We are at the DGVision 2019 conference in Washington DC as one of the sponsors (https://dgvision2019.dataversity.net). Several attendees stopped by our booth to talk to us about their data governance challenges. We asked some of them to write down their thoughts on a blank piece of canvas, so that we could identify the key themes and share them with those that couldn't be here.
Here's what they wrote -
In this post, I'll discuss the major themes related to data governance challenges that emerged and share my thoughts on how organizations can overcome them.
Here's what the Attendees Told us
The three major themes that emerged were:
Culture change: "Building a data-driven mindset", "Spread the cult of data", "Improve business-data partnership", "Skill and Culture Change", "Too Many Areas Claiming Data Governance", "Sponsorship", and "Culture eats Strategy and Leadership eats Culture",
Data Quality: "Adoption of Quality Data", "Integrity post Integration", "Can't do Analytics with Bad data", "Bad data is impacting productivity", "Accountability", and "Data quality problems exist in all our systems, need a strategy and game plan to fix it",
Data Overload: Several attendees mentioned that their organizations own a lot of data but are struggling to govern, manage, and gain insights from it. This was also impacting their ability to comply with existing and new regulations and manage risk related to data (e.g., privacy, access control, security, quality, consistency, and lineage)
What should Organizations Do?
Changing Culture: Culture is a set of shared beliefs, traditions, and expectations and typically gets ingrained within organizations. Changing culture is a daunting task and a multi-year journey. Organizations that wish to become data-driven to drive outsized business outcomes, must take steps to address this across all levels of the organization - starting at the top. Three things that can kick-start culture change are: (1) In this "Age of Data" organizations must focus on People, Process, Technology, and DATA. Endorse the notion across the organization that data is a strategic asset and must be treated as such - not as digital exhaust that can be ignored, (2) Educate staff that data hygiene is as important as personal hygiene. Bad data impacts operational efficiency and an organization's bottom line, and (3) Develop a data strategy focused on business outcomes and invest in maturing data management and governance capabilities to achieve it.
Data Quality: Poor quality data impacts staff productivity since profiling, standardizing, and cleaning bad data typically consumes 70-80% of the data analytics effort. Only 20% effort is spent on the value-added activities. A data quality strategy is a critical first step, if the organization doesn't have one. The strategy should focus on the critical business outputs and outcomes and the data associated with them. This should be followed by the definition of data quality dimensions of value to the organization, followed by identification of the subject matter experts that should define data quality requirements for the critical data sets.
Data Overload: Most organizations are dealing with this challenge, especially due to the proliferation of enterprise data in cloud environments. Building out a data catalog, data dictionary, and business glossary, and identifying systems of record for critical data sets are starting points. They enable knowledge workers to quickly find the meaning, policies, data quality rules, and location of data assets during the data preparation phase, significantly reducing time-to-market.
Conclusion
The comments and challenges that the attendees at DGVision 2019 shared with us are in line with what we hear from our customers and prospects. Managing and governing data well is an art and a science. Organizations that have succeeded at governing data well, started by focusing on critical business outcomes and outputs. Rather than "boiling the ocean" they scoped their governance program around data that was associated with the critical outcomes and outputs.
Data Governance is a key component of an organization's risk governance, since it focuses on data privacy, data security, data quality, and data semantics.
It takes a well crafted data strategy tied to key business outcomes and outputs, corporate sponsorship, culture change, change management, training, and a strategic mind-set to get it right. Data governance is a critical aspect of corporate governance, so organizations should invest the time and effort it takes to mature their governance practices. Maintaining the status quo isn't an option.
Post Script
To highlight the fact that data hygiene is as important as personal hygiene, we handed out Data Hygiene Tube Kits at the conference. Contents of the kits were a deodorant stick, shave cream packets, safety razor, toothpaste, toothbrush, comb, shampoo, and a compressed towel.
About Jay Zaidi:
As the Founder and Managing Partner of AlyData, my team and I help CXOs drive out-sized business outcomes from their data assets — through our CDO Advisory, Data Governance, Data Risk Management, and Insights consulting services. We've developed proprietary frameworks and methodologies for data risk management, data governance, and data quality - which enable us to deliver high-quality solutions faster. To learn more or get in touch, visit http://www.alydata.com.