Successfully launching the office of the chief data officer- and ensuring its sustained success

The world of Big Data, Artificial Intelligence and Machine Learning is right now in a danger zone. There are many technological silver bullets falling out of the sky for us to try out, and it is an exciting time indeed. However, for us to achieve sustainable business value with these technologies we must ensure we are giving due attention to building and maturing our Data & Analytics capabilities.
It is in this environment that the Office of the Chief Data Officer (CDO) plays an increasingly important role. Practically speaking, the CDO is responsible for accelerating enterprise innovation and transformation through strategic management and use of data and analytics.
In 2015, there were just a few hundred Chief Data Officers globally. Since then the number of CDOs has risen into the thousands. The role is still mostly nascent, and it continues to evolve with only 20-30% of CDOs successfully establishing a data culture in their organizations, Bean (2019).
How does a Chief Data Officer effectively establish a data culture in an organization, enabling ongoing business value to be derived from its data assets and analytical insights? What is needed is an Integrated Capability Framework, bringing together People, Process, Architecture and Technology, and embracing an end-to-end vision for Data & Analytics. Gaps in this Framework, if left unattended, will certainly undermine your ability to derive ongoing value from your Big Data and AI/ML investments.
The Gavroshe 7 Streams Framework™ provides a comprehensive approach to ensure effective, long lasting data management solutions aligned with strategic needs. Woven through this framework is consideration of the people, process, technology and data needed by companies and agencies to leverage their data as an asset and reach their business goals faster. We have found that successful CDOs focus on these 7 Streams of Strategic Data Management activities, simultaneously:

• Data Governance – establishing the Data Governance Council, Data Policy and the Data Stewardship process
• Data Architecture – establishing a Data Reference Architecture and the Data Modeling process
• Data Asset Development – iteratively plan, design, develop and deliver enterprise-class Data Assets
• Data Quality – profile, map and cleanse Critical Data Elements
• Data Context – develop a Business Glossary and Data Lineage
• Data Analytics – support implementation of Business Intelligence and Advanced Analytics toolsets and enable Data Science
• Data Infrastructure – manage the Information Life Cycle of Corporate Data Assets and manage Data & Analytics Platforms to cater for SMAC (Social, Mobile, Analytics and Cloud)

There are 3 key issues that demand the attention of most CDOs in today’s environment:

1. Managing the Organizational Culture Change

Schein (2017) thinks about organizational culture (‘OC’) at 3 levels: (1) company practices and behaviors, (2) explicit statements of values and beliefs, (3) deeply held, basic assumptions and beliefs. The third level is where the true essence of OC resides.
The cultural milieu of an organization is a complex multi-layered phenomenon, often guided by shared beliefs and assumptions, which in turn influence core values, which in turn drive behavior patterns (e.g. perception, thinking, feeling and doing). In some organizations aspects of the culture are explicitly codified; in others they are unwritten and lurking below the surface.
An organization’s culture is often too complex to attempt to change at the macro level. It is better to focus on a specific organizational problem and to make adjustments to its associated cultural strands.
The CDO needs to lead the organizational change, paying specific attention to the following:

• In the Business Community – Data Governance, Analytics, Data Science, AI/ML and Data Ethics
• In the Technology Community – Agile Design/Build, Cloud Computing and other modern platforms, and balancing Data Security needs with ease of Data Access

2. Data Privacy & Security

The CDO collaborates with the Chief Privacy Officer and the Chief Information Security Officer on these matters. A good example is General Data Protection Regulation (GDPR), which gives control of personal information back to its individual owners: requiring businesses to capture, control and protect data under strict guidelines that impose hefty fines for non-compliance. The May 25, 2018 deadline for GDPR may have come and gone, but the compliance journey is only just beginning as firms plan more robust and sustainable solutions that can support a cost-efficient response to data subject access requests and accommodate regulatory change over the long term.

3. Data Ethics (especially in the light of AI/ML)

This is an increasingly important focus area for the CDO. “Data Ethics refers to systemizing, defending, and recommending concepts of right and wrong conduct in relation to data, in particular personal data.” Kitchin (2014).
The CDO must work with the organization’s stakeholders to resolve the following key issues:

• Ownership – individuals own their own data;
• Transaction Transparency – if an individual’s personal data is used, they should have transparent access to the algorithm design used to generate aggregate data sets;
• Consent – if an individual or legal entity would like to use personal data, one needs informed and explicitly expressed consent of what personal data moves to whom, when, and for what purpose from the owner of the data;
• Privacy – if data transactions occur all reasonable effort needs to be made to preserve privacy;
• Currency – individuals should be aware of financial transactions resulting from the use of their personal data and the scale of these transactions;
• Openness – aggregate data sets should be freely available;
• Algorithm Transparency – inclusiveness/exclusiveness of certain sectors of the population based on use of algorithms.

IN SEARCH OF A NEW MODEL
Research has been done in the past, and several models have been developed, to help guide companies to become data-driven and to establish a data culture. However, most of these studies seem to have focused on strategy, with very little in-depth attention being given to culture and organizational change management.
The savvy CDO is looking for a comprehensive framework that can be used as a thinking tool for mapping out the organization’s transformative journey to becoming a data-driven enterprise. Such a framework must be focused on business value and must include an incremental approach to organizational change management and OC, taking into account data privacy, security and ethics.