Gartner: Data-Driven Decision-Making Never More Important
Organizations must take a concerted approach to most effectively take advantage of their data, Gartner analyst Gareth Herschel said during the advisory firm's virtual conference.
Data and analytics, the fuel for data-driven decision-making, are the two most important assets organizations possess.
Gareth Herschel, research vice president at Gartner, delivered the keynote address of the Gartner Data & Analytics Summit, where he explained how organizations need to prioritize data-driven decision-making to be successful.
That is particularly true in the current economic climate when change is happening faster than ever due to the pandemic, he continued. Making data-driven decision-making a priority, however, is not always an easy task.
Many organizations -- and their leaders -- are entrenched in their ways of thinking, and before a new way of thinking can be instilled the old ways need to be unlearned. To make data-driven decision-making the norm, to take advantage of the most precious assets organizations possess, leaders need to take three important steps, according to Herschel.
Find agents of change
Build adaptive systems that will evolve as technology advances
Find ways to extend the influence of data and analytics to all employees and embed data and analytics into every decision.
"The last year has been incredibly challenging," Herschel said. "Organizations must recognize that when so many things are changing so rapidly, they need to invest in people and systems that will help make sense of that change and respond to it. Organizations need data and analytics."
Agents of change
Finding change agents comes with the admission that organizational leaders can't develop data-driven decision-making strategies all on their own and expect everyone else to simply buy in.
Salespeople close deals, Herschel pointed out. Procurement managers buy supplies. Logistics departments deliver products. Marketers come up with promotions.
Data and analytics enable each to do their jobs faster and cheaper, and make more informed decisions. But each of the people in those roles can ignore data and analytics if they don't fit in with their previous beliefs about how to do their jobs.
To make the decision-making process truly data-driven, people need to propel the change. That means seeking out employees curious about finding new ways of doing things even if the old way still seems successful. It means finding people who are tired of dealing with a constant stream of emergencies and encouraging those who were just promoted to a new role, put in charge of a new initiative, or who perhaps were just hired.
"Real change means changing people and processes, not just technology," Herschel said. "Changing what we do, and how we do it is not easy, and it is not something we can control because we do not have control over those business units. But real leadership is about achieving change through others."
Organizations must recognize that when so many things are changing so rapidly, they need to invest in people and systems that will help make sense of that change and respond to it. Organizations need data and analytics.
In addition to moving people to drive change, organizations need to have technology in place that not only can handle their data and analytics needs in the present but be capable of evolving to aid the data-driven decision-making process as technology advances. Without adaptive technology in place, organizations will eventually get stuck.
"Now, we live in a world of necessary and continuous innovation," Herschel said. "We need technology and processes that are designed to help us keep up with the change."
Augmented analytics and machine learning (ML) are key. They enable technologies to adapt as they take in more information, and do so automatically.
Certain technologies that will enable data-driven organizations to adapt amid constant change include a data fabric that automates -- or at least assists with -- data integration, graph, generative adversarial networks (GANs) and Generative Pre-trained Transformer 3 (GPT-3).
Data fabrics are the different tools working together. Graph is a means of connecting multiple data points at once rather than just two at a time to find complex relationships. Generative adversarial networks are ML models that compete with one another to become more accurate. And GPT-3 is a natural language processing tool that develops data stories.
"We can rely on the technology to keep pace with our changing world," Herschel said. "As new sources of data become available, they are automatically integrated into the data fabric. As we need to identify new connections, graph finds them for us. Processes built on top of that graph helps improve workflow even further.
Extending the influence
While organizational policies are determined by upper management and often delivered from the top down, adopting and using data-driven decision-making is a bottom-up process.
Data and analytics, therefore, have to be available to those making decisions in the moment -- the salespeople and procurement managers, for example -- when they make their decisions.
But to get data involved in every decision takes some reengineering, both technologically and from a business process perspective, Herschel said.
Many decisions can be automated, and are in fact better when automated with AI and ML. Fraud detection is one example.
Most decisions, however, will remain in the hands of humans, and therefore organizations need to establish a strong governance framework. Enterprises also need to support decisions with data and analytics, which means embedding information that will drive decisions into the business processes in which end users do their work.
"Data and analytics is a game-changer," Herschel said. "If we get these [things] right, we will succeed as individuals, as a data and analytics practice, and as an organization because we will be aligned to the needs of our stakeholders, adaptable to a continuously evolving reality and ensuring the adoption we need to make every decision better."
Process Tempo is a Decision Intelligence Data Platform built on industry-leading graph technology. The low-code / no-code, collaborative data science, data engineering, and data analytics platform simplifies complex data environments, empowering people, processes, and technologies to work together. The secure, governed, high-performance environment delivers actionable data and insight rapidly to all stakeholders, helping to accelerate the delivery of quality, data-driven decision-making and improve business outcomes at scale. Schedule a discovery session