Data & analytics is a top priority for both small and large organizations. Consequently, a strong data & analytics is necessary for enterprises to understand their business better. Most of them move with an aim to study the data and leverage the insights. But the companies are facing the pressure and failing to leverage the full power of data & analytics. Here is an article that explains the 4 areas that prevent the success of data & analytics.
The lack of a solid data foundation
Statistics from Gartner confirm that most organizations believe data is critical. With nearly 80% of executives stating that their companies will lose competitive advantage if they do not effectively use data. Yet Gartner also found that more than half of organizations do not have a formal data governance framework and a dedicated budget.
The lack of such foundational elements can hinder organizational ambitions.
“You need to be very intentional. And if you are not being intentional, you might not see value,” says Roy Singh, a partner at Bain & Co. and a member of the firm’s Advanced Analytics and Enterprise Technology practices.
Without a fully implemented data governance program, organizations cannot expect to have sound data hygiene practices in place. Furthermore, they cannot access or integrate the data they have, as it remains locked away in departmental siloes. Besides, the company might not even know what data they need to be effective.
Data silos make things worse
“They have islands of information, and they have parts of their companies doing some of the same types of things. In contrast, others have missed the mark because they have dirty data or they’re grabbing the wrong data sets or feeding it to their dashboards incorrectly,” says Edward Matthews, an instructor at Boston University’s Metropolitan College and a senior IT security engineer at Partners HealthCare. “These companies think they have decent programs until they look at frameworks and they realize they don’t.”
Moreover, many organizations do not have the right foundational technologies to enable their objectives. Consequently, they just chase tools that might promise big dividends yet not fetching advantages for their own needs, Matthews says. Or, conversely, they stick with tools that do not enable growth because they did not devise a solid strategy. For this purpose, IT leaders can address the foundational pieces for success by creating a strategy for their data programs. This includes data governance, accountability & ownership, training requirements, strategic objectives, and leadership.
The wrong strategy of data & analytics
On the other hand, organizations should not view data & analytics as a monolithic undertaking, either. Experienced analytics leaders say they have seen CIOs go big all at once. For example, it could be either building data lakes or implementing high-priced infrastructure. They deliver their projects but then find the technology is underutilized or ignored.
Like any other tech-driven proposition, it is better to implement targeted solutions that can demonstrate value to the users. “Make sure you’re solving business problems,” says Dinanath (Dina) Kholkar, Vice President and Global Head of the Business Process Services and Analytics Units at Tata Consultancy Services.
Kholkar started his data journey by targeting an area where a data project would deliver value. This approach allowed the team to clearly define objectives and identify the necessary data and tools. In other words, this approach created manageable, achievable goals capable of generating measurable value. “That then could become a showcase for the rest of the organization,” Kholkar says.
“The business units want to see results. In addition, they don’t have the patience to wait for large transformational projects,” he adds. “They are OK not getting 100 percent results. If they are getting 60 to 70 percent results, they are fine. And, then they can get incremental improvements from there. Because when you deliver outcomes, it gets easier to get the next wave of investments. That’s very important to keep in mind.”
Similarly, experts advise an iterative approach to the analytics program rather than going in with a big bang tech project.
Iterations makes your data perfect
“It needs to be an interactive exercise where IT, business and data all work together in an agile mode. And, they should be able to run experiments and test hypotheses,” Singh says.
Brian Hopkins, Vice President and Principal Analyst at Forrester Research, refers to an example of a retailer. The company created a three-year data strategy built on adding investments every year rather than a single upfront investment. “They discovered that, like digital, once you start on an analytics program you shouldn’t stop. You invest every year in advancing your data strategy,” Hopkins adds.
Moreover, these iterative investments need to be driven by evolving business needs. They should not be impacted by new technology capabilities as they come onto the market. Organizations should build data & analytics capabilities, while incrementally expanding their data program. They should adopt more advanced tools and enable more users to tackle increasingly complex problems. “The CIO needs to think of it as an iteration, in fact, many iterations. They are going to have to continually check the data program against the market. And, they need to know what their company is trying to accomplish. Besides, they should try new tools and they should be able to upgrade for new insights,” Matthews says.
Failure to balance freedom and control
Despite big investments in data & analytics initiatives, executives say they are still falling short in reaping benefits. In its Big Data and AI Executive Survey 2020, NewVantage Partners found that 74 percent of the 70 companies polled see business adoption of big data as a continuing struggle.
Failure to recognize and respect different user needs is one factor driving that high figure, according to Singh, who sees some data leaders allowing every business unit within their organizations to pursue its own data strategy without establishing organization-wide standards—an approach that creates inefficiencies and leaves many user groups floundering without any support.
Other organizations go to the opposite extreme by centralizing everything, which prevents savvy business users from scaling up quickly and the organization from reaching the program’s full potential, Singh says.
But IT leaders who recognize the need to create a data & analytics program balanced between those two extremes—and that can adjust to users’ differing needs within their organization—are the most successful, Singh explains.
“You need a hybrid of being completely centralized and completely decentralized, and the balance between the two will change over time, maybe starting more centralized at first,” he adds. Singh points to the approach taken by one specific utility company. As its leaders invested in analytics capabilities, they recognized that the energy trading group had extensive experience with data science, so they built a self-service platform that adhered to the organization’s data governance standards and technology requirements. But they also recognized its safety division was less experienced with data & analytics, so they crafted a strategy for those users that featured more centralized support.
Shortchanging the need for culture change
Still, executives need to devise more than just a holistic data program that is aligned with strategic goals. They also need to change the culture of their organizations so that users embrace the use of real-time data-driven insights and view engagement with data as the norm.
“This is a shift in the entire business paradigm, and organizations need to plan for that change,” says Tata Consultancy’s Kholkar.
Most organizations are not doing that. According to the NewVantage Partners report, only 38 percent of surveyed companies have created a data-driven organization and only 27 percent believe they have built a data culture within their firms. Further, 91 percent said people and process challenges are their biggest barriers to becoming a data-driven organization.
Adopting to the new technologies
Nassar Nizami, Executive Vice President and CIO at Thomas Jefferson University and Jefferson Health in Philadelphia, has been maturing the data program at his institution by addressing technology needs, such as standardizing data & analytics tools and managing the data warehouse, and aligning the data program’s priorities with the organization’s overall strategy.
But he went further, driving the required cultural shifts in part through the creation of a training program called Jefferson Analytics Community (JAC). Its tagline is, “Trouble getting data? You don’t know JAC.”
“In creating JAC, our vision was to create a federated model of governed self-service analytics driven by operational owners,” Nizami says. He adds that the objectives were to increase user adoption of data & analytics tools, transition from a “data-rich” organization to a data-driven one and promote self-service data—goals meant to increase productivity and decrease turnaround time.
Fisher says other CIOs and their executive partners need to follow suit by moving their analytics program from “a great standalone effort that generates insights” into something that’s integrated in processes so that users see it as business as usual. “Users don’t know or care what all the data sources are or how cool the data science is,” Fisher says. “They need to walk into their office or punch it into their phone and get the information they need to do their job. So, it needs to look and feel like an application. That is something that the CIO uniquely understands.”
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