Data Governance and Data Quality Are conjoined Twins….. Doing one without the other is a disservice to your data maturity.

Data Governance and Data Quality Are conjoined Twins….. Doing one without the other is a disservice to your data maturity.

Every organization today knows the health of their data is the oxygen to their vitality. The better your data health, the greater your opportunity and edge to compete in today's marketplace. Your data is your life-wire for business survival and growth. Any deficit in your data health could potentially put you out of business and competitive game in a heartbeat. Hence, the importance of every organization's investment in Data Governance and Data Quality is non-negotiable.

Having said this, it's surprising that we still have organizations struggling to invest their time and money in these two disciplines rightfully.

The reality is that there is no option between Data Quality Management and Data Governance adoption. You cannot successfully do one without the other.

Most organizations today need little or no lecture to kick off their data quality initiative. Many have woven this to their operational rhythm for a long time and still proactively budget for their Data Quality initiative year after year without kicking off a Data Governance Adoption alongside to support this.

The sad truth is that your Data Quality initiative can only be as good and efficient as the seriousness of the governance culture built around it.  Your Data Quality initiative will continually suffer the same faith of poor data ethics in an ungoverned data environment.

You've heard me call out data quality and issues remediation in an ungoverned data environment is like playing a 'whack-a-mole' game with the health of your data. Your quality issues never go away as you’re only treating the symptoms and not the root cause. Eighty percent of quality issues in most data environment are ethical issues. The earlier an organization realizes the optimal quality of their data requires a fertile data environment where governance is at play, and cultural transformation of mindset is ongoing, the better it is for that organization to start seeing the light at the end of the tunnel for its recurring poor-quality data.

So, what is the relationship between Data Quality and Data Governance?

Data Quality and Data Governance are both complementary disciplines around the optimal health of an organization’s data asset.

Data Quality defines the attainable fitness state of an organization's data in terms of its fitness for purpose to fulfill the organization's vision. Quality of data is defined relative to its accuracy, completeness, reliability, accessibility, and timeliness to meet the organization's needs.

While Data Governance is a discipline of formalized oversight of people's accountability around key controls required to enable an organization to manage, optimize, protect and leverage its data asset for multiple needs to achieve the same fitness for purpose and business needs.

Basically, both Data Quality and Data Governance go hand in hand. You cannot do one without the other. Data Governance is the ‘train’ that drives every organization's data asset to a desired quality destination. Optimal data quality is the end in mind for every data governance adoption. Hence, you cannot separate the two.

At the heart of data, governance oversight is a 'community of people with assigned roles & responsibilities to drive and achieve the stated purpose through Stewardship. Stewardship is the conduit of effective governance adoption. Governance defines ‘what’, Stewardship drives ‘how’. 

Having a solid stewardship activation in motion in any organization is a booster to Data Quality Management. Hence, Stewardship activation propelled by Data Governance is a win-win for both Data Governance adoption and Data Quality Management execution.

To this effect, the following are some of the ways your organization’s Data Governance adoption can help optimize your Data Quality initiative when activated together as part of Governance Standards of Care:

·        It creates an accelerated path for accountability around data quality monitoring and management by facilitating an ethical creation, use, and remediation of quality issues. 

·        It provides a richer platform for needful cultural transformation around your data asset.

·        It provides needful stewardship around your data value chain. Ensuring quality issues are non-recurring and not propagating your data landscape.

·        It leverages the governance framework for quick wins around prevailing quality issues.

·        It helps promotes a formalized quality management agreement as SLAs(Service Level Agreements) between data providers and consumers and fosters data quality certification, instilling a culture of trust around data asset for innovation growth and analytics.

In a nutshell, you should never kick off a Data Quality Management initiative without embracing a Data Governance adoption. Actualizing and sustaining optimal quality in your data asset requires a governed, trusted business environment alongside your data quality initiative.

The primary goal of Data Governance is to ensure the highest degree of data integrity across your enterprise data asset. Achieving this goal will help your organization fully harness the full potentials in your data asset. You will be creating a sustainable trusted, business-ready data environment to help fuel and optimize all your current and future competing data demands. You will be positioning your organization for better success as you start lowering the operational cost of data fix and cleansing often incurred yearly in the absence of a governed data environment to support a Data Quality Initiative.

Need some help harmonizing your Data Quality and Data Governance adoption?

Book a (free) discovery call with me to discuss your challenges, and we can explore some simple strategies to actualize your desired goals.

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