Data Governance or Government?

August 29, 2017

 

Ronald Reagan said, “Government’s first duty is to protect the people, not run their lives.” Unfortunately, the alignment of what we intend and what results is often completely askew or missing altogether. The vision created in our minds tends skim over the reality which is much more bogged down in detail and process. It’s not hopeless though, we can certainly achieve the ideal we desire with dedication, education and a lot of communication.

 

 

Data governance suffers from the unfortunate circumstance of having “govern” plopped right into the middle of its name. This situation has certainly contributed to a feeling that data governance is the act of creating a government of sorts around an organization’s data. It does not help that the definition of data governance is so varied across organizations, educators and practitioners. This often results in an individual looking to bring order to the chaos around their data by creating their own definition. That new definition takes hold and the mythology is perpetuated.

 

Let us step back from the brink of insanity for a moment and try to understand what the purpose of data governance is. Why would any organization want to create a data governance practice around their data? Is it fair to assume that by implementing data governance, your time to market with data insights will grow ever longer? Is data governance yet another layer of the data security processes already established in your organization? Does everything that comes from your data governance organization have to be a mandate?

 

The first place I start when thinking about data governance is to ask why I should implement it in the first place. You may be asking how you can ask why if you don’t know what data governance is. It’s true, the definition of data governance is important but I am going to do this in reverse for a reason; the answer to why will in some ways dictate what data governance is for your organization. There are certainly some common threads that should make up every data governance practice that can be summarized with the following idea; Data governance is a set of policies, practices and guidelines established to ensure the accuracy, completeness, consistency and security of data in the organization. This is a very broad definition and as such, understanding why we want to clean, organize, and secure our data will drive a more in-depth definition of what data governance means to us.

 

Before we explore our reasons for why though, I would like to mention that wanting to clean, organize and secure data should always be efforts we undertake to enable the use of data. If ever data governance becomes a hindrance to the use of data, it has failed. Data governance is established to encourage data usage and make easier the insights generated from your organization’s data. At no time should data governance slow down or complicate your organization’s data usage strategy. Given that I just introduced at least three new areas of concern for governance, you may wonder how that would not complicate the data organization’s strategy. If you do have this question, good for you! The implementation of the data governance practice should be agnostic of data technology initiatives and exist in a way in which it can remain objective. Additionally, while we want to ensure we are not in the way of existing and future data projects and programs, it is critical that data governance is implemented alongside these initiatives the way the cowboy on the horse runs up alongside a moving train in the movies. You never see that train slow down to allow the horse and cowboy catch up; it just keeps moving. The governance practice needs to integrate at the same pace, parallel to the organization’s data practices and begin to benefit the data projects as soon as it is introduced. That is not to say that the practice would start at the same pace. It can take a while to set the foundation to be able to keep pace with our data train and that is okay. The key is to avoid slowing that train down.

 

So, what is Data Governance?

Data Governance promotes confidence, increases efficiency and strengthens the security of enterprise data through policies and programs established to ensure accuracy, accessibility completeness and security of critical data assets.

 

The Importance of Data Governance

The importance of data cannot be overstated. From consumer behaviors, to product performance, to employee satisfaction, data is a critically important asset for any organization. Gone are the days of simply seeing data populate the contents of a report or fill out a spreadsheet. Data ensures an organization’s viability and validity in the industry and without proper use of available data, a company cannot survive.

Given the sheer importance of data in an organization, it is important to ensure this resource is treated with the respect and care it demands. Data must be managed in a way that guarantees accuracy, accessibility, completeness and security. With the assurance of these four pillars, leadership can be confident in the data driving decisions for their organization

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Confidence

Given the role data plays in the ability to make decisions in the organization, it is critical that leadership have the utmost confidence in the insights their data provides. As such, the following tenants are essential to the successful use of an organization’s data assets.

 

Accuracy

Confidence in data begins with the accuracy of the data. Varying results derived from differing data sources that house the same data can lead to a lack of confidence thereby weakening the enterprise data practice. By implementing processes such as an enterprise-wide taxonomy initiative, the data organization can insure the sharing of consistent and accurate data across multiple systems and platforms while maintaining the autonomy those individual systems were built on. Following an extensive review of government policy and process, the 9/11 Commission concluded that the lack of data sharing between the law-enforcement agencies of the U.S. was partly to blame for the success of the attacks that took down The World Trade Center buildings. Had there been practices in place to share data consistently within a structured data classification environment, we may have been given the warning needed to act before being hit. Data in siloes is weak, unreliable data.

 

Accessibility

Establishing the accuracy of data is of little use if data is not accessible. Data accessibility is the responsibility of several organizations in your company, including Security and Data Governance. The data governance organization must work to identify the data that exists in the organization, who owns said data and how this data is classified, organized and structured. To accomplish this a Data Inventory effort must be implemented and maintained. This effort will result in the ability to identify where data artifacts are house and how to access them for various data efforts. Understanding where customer data is stored for instance, who owns this data, how it is used and accessed will allow the data team to identify the best source of this information and build robust mechanisms for capturing and acting on the insights provided from customer data.

 

Completeness

As with accessibility, completeness speaks to how usable data is in the organization. Of the systems identified as part of the Data Inventory effort that currently house critical customer data, it is important to know how complete these datasets are. This process will require the establishment of an organizational data profiling practice to insure identified data from targeted data domains has been thoroughly profiled for data consistency and completeness. Only then will this data be able to roll up into the Data Inventory with a documented understanding of the state of this data.

 

Security

Although a fully mature data security organization exists within most organizations out of necessity, it is of critical importance that the Data Governance organization maintain a close working relationship with security in the development of policies and guidelines that will drive the effectiveness and success of a governance effort. Security is a subject to cover on its own as the advent of numerous breaches and hacks have necessitated the maturity of security into a complex and multifaceted organization.

 

Ultimately, it may seem that through the authoring of policies, implementation of process and oversight of many data initiatives that Data Governance does resemble “government” on the surface. But if we dig into the what and why of Data Governance, we see that it is truly a set of tools created to enable the usage and fluidity of data. If we must treat Data Governance as government, but do so by “protecting” data rather than “running” it, our data practices and organizations will thrive and grow into mature insight-generating ecosystems and not red-tape laden bureaucracies. The most challenging aspect of any Data Governance practice is understanding that it is a shift in culture and not necessarily updates to the management of data that will drive success. In subsequent articles, I will cover the cultural shifts needed to successfully implement a drive data governance in any organization.

 

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