I recently had the pleasure of hosting a webinar that explained how to increase enterprise data value and data trust, and avoid data management mistakes that can turn data assets into liabilities for an organization.
Topping that list of data management missteps is absent or poorly executed data governance. When data is improperly governed, its usage, accuracy and reliability can all come into question. But before we dive in, let’s first define what we mean by data governance. Simply put, data governance is the formal orchestration of people, processes and technology to enable an organization to leverage data as an enterprise asset. A successful strategy depends on this three-legged governance stool–people, process, and technology. Without a strong foundation in each, the effort will ultimate collapse and fail. To be successful, the technology must support the people and processes that are driving the initiative. With that in mind, let’s explore the data governance maturity of companies today.
During our recent webinar, we conducted a poll to see where companies are at in their data governance journey. Participants could choose from five possible answers: A) not started, B) currently defining our strategy, C) started, but a work in progress, D) implemented with measurable KPIs, or E) Restarting initiative. The results were interesting:
Among respondents, a small percentage have not yet begun, a few are restarting their initiative, roughly a quarter are still defining their strategy, and the vast majority have started but it’s a work in progress. These results demonstrate that most organizations don’t have a mature data governance program. So the big question is, how can organizations reach a desired maturity level with their data governance program? There are several steps organizations can take to advance their data governance initiative.
First, you need to align the data governance program with key enterprise business objectives and KPIs. Once these are identified, the next step is to define the processes and business rules around these objectives.
Next, you must identify the key applications and data elements that support those objectives. You’ll want to define these key artifacts, understand any other artifacts that may be related or impacted and map out the data lineage. Once these key artifacts are defined, you need to apply data quality to ensure the data is accurate and trustworthy. This is referred to as a “top down” approach to data governance.
Data quality is a critical component of a successful data governance program. Data quality helps build users’ trust in the data, encourage data utilization and produce valuable business insights. Yet studies have shown that data trust is lacking in most companies. Which leads us to the BIG question: “How do I help my users gain data trust?”
There are several steps that organizations can take to help gain this data trust. We previously talked about governing your data and identifying the key assets in the organization that line up with the enterprise KPIs and objectives being measured. Once those data assets are defined, data quality rules need to be applied. There are two main categories of data quality rules; basic and advanced. Basic quality rules are the most common used in organizations today, and consist of checks for completeness, conformance and validity. When applied, these rules will ensure the data meets basic quality standards.
Advanced data quality rules ensure data integrity and accuracy. Integrity checks track data as it’s moving from point to point across the data supply chain, within and between systems and processes. They ensure that data values have not been changed or records dropped. When combined with data lineage, these data quality rules give you a powerful view of end-to-end data integrity across the enterprise.
Data quality checks for accuracy evaluate data at the point of consumption, to determine whether data values are what they are expected or supposed to be. For example, I have a phone number for John. Can I confirm that it is actually John’s number? When you combine advanced data quality checks for accuracy and integrity with a strong foundation of data governance, it allows your users to trust the data and leverage it as the valuable asset the organization intends it to be.
For a closer look at how you can ensure data quality within critical enterprise systems, check out the white paper below.
For a deeper dive into this topic, visit our resource center. Here you will find a broad selection of content that represents the compiled wisdom, experience, and advice of our seasoned data experts and thought leaders.