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The Blueprint for a Data Governance Program

Employee productivity might be summed up by the expression “The last thing I need in my day is_________.”

2022-11-03 21:09:14


GBI, Industry News

1 The Blueprint for a Data Governance Program

Most organizations realize data governance  is necessary - if not indispensable - for  mitigating data risk while sustaining its long term value. However, there’s still a fair amount  of ambiguity around what exactly data  governance is and how to best implement it. 

By definition, data governance is an  enterprise-wide initiative for formalizing the  processes, people, and protocols for using  data. It reduces risk while increasing data’s  ultimate value: consistent reusability for  achieving mission critical objectives. Data  governance is a strategic approach for  creating unassailable trust in data throughout  the enterprise. 

However, it’s far more difficult to succeed in  implementing a data governance program  than it is to define this term. There are many  more companies trying to create governance  programs than there are those who have done so successfully. 

Gartner indicates 80 percent of  companies attempting to scale  digital business will fail because  they don’t have a modern approach to data and analytics  governance. 

New Vantage Partners revealed that  organizations are struggling to become data driven, as only 26.5% have achieved this end,  while less than 20 percent have established a  data culture. The main reason for these woes  is often data governance is an afterthought. A company’s business objectives aren’t based  on data governance, but rather on solving  specific business problems related to strategic  business objectives such as cryptocurrency,  electric cars, etc. 

Oftentimes, companies don’t prioritize data  governance until they reach a sufficient  maturity level in terms of revenues, customer  base, data quantities, and sources. Then,  governance outputs—such as gaining the  visibility of where sensitive data resides,  ensuring the right people have access to the  right data for the right purposes, or reinforcing  corporate responsibility by responding to data  and privacy regulations—become paramount. 

Moreover, many fledgling governance  initiatives are hampered by multi-tasking. SMBs usually don’t have dedicated governance  personnel. A small credit union, for example,  has perhaps 20 employees managing  multiple tasks. If someone’s doing any data  stewardship or data governance work, they  usually have several other responsibilities  preventing them from concentrating on it. 

2 Business Drivers and Data Governance Justification

It’s critical for businesses to recognize and  believe in the potential of their data. A  governance program inspires such trust by  assuring end users about what possibilities  their data supports. Consequently, there are  several business drivers that justify the need  to implement data governance programs. Most pertain to fostering an organizational  culture of trust in data. In this regard, secure  data access is a preeminent requisite for users  to trust their data. Data security encompasses  several dimensions, such as ensuring data’s  controls are current, consistent, complete, and  reproducible. If marketers are attempting to  mine customer data for insights, they need  to know it’s trustworthy and customers have  given their consent to use their data for  promotional purposes. 

Risk management is another driver,  particularly because of the rapid  technological advancements that have  accelerated over the past several years. Technological developments are close to  outpacing those of humans, which creates  trust gaps when the former enables people  to do so much that not all of it’s legal. Technology is going so fast that people can  do more than ever before. The question is:  should they? Popular social media platforms  didn’t collect tons of graph data to break  laws; no one initially told them they couldn’t  and that data contains plenty of customer  insights. In these situations, oftentimes  

organizations didn’t know what they couldn’t  do because of a lack of resources for a  creditable governance program. 

The continual emergence of data privacy  regulations typifies the need to implement  governance for regulatory compliance. With  GPDR, CCPA, and various state level mandates  being enacted, companies need governance  just to assess what data relates to which  regulation. There are also industry-specific  regulations (for finance and healthcare, for  example), and data sovereignty issues in the  cloud about where data reside. 

Additionally, data is the raw material for  data science. This discipline requires  high quality data without regulatory  complications, which is only possible with  effective governance. Employing Artificial  Intelligence and machine learning to  understand customer behavior and improve  customer satisfaction is another compelling  driver. These analytics capabilities are the  new data products enabling organizations to  understand, anticipate, and fulfill customer  needs in an era in which customer experience  is a competitive differentiator. AI and ML  predictions can reduce customer friction  so companies can go from being  

product-oriented to customer-oriented. 

Therefore, a good AI strategy  needs a good data strategy.

3 Data Governance Components 

Guidelines are the specifics about what  employees and business partners can and  can’t do with a company’s data. Data access  is one of the strictest guidelines; it’s enabled  for some users on some datasets and  restricted for others.Guidelines are frequently  based on regulatory requirements, data  governance councils’ input, and collaborations  with business end users. Data residency and  permissible purpose are examples of guidelines  that keep users “within the authorized lines”  of what they can do with data. 

Managed policies are the detailed rules around  how data is used or accessed. They function at  a granular level relating to particular sources,  organizational roles, and user attributes, and  are specific for individual columns, rows, and  cells. These might pertain to which users can  view certain data. For example, data scientists  can access customer data in Salesforce, but  not the credit card numbers they contain. Effective data governance solutions create  policies with executable code in source  systems, so they’re directly enforceable in  operational settings. Robust access controls  are the bread and butter of any effective data  governance program. 

Governance programs must also have  capabilities for evaluating, monitoring,  enforcing, and validating users’ access to  data. Data discovery is the starting point  of these programs and is the capability  to identify sensitive elements in data  throughout the enterprise. Organizations  know they have sensitive data; the problem  is finding it. Traditionally, time-consuming  manual approaches were used, which  were ineffective. Automated data profiling  capabilities are much better. Classification  is the next step in the process, and is the  ability to categorize data according to  definitions, regulations, use cases, and more.  Classifications are part of data cataloging  functionality that provides a central means  of denoting where data is, what it’s regarding,  who owns it, and other vital information. 

The next step in the process is the ability  to define and enforce access control  policies. These policies can be based on  users’ organizational roles, attributes, and  other such things. Access control policies  also involve auditing chronicles of who  accessed data and how, for what reason,  what operation was performed on it, where it  was sourced, and other particularities. Ideal  solutions let administrators easily get answers  to these questions, which are invaluable  for demonstrating regulatory compliance,  assessing risk, and understanding which  regulations pertain to data.

4 Stakeholders and Dependencies

Regardless of which data governance approach  is employed – top down, bottom up, or a hybrid  – the stakeholders for governance programs  are usually the same. These include C-level  executives, data owners, data stewards, and end  users. It’s imperative to get the approval and  participation of the C-level for program success. Since these executives fund these programs,  continuing them hinges on tangible indicators  of success to validate their worth to the C-level. Since these executives are directly responsible  for corporate adherence, the primary  incentive for these stakeholders is regulatory  responsiveness—regardless that they’re the ones  funding governance programs—so organizations  aren’t jeopardized by new regulations. 

Data owners are the top personnel in  business units, such as a VP of Finance,  for example. Their chief area of interest is  increasing their department’s effectiveness  and business value by using data. Data  stewards are generally assigned to respective  business units by data owners. Stewards  actively participate in governance programs;  their responsibilities include developing  an understanding of data and its usage,  granting data access, ensuring policies are  met, ensuring sufficient guidelines are in  place, and monitoring operational activities. This role is oftentimes multitasked. 

There are two types of end users: internal  and external ones. Internal users are data  analysts or data scientists. Data scientists  want to create innovative solutions without  transgressing policies, regulations, and data  governance protocols. External end users  are customers or business partners. They’re  motivated by the growing scope of data  privacy rights for things like subject access  requests or honoring data contracts. Subject  access requests enable data subjects to  ask firms what information they have about  them. These requests often spur executives  into action for governance programs. The  rationale is if firms can’t fulfill this request for  their own customers, they’ll have tremendous  difficulty doing so for others.  

5 Implementation

There’s a two-step process for initiating  implementation of a data governance  program. The first is identifying exactly  where an organization’s data is at the  source level. Pinpointing what data are in  which sources involves mapping how the  organization itself functions. Oftentimes,  a company’s business units indicate how  it’s mapped. An insurance company, for  example, has divisions for products, policies,  sales, and field representatives. These  units are the major business areas that the  leadership of the business naturally thinks  of as fundamental. When assessing this  information, it’s key to not only consider  what information or data is generated by  these different departments, but also what  data they need to function optimally. 

Once organizations determine where exactly  their various data is by mapping how their  businesses function, the second step is that  they must craft a conceptual data model or  subject area model to reveal exactly what  that data is. Specifically, this step revolves  around the business meaning ascribed to  various data. Time-honored approaches  involving ontologies and taxonomies are  invaluable for articulating the world view of  a specific business unit as expressed through  data. These methods identify the particular  terms, semantics, and nomenclature business  users understand, which are generally more  helpful than conceptualizing data in arcane  terms only IT teams comprehend. 

6 Data Governance Best Practices 

Since the goal of data governance programs  is to foster unconditional trust in data, best  practices for such programs center around  a dedicated data access management  strategy. That strategy naturally incorporates  aspects of what data is in which sources  and what it actually means to the various  business units that interact with it—as  identified in the initial implementation  process above. This information is theefoundation upon which an effective data  access management strategy is built. Once that’s been solidified, firms can  engage in data discovery mechanisms  (which are another hallmark of creditable  data governance solutions) to delineate  exactly where their data is. Data access  management strategy best practices for  data governance include:  

These staples of an access management  strategy are analogous to parking in a  garage so someone can’t steal the tires on  your car. The reality is fences make good  neighbors, which is exactly what guidelines  are for valuable enterprise data. 

Another best practice is to ingrain data  stewardship into the process of developing  the business, instead of trying to create  a separate data governance stack later. By getting started with this aspect of  data governance early on, organizations  cultivate a culture of—and respect for—data  governance that’s essential to sustain these  programs for the long term. Aligned with this  

concept is the best practice of selecting an  adaptive architecture that flexibly expands  to include different sources. 

Most businesses leverage data sources that  are external to the enterprise and frequently  found in hybrid cloud, multi-cloud, poly cloud,  or edge computing environments. The ability  to seamlessly connect to these resources  via a distributed architecture that pushes  governance and security policies directly  into these various cloud services is priceless  for successfully governing them. Moreover,  data stewards are central to ensuring this  architecture supports underlying data  governance objectives.

7 Pitfalls To Avoid

The aforementioned Gartner and New  Vantage Partner statistics strongly support  the notion that most organizations either  never complete their governance programs  or fail to do so successfully. Consequently,  the first pitfall to avoid is over-thinking  the various considerations involved in  creating a successful data governance  program. Organizations should adhere to  the best practices in this document for  implementing data governance, but they  should also prioritize taking action over the  time-honored ‘analysis paralysis’ syndrome. Governance should be part of an overall data  management program. 

Ideally, the various steps, tips, and practices  detailed above should be implemented in a  non-intrusive, non-invasive manner. It may  be helpful to contextualize these different  measures by positioning them to relevant  stakeholders (whose organizations may  already be doing some data governance  work) in order to avoid duplication of effort  and resources. Following the above advice  about establishing a data governance  program only formalizes those measures,  makes them consistent, and repeatable  throughout the enterprise—instead of in  different departments or silos. 

Multitasking is another pitfall organizations  should make a point to avoid. Governance  programs work best when employees who  have other responsibilities don’t take on  additional ones for data governance. As  previously indicated, this situation happens  far too frequently, making governance  programs difficult to scale and ineffective.  It’s way more advantageous to have  dedicated employees for the various data  governance positions. 

The final pitfall to avoid is attempting to unify  enterprise data and sources for the purpose  of improving data governance. Doing so  invokes the single source of truth conundrum,  in which the holy grail of data-driven practices  or analytics is to physically consolidate all  data into a single repository to implement  data governance mechanisms there. There  are numerous approaches predicated on  achieving this objective, which involves cloud  data warehouses, conventional ETL, data  lakes, and data lakehouses. 

Nonetheless, this is often an exhaustive task  requiring several months or years to finish.  During that time business requirements and  data sources change, nullifying any value  from this endeavor. Also, by trying to drive  down time to insight with these holistic  platforms, organizations may inadvertently  accelerate time to a lawsuit—without the  proper governance in place. 

8 A Modern Prerequisite

A data governance program is a vital  prerequisite for obtaining long term  value from any significant investment  in data-driven processes. It’s silently  transitioned from something that’s  only necessary for the most mature  organizations to something allorganizations must embrace—or suffer  the regulatory, reputational loss, and  customer dissatisfaction consequences

This paper outlines the specific business drivers, various stakeholders,  components, implementation processes,  best practices, and pitfalls for a data  governance program. Organizations  would be well to internalize these tips and  strategies for establishing a longstanding  data governance program that augments  the recurring business value data itself  produces for the enterprise

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