Recapping the Data Governance Winter Conference and looking ahead to 2018 governance trends
The 2017 Data Governance Winter Conference on 4 - 8 December covered topics ranging from how to start a data governance program to attaining data governance maturity to how to improve your organization’s information quality. After attending, Brian Mayer, business ready data practitioner, and Mark Lynch unified governance practice lead, discuss major data governance trends covered at the conference.
What was the most common data governance topic and/or question this year? Do you foresee this changing in 2018?
Brian Mayer: For me, it was by far the need to prove short-term and long-term value from a governance effort. We all hear how organizations need to be more resourceful with the resources they have...and this is no different in the governance space. It may be even more important with governance programs, as there tends to be a good amount of skepticism regarding tangible results. I don't foresee the focus on quantifying a governance program changing to a different area of interest. I actually anticipate having more discussions on value quantification. There is no shortage in concern over getting a return on resources dedicated to governing data and maintaining data quality standards. We will continue to share ways to link data quality and data governance metrics to business objectives and key performance indicators.
Mark Lynch: Organizations are looking to move successes in data governance to an enterprise level. This brings many challenges, beginning with communication across all business units. Not surprisingly, data governance teams are comparatively small for most organizations. These teams are the thought leaders in data management, remaining nimble and allowing the day to day to happen in the business areas by the owners of the data. Critical factors in building an enterprise program include: first and foremost, data governance must be looked at as a framework to guide data management, as opposed to a project that will have a definitive beginning and end. A successful enterprise effort requires support at the senior management level and drivers of change throughout.
What are the major hurdles and challenges that an organization will have to overcome in 2018 to keep a forward momentum in developing a data governance program?
Mayer: One of the more common challenges our team receives questions on is related to value or return on their data governance investment. Whether it be personnel or allocating funds to a data strategy or a specific governance initiative, many are looking for quantitative validation of program success. Since this question typically comes during the initial phases of designing a program, it is a best practice to link quality and governance metrics to the objectives and key performance indicators (KPIs) of the business. Having a baseline prior to implementing, then measuring post-implementation will show how the governance program helped make an impact to a specific business KPI. There may be instances or aspects of measuring success that can't be quantified. So one can use qualitative assessments to understand and communicate the value a data quality and data governance program provides. An example of this would be to have periodic checkpoints with data users to understand how the governance people, process and technology have impacted their day-to-day data usage and decision making.
Lynch: Attracting and keeping talent that has the necessary skill sets is a concern voiced by many at the recent Data Governance Winter Conference (#DGWinter) in Delray Beach, Florida. This requires a disruptive look at our data supply chain and how we organize people and processes to make it most effective. Budget is also a major hurdle to overcome. It requires a top-down alignment with support and commitment from the most senior levels of management. Finally, commitment to a data strategy is essential so that all understand guiding principles for the enterprise governance framework.
Are there any governance trends you are anticipating in 2018?
Mayer: There are two areas in which we are paying close attention. One, as witnessed firsthand at #DGWinter, the increasing engagement of lines of business, as opposed to IT, and their management of governance programs. We anticipate more governance efforts being driven by the business as they are becoming more actively involved in data and analytics and want to assure they have access to quality data. Two, we have all heard about machine learning...the ability of systems to learn over time, anticipate and make decisions with limited or no human interaction. IBM is certainly not new to the machine learning technology. In fact, IBM is applying automation into its governance software solution, Information Governance Catalog. This technology will help free up governance resources such as allowing data stewards to focus on more complex data quality needs and leaving the more common tasks for Information Governance Catalog to handle.
Lynch: Also seen at #DGWinter was that the use of data will continue to be a key differentiator for success. The speed at which data consumers need access requires new approaches and a leveraging of technology. Microservices and compartmentalizing data will provide this access while not compromising security. The complete ecosystem of data must be available and visualization across multiple platforms will be essential. IBM unified governance embraces this thought process and looks at all types of data, in total, allowing an enhanced user experience.
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