3 Steps for Establishing Your Chief Data Office the Right Way
You can avoid common challenges associated with creating a C-level data office by partnering with both IT and business leaders on three critical tasks.
- By Suresh Venkateswaran
- October 17, 2017
Formalizing an organization's data management under a chief data officer (CDO) has become a top priority for businesses to generate value and gain competitive advantage. Although compliance regulations already require a comprehensive approach to the collection, protection, and management of data, organizations are discovering the additional value of working towards an improved data ecosystem to help digitization and monetization of data.
The chief data office, under the leadership of the CDO, faces the complex challenge of establishing the data management functions, policies, and procedures and then governing the data. However, common challenges associated with instituting a CDO office are usually preventable if the right steps are taken from the outset.
To establish a successful CDO office, begin with these three crucial tasks.
Task #1: Champion a Data-Driven Culture
Lay the groundwork for your CDO by partnering with business and IT leaders in your enterprise to create a data-driven culture. These partnerships are the gateway to a successful culture shift. Such leaders can convey not only the potential in unleashing the power of data using modern tools and technologies but also the risk of keeping the status quo. These partnerships also help navigate power and politics within the enterprise.
Educate your partners, empower them, and utilize them in all aspects of the process, including the development of CDO priorities (such as artificial intelligence, predictive and prescriptive analytics, digitization, customer relationship management, marketing, and regulatory compliance). The priorities listed as an example indicate a range of objectives to drive innovation and optimize the use of data.
In partnership with the key stakeholders, develop education, training, and awareness programs for an enterprisewide adoption of the data management programs. After all, a data-driven culture is achieved by identifying and communicating the value of data to the people who will be operationalizing the results.
Task #2: Formalize the Chief Data Office and the Chief Data Officer Role
There is a common misconception of the CDO's role in an organization. Many see the CDO function as an extension of IT rather than an enterprisewide function to enable and or support business capabilities
One emerging trend is to federate the CDO function by business unit, geography, or by purpose. Additionally, although there is no rule as to who the chief data officer should report to, many organizations are leaning towards aligning data with business functions other than IT and combining data and analytics roles such as in a chief data and analytics officer.
Clarify how your CDO is integrated with the enterprise by identifying at least one strong business function or executive sponsor who will directly benefit from the CDO's success -- this is in addition to the CIO who ultimately is the custodian of data.
Depending on your data management plan, a successful CDO can realize business benefits, drive down risk, and improve competitiveness. Identify the right model for your business so it aligns with your organizational structure. Doing so will also enable the successful execution of the CDO's tasks with the least resistance.
Task #3: Develop the Strategy, Road Map, and Implementation Plan
Involving business stakeholders to champion and formalize the CDO office and CDO role is important, but even more important is their buy-in for strategy and execution. When developing the strategy road map and the implementation plan, consider the following:
3a: Conduct a Data Management Maturity Model Current State Assessment
Establish the baseline of the current state of the key data management functions of your enterprise. From here, develop short- and long-term goals that tie into your enterprise data management program. Jointly develop an implementation road map with enterprise stakeholders. Make sure to align CDO initiatives with desired key business outcomes; determine how each item on the agenda will benefit business functions to improve the bottom line. You should ultimately focus on enabling business capabilities with quantifiable benefits. Leverage agile methodology to quickly and iteratively deliver tangible results from data management programs.
3b: Define and Implement a Data Governance Framework Unique to Your Organization
To create a governance framework, you will need to identify data stewards and establish a data governance council. You should start small with an initial set of enterprise-critical data elements and then expand rapidly to an actionable, practical data governance program.
Clearly establish business and IT ownership for definition, quality, and security of data. The key is business stakeholder participation and data ownership.
When identifying enterprise-critical data elements, start with your enterprise applications (ERP, CRM, SCM, and other administration systems) as they are the initial data source in many instances. Although data governance is often centered on downstream data warehouses, feedback to upstream systems should also be considered for the data governance program to be effective.
Pulling It All Together
Although there are other important aspects to consider, the three imperative steps above can be used as a general guide to successfully establish and operationalize the CDO and to leverage your enterprise data assets to improve your bottom line. The depth and breadth to which you implement the above will be unique to your organization based on your business priorities, desired outcome, organizational culture, and stakeholder engagement.
Suresh Venkateswaran has over 20 years of experience developing successful business analytics applications that deliver actionable insights for companies ranging in size from small and medium businesses to Fortune 500. His background includes data management maturity model assessment, data vision and strategy, full-stack business intelligence implementations, big data, and predictive modeling. Suresh has extensive experience in financial services, insurance, credit cards, auto finance, student loans, and healthcare. He was previously employed by AIG, Fujitsu, IBM, and KPMG. He is currently the founder and principal of GenZ Solutions, a data management and analytics services provider, based in Houston, TX. He can be reached at