Robert Seiner and Anthony Algmin squared off – virtually, of course – at the DATAVERSITY® Enterprise Data World Conference to determine which of the following is more critical: Data Governance, Data Leadership, or Data Architecture. Originally intending to play the Rocky theme as they appeared from backstage wearing capes, these two strong hitters instead battled it out online, with each getting four rounds to present their case.
Seiner is the founder and president of KIK (Knowledge is King) Consulting & Educational Services, as well as the publisher of The Data Administration Newsletter (TDAN). Seiner is an author, educator, and thought leader in the domains of Data Governance and Metadata Management, as well as an advocate for Non-Invasive Data Governance.
Algmin is the Convergence Platform Program Lead at AbbVie. He is an advocate for Data Leadership, the author of the first book on the subject, and the host of the Data Leadership Lessons podcast. Algmin has led data-driven transformations in a variety of businesses as a consultant, data architect, and Chief Data Officer.
Seiner began by posing the following questions:
Why is it necessary to connect Data Governance, Data Leadership, and Data Architecture?
Is it feasible to own only one of these items and not the other two?
Why is it critical to have a concise, easily comprehensible definition?
What is each individual’s relationship to the others?
Which one is the most critical?
The First Round: Definitions
Governance of Data
Even if your firm does not have a formal Data Governance programme, data is governed. Default decisions still have an effect on the firm, although not always in a constructive or profitable way. Governance establishes a framework for data use by establishing norms and structure. Seiner defined data governance as “the exercise and enforcement of power over the administration of data and data-related assets.” The strong language is critical, he explained; regardless of the approach, “you must execute and enforce responsibility over data management.” This is not an area in which a “agree to disagree” mentality works well. “You will not fix anything” without execution and enforcement.
Algmin concurred, adding that governance offers the “what” in the exercise of authority: “What are our priorities?” What is the significance of our data?” Both agreed that a brief, simple-to-understand explanation is crucial, because “this stuff is difficult,” as Algmin put it. The aggregate activities of the individuals who work with data drive the business processes that result in success, and those procedures require precise definitions to be carried out successfully.
Leadership in Data
Seiner stated that data leadership is what management need – those who have a plan and understand how to carry it out. According to Algmin, Data Leadership is fundamentally about maximising the value of data. “Data value” refers to the difference in outcomes that a firm achieves when it uses data vs when it does not use data. This value is manifested through increased income, cost savings, or risk management. “So, Data Leadership is the ‘why’ behind what we’re doing: we’re generating data value.” In some firms, he noted, Data Leadership is completely absent.
Architecture of Data
Oftentimes, data architecture comprises of models, policies, rules, and standards used to govern data. To effectively manage which data is collected, how it is kept, how it is organised, how it is integrated, and how it is used in your systems, “Data Architecture is crucial,” Seiner explained.
Algmin emphasised that Data Architecture is the foundation upon which critical objects and methods for Data Governance are built.
not simply occur once, but occur consistently, reliably, and extensibly for the benefit of the business. “These are fundamental business capabilities that result in tangible, meaningful benefits,” he explained. It’s all about the ‘how.'”
Summary of the First Round
The organisation requires all three, Seiner explained. It is critical to establish governance to ensure that people behave responsibly when it comes to the definition, production, and use of data. Data leadership that understands what is being done and why it is necessary, as well as a strategy for achieving it, is also critical. With a formal governance structure, data-savvy leadership, and a formal process for adopting Data Architecture, “you’ve truly nailed the trifecta,” Seiner said.
They are all interconnected, Algmin added. While Algmin feels that leadership is the most critical of the three, Algmin argues that leadership is ineffective without Data Governance and Data Architecture.
Round Two: The Role of Leadership in Data Governance and Data Architecture
When Seiner outlines best practises for Data Governance, he discovers that the most important success indicator for each client is that senior leadership supports, sponsors, and understands the complexities of implementing Data Governance or Data Architecture. Often, firms will not be granted the time or resources necessary to develop a Data Governance programme or a Data Architecture unless the value is recognised at the top levels of the organisation, he explained. ” As practitioners, we recognise the importance of properly communicating to our leadership what these disciplines are and how they relate to one another.”
Data governance include authority enforcement. Data Architecture begins to codify it and put the necessary components in place to ensure that it occurs consistently, according to Algmin. Company leadership—not data leadership—should determine what is critical for the firm as a whole, but do not expect them to know how to use data effectively to accomplish those goals. Data Leadership must devise strategies for integrating Data Governance, Data Leadership, and Data Architecture into a narrative that gives solutions to the problems that business leaders already recognise, he said. It begins with determining how to more effectively accomplish the business’s objective and determining how data contributes to that mission.
Business executives must understand what decisions they must make and what expenditures they must make to maximise the value of their data. If a business owner is having difficulty meeting service level agreements with their clients, “we can find a solution,” Algmin added. Leadership does not need to understand all of the functional distinctions and details of governance and architecture; they simply require feasible solutions that benefit the business. Finally, the most significant shift occurs as a result of effectively maximising potential at each stage of the data lifecycle.
Seiner and Algmin concur that without coordination between the three disciplines, it is difficult to maximise the value of the data. Some organisations have completed one without completing the others, but not as effectively as if they completed all three or completed all three at the same level. “We want people at the executive level of our organisation to understand how they all fit together, why they all fit together, and why they’re all critical,” Seiner said, adding that resources are required to successfully integrate any of these disciplines across the firm.
Round Three: Integrating leadership and development assistance into the DG plan
Seiner began round three by introducing seven critical components of a data strategy:
Define business requirements and strategic objectives for effectively managing data across the enterprise.
Recognize the business’s questions and determine which of those questions can be answered using data. “Everyone has heard the phrase ‘data is the organization’s most precious asset,'” but without a knowledge of what the firm is trying to accomplish with the data, it’s akin to aiming at an undefined target, Seiner explained.
Determine the requirements for technical or technological infrastructure. Establish the technology infrastructure necessary to meet the given criteria.
Develop the ability to transform facts into insights and insights into knowledge.
Determine the appropriate personnel, procedures, skill sets, and resources and incorporate them into the data strategy.
Establish a path for how people will identify, produce, and use data across the organisation, incorporating Data Governance as a critical component of the overall Data Strategy.
Utilize data strategy as a road plan for achieving organisational goals, taking governance, leadership, and architecture into account while being constrained by available resources.
Algmin argues that there is a widespread misunderstanding of the term “Data Strategy” and has suggested that it does not exist. Rather than that, he views it as a means to a goal, because a Data Plan by itself adds little value unless it is well-aligned with a business strategy. “At the end of the day, this is not a ‘data’ strategy at all. This is a data execution strategy.” The final state is already largely established by the company strategy; all that remains is to determine the implementation. “What counts is how we participate in that journey.” Rather than that, he proposed developing a system for assessing how data use impacts desired company outcomes and contributes to the achievement of company goals.
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