A client of ours was working with a well-known analytics and BI platform and was recently informed that due to their usage and because the pricing model for the platform had changed, they could expect a 4x increase in costs for future contract years! Of course, this created a ton of angst at the leadership level, so we dug in and discovered that the company was not using the platform optimally. After further discovery, we pinpointed one of the primary causes - the lack of a defined and enforced data governance policy. Because there was no clearly defined way in which the platform should be used, nor stringent oversight of its usage spanning years of usage, it had become a virtual Wild West.
Many of today's data management platforms offer extensive capabilities to help data operations professionals navigate their data landscape and generate real value from their data assets. However, these tools can also create unintended expenses (especially in an era of credit-based consumption models), resulting in added risk to the enterprise.
And these are not isolated incidents.
Many companies end up misusing tools simply because they typically buy those tools based on a need to solve a particular problem or set of problems. With data governance and quality standards in place, onboarding a tool or platform is akin to inserting a puzzle piece in the middle of an incomplete puzzle. But without a robust data governance framework or data quality standards, today’s advanced tools can do more to create inefficiencies and unforeseen costs than they can to solve the business problems they were intended to solve.
A Plethora or a Framework?
A common practice for companies is the hasty adoption of tools that appear to offer quick fixes.
The idea is that when something is broken, it needs to be fixed, and even if the fix isn’t perfect, the business keeps moving. Companies, in their quest for quick solutions, often end up investing in a plethora of tools. And the worst part? They typically end up utilizing only a fraction of these tools' capabilities—maybe 40% of one tool's functions, 20% of another’s, and 15% of a third – you get the idea. It doesn’t take a Ph.D. to know this is not a good business practice.
So, what the heck do we do about it?
One of the best ways to avoid the challenges described above is to establish data governance and quality policies before procuring these tools. Without this best practice in place, you may be making very expensive mistakes. By establishing a robust framework for data governance and clarifying data quality standards, organizations can select tools that are truly aligned with their specific requirements. The result? Tool usage will dramatically increase, so you will get more value out of your investments, and, more importantly, the tools will be used for their prescribed purpose,. Teams are no longer bogged down by inefficient tools ill-suited to their tasks. This sets organizations on a path of effective data management, where every tool and process is aligned with the overarching goals of data quality and governance. And the Wild West becomes a thing of the past
What Is Data Governance?
Data governance refers to the collection of practices, policies, standards, and processes organizations use to manage their data effectively and responsibly. It's a comprehensive approach encompassing various aspects of data management, including quality, privacy, security, and compliance. Data governance aims to ensure that data is accurate, accessible, consistent, secure, and used to add value to the organization.
Data shouldn't be viewed as a byproduct of business processes. It is a vital asset - perhaps one of a business's most valuable assets. With data governance in place, companies have a central pillar supporting and guiding their decision-making.
For this to happen, organizations need to start by creating a data-centric culture. In this culture, data governance becomes a shared responsibility, not just a directive from the top. It's about empowering every member of the organization to play a role in maintaining data integrity, quality, and security. When this happens, the value of your data will increase over time, enabling others to have data that remains usable over time, avoiding the inefficiency of repeated clean-up cycles.
What Is Data Quality?
Data quality refers to the degree to which data is accurate, complete, consistent, reliable, and relevant for a specific purpose. It measures the condition of data based on factors such as accuracy, completeness, consistency, and reliability. High-quality data is crucial for organizations. It forms the foundation for informed decision-making, efficient operations, and strategic planning.
High-quality data enables businesses to make informed decisions, understand customer needs, optimize operations, and stay competitive in their respective industries. But, poor data quality can lead to misguided decisions, inefficiencies, and lost opportunities.
Many companies have the challenge of knowing the data is bad but thinking they will clean it later (effectively kicking the can down the road). And many have gotten by like this for decades. But now, with AI bursting onto the scene and its potential to transform decision-making, predict trends, and optimize operations, corporate leaders and BoDs have begun demanding its adoption. There is just one problem for many companies. AI algorithms that enable these new capabilities heavily depend on data quality, and all those wonderful outputs are only as good as the data we input. No one should be surprised to know that if you run bad data through an AI engine, you will only accelerate bad outputs and bad decisions.
This is why continuous data quality is shifting higher up the priority list.
When these two elements (data quality and data governance) are synergized, they create an environment where data management tools can thrive. Tools are only as effective as the data they work with and the policies that govern their usage. With data governance and quality as the guardrails, organizations can be sure they aren’t wasting money on tools that can’t do what they need them to, or time constantly cleaning the data.
Other Potential Hidden Costs
When data governance and quality standards are lacking or inadequately enforced, organizations encounter costs that extend beyond the obvious. One hidden cost is the misuse of data management tools, particularly in how they handle sensitive data.
In an effort to get jobs done efficiently, teams might implement tools that inadvertently provide excessive data visibility. This becomes particularly problematic when sensitive data, such as credit card details or social security numbers, are involved. Without stringent data governance policies and quality checks in place, there's a real danger of such sensitive information being exposed to unauthorized personnel.
Beyond privacy concerns, the misuse of tools due to inadequate governance and quality controls can place an organization in a precarious position regarding security. The risk of cyberattacks or inadvertent data leaks escalates when Personally Identifiable Information (PII) is not adequately safeguarded, which could land your organization in a world of trouble!
Be A Data Steward
Here’s a suggestion. Everyone, from the CEO to the newest intern, is a data steward. This means that if you’re in the system and notice an issue, the expectation is that you don’t just acknowledge it and skip on to the next thing but take action to rectify it. Allowing data inaccuracies or issues to persist is not a good business practice, plain and simple. By encouraging every individual to “fix it, not ignore it,” you can create an environment where data quality is continuously enhanced, not degraded.
With these policies in place, your data becomes more accurate, reliable, and useful. And the byproduct of the collective effort to maintain data integrity will propel growth, foster innovation, and enhance decision-making across all levels of the organization.
