Chief Data Officer: the powerhouse behind customer data optimization
Many organizations have realized the need to have a proper data analytics structure in place to manage their data. The initial driver has been the ability to cope with specific governance and compliance issues by establishing a clear inventory of different data sets and laying out the underlying rules related to this data (e.g. retention policies).
This has been the first mission for newly appointed Chief Data Officers (CDOs) who often also take on various responsibilities usually assigned to Chief Analytics Officers. So, what are their two main objectives?
- Definition of clear data governance to better manage enterprise data life cycles
- Implementation of robust operating models to ensure availability and usability of enterprise data
While today’s CDOs work in close collaboration with CIOs who help them put in place the right data-driven technologies (e.g. data lakes, data platforms, cloud services, artificial intelligence and machine learning) for processing the data, they are still pretty ‘disconnected’ from other CxOs and business functions.
Limiting the role of a CDO to those two primary goals will prevent organizations from getting the maximum out of their data. That’s why a third objective must be added to their mission list:
- Introduction of an innovation mindset across the enterprise by showcasing the data analytics dimension.
CDOs should not report directly to CIOs (as is mostly the case nowadays), but should sit on the executive committee. This way, they will be empowered to reach out to business leaders to showcase the value of data and information resources held within the organization to drive business outcomes. This allows leaders to make business decisions that are not based just on gut feeling, but on factual data. Business managers will gain new insights into trends and customer behaviors that they wouldn’t have had otherwise.
Change management is crucial
Shifting towards a data-driven organization requires a cultural change. As we usually see for important and instrumental technology projects, introducing new tools and processes requires adequate organizational change management. This is also applicable to data analytics which should be viewed as a new tool for enabling business opportunities. While organizations are starting to use advanced analytics, they are still struggling with successfully implementing solutions in their day-to-day business. This could partly be explained by the communication gap between data experts (e.g. data scientists) and business users.
So, how can we address this gap? First of all, by educating business users on the concept of data as a source for providing insights into how to improve their work and, at the same time, by educating data experts on the business implications of analytics. An alternative would be to form interdisciplinary teams (biztech) to create so-called hybrid profiles (people with literacy in both areas).
KPMG’s global Behavioral Change Management (BCM) methodology provides a step-by-step process to support organizations looking to become more data literate.
Getting data on the balance sheet
As several analysts have pointed out, data-savvy companies with an enterprise data function and data science professionals on board currently have a book value twice the market average.
Essentially, organizations should value their data the same way they value their real estate assets, IT assets, software licenses or any other equipment.
If they do, they can expect two additional benefits:
- Stronger foundations to monetize the information they have. In the event of a future merger or spin-off, having good visibility on data as an asset will be another element used in the valuation of the entity and its brand (in addition to traditional criteria such as EBITDA, client base, revenue growth etc.).
- The ability to shape a strategy and secure investment to protect these assets when it comes to risks such as data leakage or cyber attacks. Business leaders will also recognize the value of these data assets when getting insurance to protect them against the same risks.
This is the new concept behind ‘infonomics’ (a new discipline introduced by Gartner) which measures and monetizes company information as an asset.
The bottom line
Data, analytics, intelligent automation and artificial intelligence have become more mature and play a fundamental role in the current age of digital transformation. At the same time, the creation of a data-driven culture will not only drive new business outcomes, but will also require behavior change management across all functions to ensure everyone is aligned on future data-driven initiatives. As highlighted by other KPMG experts, this is the backbone of new reality.
This article has been written by Philippe Bovy.
