Down-to-earth offshoots of artificial intelligence are increasingly accessible for digital transformation work. These projects tapped into machine learning – with existing talent.
How CIOs can encourage data-driven mindset
[Editor's note: Seth Earley, CEO of Earley Information Science, will speak on the "Putting AI to Work" panel at the upcoming MIT Sloan CIO Symposium on May 24 in Cambridge, MA.]
Among the pain points CIOs grapple with today, shiny object syndrome and data complexity loom large. Last week we explored how to fight shiny object syndrome, so let’s dive into how CIOs can encourage a data-driven business mindset and digital agility.
New digital technologies, especially those related to the customer experience, require revamped or new supporting processes. A 360-degree understanding of the customer – the holy grail for many digital business leaders – requires that data moves through value chains as seamlessly as possible.
Customer lifecycle management, for instance, is cross-departmental and cross-functional, cutting through many processes and applications. Dashboards need to tell the organization how to remediate an out-of-bounds indicator, such as high bounce rates from an e-commerce site after a product launch, or large numbers of returns after a product promotion.
By monitoring the effectiveness of each step of the customer journey, the health of upstream systems and supporting functions can be measured continually. The IT organization can provide infrastructure and instrumentation; however, the business side needs to drive the development of metrics and KPIs as well as a remediation playbook, based on its knowledge of customer experience.
Who is responsible for quality data?
That experience is dependent upon complete, consistent, and high-quality data. While the CIO and IT organization are seen as the providers of customer experience data, that is not actually where responsibility belongs. Just as the business owns its processes, it also has to be responsible for the data that both fuels those processes and creates exhaust from the machinery of those processes.
IT needs to educate the business units about how to own their data, and should also provide the business the correct initial design, tooling, instrumentation, and infrastructure. IT also needs to help develop the playbooks that allow business functions to optimize data-driven processes. But data quality, consistency, completeness, and provenance need to be owned and managed by the business.
Cultural change ahead
Encouraging the organization to develop a data-driven business mindset also poses a significant cultural challenge. This mindset begins at the design and capability development phase. It requires framing digital capabilities in terms of end-to-end processes and experiences, rather than focusing on point solutions.
An agile business needs to think digitally – driving decisions with data, by testing products, services, and offerings, finding what does not work quickly (failing fast), and rapidly iterating to improve the customer experience.
However, most organizations are weighed down by legacy tools and applications that were designed and deployed without adaptability in mind. Problems were considered from a limited span of processes – usually owned by a siloed function. Multiple cycles of development with this line of thinking incurred significant technological debt and required dealing with incompatible architectures and inconsistent data structures. Agile, iterative evolution of offerings, products, and processes under these circumstances is not possible. Adding new capabilities requires significant time and cost.
Making up for data sins
It is possible to remediate some of these challenges without a massive, risky, rip-and-replace approach. Data virtualization, ontology-driven integration layers, semantic search/unified information access tools, and back-end robotic process automation (RPA) are all viable approaches to help organizations make up for their past digital sins. A good starting point is to inventory data sources, develop ownership policies, establish quality measures, map trust dependencies, and create consistent reference architectures. As clean, consistent, quality data is the fuel for the organization’s applications, the role of a Chief Data Officer may also prove to be a valuable catalyst to kickstart the necessary process and culture change.