Data Leadership role in Digital Transformation
By Prakash Kewalramani, Sr. Director of Data Governance, WWE
The rapid and widespread development of digital services has been at the heart of the digital transformation that has influenced our lives. Many new ways to communicate, engage, shop, or access information online have appeared and they are constantly evolving. We need to ensure that the value of the data evolves with these digital services.
Digital transformation is imperative for all businesses, from small, medium to large organizations. Is it just one of the buzz words to show companies are moving to the cloud, or are all their internal and external systems are integrated? Some leaders feel that the term “digital transformation” is being used widely and have lost its real purpose. There are multiple examples of the digital transformation programs, moving the legacy systems to the cloud or replacing the existing critical business application with the latest technology. Most of the companies already have data driven processes even if they are using the legacy systems because the data exists in their legacy systems as well. When we say we are moving towards data driven culture, what exactly do we mean? Let us find out in the following session.
Data has taken a different shape and has become the most important element in many of the industries and in our day-to-day activities. We have been hearing that the data is a new oil. In my opinion, data is better than oil.
If you are leading innovation, it’s going to be challenging. Even the probability of success may not be very high depending on the magnitude or the radicality of the innovation that leaders will be leading. One of the misconceptions about leading an digital transformation program is that as a leader, you see this new idea, and it is one of the obvious things to assume that the systems are going to go along with this new product innovation. Therefore, it is very critical to move people and the systems in new directions. The success behind the digital transformation program can be related to leadership transformation. Many roles are required in running a digital transformation program. Some of the roles are program sponsor, program manager, enterprise architect, data engineer, data scientist, data analyst, data steward, data owner, subject matter experts, etc. It is the responsibility of the data leaders to bring the right value from the data stored in our systems and articulate the data patterns to senior leadership so they can make the right business decision. Data and its metadata play a critical role in the success of the digital transformation program. To transform the company into data driven organization, it is important for data leaders to understand the characteristics of the data and align the data strategies with the corporate business goals and objectives.
Data has taken a different shape and has become the most important element in many of the industries and in our day-to-day activities. We have been hearing that the data is a new oil. In my opinion, data is better than oil. Why? Once the oil is used, it cannot be re-used for the same purpose. For example, we need to flush the used oil from our vehicle to replenish it. In the case of data, it can be refined and then consumed in a effective and efficient way. We can merge the current data with the historical data and predict new data patterns, there can be many different perspectives of the data and its outcome when data moves from one point to another and from one data owner to another data owner.
Data Governance provides a good set of tools and techniques to execute the successful digital transformation program. It talks about people, processes, and technologies. However, there is a fourth component to this framework: DATA itself. We often focus on people when we talk about organization organizational structure change, cultural change, emotions, and how this change can affect the overall delivery of the program. We create numerous processes, policies, guidelines, and procedures that can bring discipline, standardization, and structure to our day-to-day activities & results. We invest in technologies to speed up the delivery to make it more efficient and effective. We do not think much about the data and specially the metadata, which is the most important ingredient in digital transformation.
Most attention is given to the application and its interfaces that include including various APIs, micro services, third-party shared services, architecture, business process, etc. These building blocks are very important, but the underlying data that is used by the business to make business decision is equally important. It’s important to involve business from day one and ask them what problem they are facing that will be resolved by the digital transformation program? The health of the data drives the actual transformation engine that produces the value out of data and measures the business outcome that directly affects revenue, cost, and risk.
Initial phase of any digital transformation practice after defining the vision & mission statements is to perform a gap assessment. This blueprint will guide us to where we are today. What is the transitional plan? What is our target state? This framework can provide a simple vicious cycle of MIA (Measure, Improvement scope & Action). Measurement process will help us understand the baseline and the complexities surrounding the current state. Improvement scope will list all actions that needs to be taken to achieve the target state. Implement the optimal action determined by the previous step and prepare for the next measurement. Any improvement towards the digital transformation should drive the data value and business outcome.
There are several frameworks available to implement digital transformation programs. I usually use the hybrid approach, picking & choose the areas from different frameworks, and customizing as per the corporate data strategies & objectives. Data Value Framework (DVF) is one of the kinds that take the hybrid approach and includes Discover, Measure, Action & Outcome. Below is the Data Value Framework that showcases the building blocks for any integration, migration, or transformation program.
| Discover | Measure | Action | Outcome |
| Data Classification | Vision & Mission | Data Catalog | Machine Learning & Artificial Intelligence |
| Data Architecture | Program Assessment | System Integration | DataGovOps |
| Data Lineage | Impact Analysis | Data Modeling | Metrics & KPIs |
| Data Security/Privacy | Non-Intrusive Approach | Access & Reliability | Compliance |
| Data Quality | Feedback Loop | Visualizations | Predictive Analysis |
| Data Products | Awareness & Engagement | Emerging Technologies | Data Monetization |
DVF consists of 4 phases and 30 focus areas across this framework. I suggest using this framework either by going horizontally or vertically, depending on the existing state of your ecosystem or enterprise information architecture. We can pick any outcome from DVF and start from the discovery phase. For example, if your goal is to define Metrics & KPIs, we can pick any focus areas that are relevant to the organization; we do not need all 30-focus areas to deliver Metrics & KPIs.
Conclusion For every data driven program, leadership is critical for its success, and if the leadership is leading to a business outcome that depends on the data value, the impact seems to be obvious. Data Leadership allows organizations to establish the right vision to implement any data management program, baselines, benchmarks, and goals to keep moving forward. Because data allows us to measure, we will be able to establish baselines, find benchmarks and set performance goals.