Conquering the last frontier of digital transformation
As I talk to my clients in organizations of every size and industry, I sense a generational shift in both their technology and business strategy in the area of advanced analytics. I define advanced analytics as the exploitation of an organization’s data assets through sophisticated data science tools and techniques performed by data scientists. Digging further, we can see that this isn’t traditional business intelligence and reporting using legacy and modern reporting tools (such as QlikView, Tableau, and Power BI.) No, this sort of analytics is often ad hoc, using bespoke combinations of tools, libraries, and analytical techniques against many types of data types and sources.
Many organizations are using advanced analytics now because they have completed the first few phases of their digital transformation projects and are moving on to the last frontier – tackling the data and analytics systems and processes to fully transform.
What exactly does that mean, though? It means that the analog-to-digital transformations are complete. It also means that the traditional IT environments have been transformed to be more efficient and services-driven and applications are now using cloud technologies and operating models. That leaves the data and analytics components where value can still be extracted and exploited. The question we must ask is, “How do we bring those learnings from application modernization, tooling from the DevOps processes, and operating models from the cloud to the data and analytics estate?” The answer lies in lessons learned from existing application modernization efforts.
Lessons learned from the first phase of transformation
- Application modernization
Application modernization includes new software development methodologies, tools, and processes coupled with a change in organizational structures and processes to be software driven. New programming languages have emerged to make writing, testing, and deploying code more assessable to software teams. That has allowed the lines of business within organizations to better understand software development and align more closely with it; this enables better integration with traditional IT, letting them become technology-driven business units. Those changes didn’t happen over-night – but when completed, I have seen improvements that are orders of magnitude more efficient and impactful than previous technology deployments.
- DevOps processes
Writing better code using public cloud tooling is only part of what has made the recent digital transformations effective. DevOps has accelerated these transformations, which have been instrumental in breaking down the barriers between application development and IT operations. With that problem solved, organizations were able to truly start using IT as a force-multiplier additive to their digital transformation. Organizations that have a “DevOps mentality” are poised for success in the next phase of their transformation. - Operating models
The public cloud has transformed the operating models of many organizations in many ways. From the way IT departments extend their own capabilities through hybrid-cloud initiatives to the way application developers use cloud-native services and functions – organizations have continued to increase their business velocity by embracing cloud principles and operating models. OpEx vs. CapEx, self-service, on-demand provisioning, elastic scaling, micro-charging, and bespoke provisioning of resources are all game-changing practices that have transformed the way organizations treat technology.
Posted from:
https://www.cio.com/article/3615696/data-and-analytics-the-next-phase-of-digital-transformation.html