Analytics has come a long way in the past decade and a plethora of technology companies have responded to the call beautifully. Some offering on-the-fly computational power, others with more affordable cloud solutions, and organizations embracing change. The data industry is in the midst of another paradigm shift and it’s a super exciting time for technologists. I think we can all agree that the change is anything but microscopic. It’s the exact opposite in every sense – allowing us to see clearer, faster.
As organizations shift into high gear to truly being more data-centric with self-service capabilities, the old way of managing data requires a significant shift as well.
“Enterprise Architecture is not an Information Technology issue.
Enterprise Architecture is an Enterprise issue.
Building and running systems is not Enterprise Architecture and vice versa.
Awareness tends to surface through Information Systems community.”John A. Zachman
Historically, data has been the domain for IT departments to manage, control, and protect. Infrastructure and data management grew into their specialties and worked closely within the IT organization. Now, with the explosion of data, the collaboration between IT and business is not only required but a necessity to enrich and manage the data for strategic and contextual purposes. The current buzz-terms Data Analytics, Machine Learning, AI, and Robotic Process Automation all require proper design, planning, and governance at the core data levels to be effective. Hence, a change in perspective on data management techniques is a must. Without a stable data foundation and strategy, 75% of the cost is spent on data gathering and cleansing activities – ROI reduced by ¾. The CFOs I know would balk at this.
Let’s step back and take a look at the bigger picture. Money is a critical asset. The CFO controls the finances to effectively reign in and control costs to ensure stability and longevity of the organization. They set into place controls and measures to monitor and manage the organization’s financial needs. They are empowered to make the necessary changes to meet targets and goals, have no issue saying no to anyone and everyone, and also make sure the monies are secure from embezzlement and misuse. They are the ones responsible for these things.
Data transformation is actually a data restructuring effort. With data being as valuable of an asset as money, Chief Data Officers also need to be empowered and supported by the entire organization to make changes where needed. This is the individual responsible for data security, usage, storage, and management. To be effective, they need organizational and executive support. Reigning in departments which have been accustomed to managing “their” data, either IT and/or the business, is an extremely difficult task without organizational support. Dealing with power struggles at all levels, job insecurity, and old regime mentality, makes transformations daunting for even the brightest and experienced individuals. Data is the property of the organization. The departmental thinking must shift from “my data’ to “our data.” Gartner states that 50% of experienced CDOs will fail primarily due to lack of support and backing.
The team that built the existing data environment is normally not able to see it from any other perspective except “perfect for us” with a few work-arounds. Hiring an experienced leader is another key factor for a successful transformation. Since this is a restructuring, bringing in an experienced data leader as a CDO offers quite a few benefits such as a different point of view, different experiences, and, most importantly, not being married to the existing design. Supported by an internal team, the CDO has the ability to successfully navigate a potentially tumultuous environment as well as identify areas that are working perfectly or need fine-tuning, and those that simply need to be re-architected or created. The organization benefits by gaining increased transformation momentum, organizational enthusiasm, and a solid data platform.
Finally, 3 questions to ask before embarking on a digital transformation and data centric approach are:
- Why are we doing this?
- How pain tolerant are we?
- How much are we willing to change to achieve the goal?
If the 2nd and 3rd questions create anxiety at the leadership level, invest in something else and revisit when the answer is “we are all in” – it is a transformation not a make-over. There is always the starting small approach… piecemealing things together over five to 10 years may not afford you the agility needed in today’s rapidly changing world. Equip your organization with all the tools needed to succeed in whatever decision you choose.