where each of those components need to be in the future.

While today’s goal is to democratize data value within your company, tomorrow’s goal is to monetize data within the ecosystem. If you do not lay the foundations for this now, you will be reactive rather than proactive tomorrow. Understand the capabilities needed to deliver on strategic objectives in the near and long term as well as how they will be prioritized and deployed to achieve the strategic vision. Be aware that shortfalls will be driven by the sheer volume of data, integration requirements and the early stage of critical investments such as AI or ML.

Obsess on the outcomes — especially the customer

You will not have the resources or talent to address all facets of data centricity. If you have to concentrate resources on one area, make it the customer. Build data centricity into your customer relationships, and that will provide the strong foundation to build from there. Additionally, obsess over the outcomes being driven from your data and adjust your strategy and actions accordingly.

Put humans at the center

A clear finding in the EY study is that those organizations that put the human element squarely in the center of transformation are those that succeed. In data centricity, this is highlighted in the need for leadership, the spread of data literacy and the digital mobilization of the broader workforce. Combined with the right technology strategy, the right talent strategy is the key to the new generation of data centricity.

Summary 

We are entering a new era of data centricity — where every decision, process and interface will be driven by data. This transformation is being built on a new technology infrastructure of cloud, artificial intelligence, Internet of Things and other technologies that build a pervasive data fabric across the enterprise. While technology is at the core of this transformation, humans are at its center — a more empowered workforce that uses and advances the data centricity of the enterprise.