Maturing Data Governance and Data Literacy to Serve Organizations Better
Maturing Data Governance means growing an organization’s conception and management of its data assets from a narrow IT project conception to the broader business.
Maturing Data Governance means growing an organization’s conception and management of its data assets from a narrow IT project conception to the broader business. Organizations can no longer ignore this approach as they drive to digitally transform, improve business efficiency and effectiveness through digitization and human resources. Al-Ruithe, et al. hammer this point home by stating that researchers and practitioners often confuse the IT governance that centers around hardware and applications with Data Governance.
This muddling of IT and Data Governance can be understood as the technology appears more visible within a digital transformation project. Think of Amazon, and picture a website where customers buy stuff. Buy a sneaker from the Nike app on your phone and think of it as the digital transformation. Then, Data Governance gets assigned to a particular IT project or box on the enterprise Data Architecture model.
Unfortunately, many organizations, misalign Data Governance with business needs. According to The DATAVERSITY® Trends in Data Management 2020 Report, about 83% of survey participants said their organization either had a Data Governance in place or plans for one. But, only 7.34% had Data Governance at a high level of maturity. As Jenny Schultz from Freddie Mac states, “we have tried to do the ‘data thing’ more than a few times, driven by IT, but it never stuck because the business didn’t see the value of governing it.”
So, how does a company mature its Data Governance to serve the business better? Research reveals at least four common themes:
– Connect Data Strategy and Data Governance.
– Use a Data Governance Maturity Model.
– Build credible Data Governance from the top down.
– Show value through collective Data Literacy.
Connect Data Strategy and Data Governance
Mature organizations connect a business aligned Data Strategy with Data Governance. They achieve this goal by looking beyond data assets for purposes of individual projects to serve and evolve with the business. Jeff Fuller writes in the North Carolina Medical Journal that organizations need to know which Data Governance metrics matter by realigning to Data Strategy priorities. When companies become myopic by concentrating only on datasets that are most readily available, the capacity to predict or deal with the business context tomorrow suffers. On the other hand, when organizations align a good Data Strategy with Data Governance, they focus on the critical data assets that serve the enterprise.
In addition to better-adapting information to future business needs, tying in the Data Strategy with Data Governance widens the understanding of how well it supports it. Donna Burbank of Global Data Strategy, Ltd., created a visual describing a top-down and bottom-up business perspective of Data Strategy, technology, and Data Governance.
For example, CEMEX developed a data-driven strategy with the help of Burbank. It centered around customer experience, from the first contact to sale. CEMEX evaluated its existing governance on its Data Strategy and repurposed it using “…small and agile-based digital teams to prioritize projects and align different internal customers toward a common goal.” Through this initiative, CEMEX matured its Data Governance in symmetry to the CEMEX Data Strategy, realigning governance with business goals.
Use a Data Governance Maturity Model
In addition to Data Strategy, knowing how to mature Data Governance towards a business mindset requires benchmarking a company’s Data Governance execution to what it was before and to generally known best practices. It means periodically and objectively assessing Data Governance using a maturity model.
Different models exist with a distinct emphasis on different goals. Gartner, IBM, the Capability Maturity Model of Integration (CMMI), and the Data Management Maturity (DMM) model represent some common examples. Choosing a good model depends on business challenges, structure, and culture.
For example, the State of Arizona, a decentralized government that operates by consensus between agencies, needed to find a way to better share data. Jeff Wolkove and his team worked on a solution to Arizona’s problem. They ended up using a widely accepted and standardized data management standard, the Data Management Maturity (DMM) model from CMMI (See the diagram below).
Jeff and his team chose the DMM CMMI model to give technology-neutral feedback to the state with useful tools to customize their approach. During the implementation of the CMMI model, a state-wide Executive Governance Council was developed, along with a Data Stewardship course.
The training provided useful content for the IT and business staff. Specific skills facilitated Data Governance and a working relationship between IT and business, as well as grow the State of Arizona’s data maturity.
Most importantly, The State of Arizona found a data maturity model that worked for its business. In some cases, organizations need to create one from scratch. For example, the government of South Africa needed Data Governance supporting the integration of complex data streams and diverse stakeholder needs. In the meantime, government departments implemented little if any Data Governance. To grab hold of its data assets and ensure sound decision making, researchers developed for and recommended a Data Governance maturity model, the DGMEM. The DGMEM identified gaps and created “awareness of Data Governance Processes and the negative implications of ungoverned data…” in the Eastern Cape province of South Africa. So, organizations need to consider how a maturity model fits with business needs.
Build Credible Data Governance from Top Down
While using a maturity model may help, Data Governance requires credibility across the enterprise, especially with the business. Michele Goetz from Forester explained:
“The purpose of Data Governance is to catalyze interested parties to build a culture of participation, enablement, and sustainability for data compliance and value.”
Getting that level of trust can prove difficult, as researchers demonstrate. For example, one DATAVERSITY® respondent said:
“We see Data Governance is necessary to succeed as a global company. But some groups in the company resist the changes that go along with it.”
Building Data Governance credibility also comes from executive leadership actions to support it. Over the last year, organizations have increased senior management involvement in Data Management. The 2020 Trends in Data Management reports an increase of the Chief Data Officer (CDO), 8.62%, as a Data Management driver for the enterprise. While many Data Management initiatives continue to be driven by IT; the study has seen more business involvement, including a Chief Financial Officer (CFO), Chief Accounting Officer (CAO) and Chief Operating Officer (COO). This shift to business managers promises more trust in Data Governance.
Freddie Mac, a mortgage financing company, learned how backing business made a Data Governance brand that would stick and be trustworthy. Freddie Mac’s Jenny Schultz and Stephanie Grimes saw Data Governance credibility as not a technology problem, but a “people-culture change-communications problem.” They got the right people involved and communicated a data Governance program based on the business’s needs. Freddie Mac’s head of business-backed Schultz and Grimes plans to move forward. While Schultz and Grimes engaged and incorporated other workers’ feedback, those resistant to changing the business for the better were told, by the head of business, to get on-board. Schultz and Grimes’s approach grew more trust in Freddie Mac’s Data Governance and evolved it towards a broader business solutions perspective.
Show Value of Data Governance through Collective Data Literacy
Data Governance credibility with the business needs to be ongoing, and collective Data Literacymakes this happen. The Data Governance structure that worked during one project needs to apply and adapt to others and handle future high Data Quality and accessibility needs collective Data Literacy ensures a shared understanding and language about how to read, interpret, use, and argue with enterprise data. Increase collective Data Literacy helps keep Data Governance alive and trustworthy, helping IT and business work together.
Vanessa Lam, Optoro’s Business Intelligence Manager, reassessed and improved Data Governance. She identified three issues among business analysts: “a fear of data, inconsistent use of vocabulary and metrics, and mistrust of data.” She worked backward from these problems to remediation, including long term Data Governance processes. Optoro made collective Data Literacy education for new hires, existing analysts, and others across the company a priority. The educational program strengthened communication, understanding of metrics, and a ticketing system to provide feedback. This approach demonstrated Data Governance’s continuous value and agility.
As the examples above show, organizations can get stuck in approaching Data Governance from an information technology short-term project perspective. To mature, organizations need a broader business approach to Data Governance. Aligning Data Governance to a good Data Strategy, choosing a data maturity model, building credibility, and showing value through collective Data Literacy helps organizations move away from an IT project perspective towards benefiting the enterprise and digitally transforming it.
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