Part 1 in the 6-part “Data-Driven Enterprise” series, which examines the challenges, leadership requirements, measurement models, and best practices to become a data-driven enterprise
Digitalization projects are unfortunately comparable to a battlefield: There are many casualties, tired survivors, and few winners. Why is it so difficult? How is that bringing more data to an otherwise well-run enterprise causes grief, angst, and resistance in many areas of the enterprise?
The crux is that when data shifts from being a business consequence to a business driver, it disrupts just about everything in the enterprise. Data doesn’t abide by the known rules and logic used for employees, processes, and funds. You can’t fire, change, or reassign data for better returns. Data doesn’t budge but often puts unwanted spotlights on weak parts in the enterprise without mercy.
Enterprises need to accept data as a powerful change agent that will impose changes to strategy, culture, processes, and technology. Data allows enterprises to become better, faster, and leaner – or just plain digital.
Yet most enterprises are challenged; they often have two or three attempts behind them in trying to turn new data insights into action, with limited success. The mediocre results point both to technical and business-related issues. This blog series will outline both the required technical landscape paradigms to pursue, as well as the business leadership and methodology to unlock the data promise, including:
- Data insight platforms to interpret trends at scale with ease
- Business execution excellence to interact at speed with confidence
- Integration platforms to interlink experience data (X-data) and operations data (O-data) with transparency
Connecting intelligence with execution at scale
Becoming a data-driven enterprise requires both business and IT platform agility. Besides asking organizations to redesign themselves and become an intelligent enterprise, the IT platform must turn data into possible actions with near-zero delay and no human intervention. Future-proof IT platforms must codify all complexity and make operations as simple as when consumers purchase online or choose what movie to see at home.
A future-proof IT platform comes with advanced machine learning, process automation, analytics, and the ability to automate many business processes through native interoperability with business execution systems. The execution systems embed semantic models, hierarchies, cost structures, and more to ensure that any data insight, streaming event, IoT alert, and blockchain event can be interlinked with business processes.
Part 2 in this series looks at data-to-outcome challenges. Stay tuned for the 6-part “Data-Driven Enterprise” series, which examines the challenges, leadership requirements, measurement models, and best practices to become a data-driven enterprise.