Introduction

Data is the new buzzword. And Data Quality is on every conversation chart of organizations who are struggling with a humongous amount of data and are at a loss on how to manage it.

While organizations generate and have access to tons of data, both internally and externally, few have truly managed to understand how to leverage the data and turn it into an enterprise asset.

Striving to stay competitive, every organization is aware of the potential value of data, and the magic that can be created through it. However, most organizations are faced with challenges related to the quality of the data the data they hold. And these challenges pose hurdles in creating the required insights to achieve impactful business outcomes, forcing stakeholders to look for quick fix reactive solutions that can be implemented to accrue quick wins.

Eureka… let’s fix the quality of the data

The era of quick wins and quick fixes have thus entered the arena of Data Management. Most stakeholders spend less time on the problem as the focus is always on the solution. The need to get to the root of the issue, find the real challenges and prioritize the same, before embarking on the solution seem to find no place in this ‘instant’ world that organizations operate in. And deadlines of managing transformation projects like migrating to the cloud, creating a Data Lake, managing Big Data initiatives only compound the situation further, leaving less time for strategizing and pushing stakeholders towards a solution.

One size fits all solution – leading to chaos and failure

Addressing the quality of the data, without really understanding Data Quality is the chosen path. And this works out to be a recipe for disaster. While organizations embark on the journey towards data quality (more as a project than a program), without really understanding the stages and nuances of Data Quality, and its interlinkages with other foundational and management blocks of Data Management, namely, Data Strategy, Data Governance, Metadata Management and Master Data Management, the solution is set up for failure.

Data Quality is many a times, assumed to be a straightforward process, where a bit of profiling, a bit of data quality rules fixing or transforming will work wonders in improving the quality of data. This is the first point of failure – the lack of time given to understand the end-to-end process of Data Quality and not creating a strategy to address the real issue.

Managing the quality of data involves several steps, and it is an ever evolving and continuous effort that cannot be fixed by a mere technology-based project or intervention. And more importantly, Data Quality cannot be fixed or improved in a silo. Data Quality needs Data Governance. Even though both use different frameworks and attack the data and manage the data problems in their own way.

The synergetic relationship is all important to comprehend

Data Quality and Data Governance are thus synergetic and interdependent in their relationship. And there’s no better way to underrated this than through an example. If you send your child to pick a pizza, chances are you will instruct the child to go to a particular restaurant and pick a pizza that you like, with the toppings and bread choices that are to your taste. There is a process that the child will follow, which you have put in place through your experience of picking a pizza that is enjoyed by your family. If your child does not follow the process of picking the pizza the way you like it (say he picks a regular crust instead of a thin crust, and adds olives as a topping, which is not liked by anyone in the family) then you will not approve the same and reprimand the child. This process and practice of getting the right pizza and not approving the deviation is what Data Governance defines and sets in place.

Now let’s understand Data Quality with the same example. In the pizza that your child purchased, there are some leftovers, which the family would like to eat the next day for breakfast. Hence, the pizza goes in the fridge. For it can be used the next day only if it is refrigerated. If the pizza is left out in the cabinet, it will spoil overnight, and will not be fit for consumption. This is Data Quality. Data Quality establishes the system of keeping data usable and fit for purpose. Data Quality works on the dimensions (accuracy, consistency, completeness, timeliness, and others) and the thresholds that are set by an organization (accuracy of 95% or completeness of 85%) to ensure data is clean, usable and can be leveraged as per purpose.

Here's a Point of View on how to tackle this intricate interdependency

Having seen the interdependency of Data Governance and Data Quality and having gained widespread experience in various data management programs, it has been observed that most organisations are willing to invest and work towards improving the quality of their data to make it fit for purpose and thus leverage data to create business outcomes.

However, many a times to improve the quality of the data, organizations move ahead with pointed technology-based solutions instead of taking the longer route to fix the issue through creation of a solid governance foundation.

While governance is recognized as an important pillar in the data management journey, the pursuit for quick wins and even quicker results from data management programs ensure that the foundational pillar is placed on the back burner, thereby reducing the efficacy of most data management programs.

As highlighted in the example above, Data Governance sets the boundaries and standards to manage data. If this pillar of data management is overlooked, chaos will reign. Each stakeholder will follow their own principles and standards to manage data and the lack of processes will result in users’ sourcing and amending data as per their knowledge and requirements. And thus, the quality of data will be compromised and difficult to manage. Even using pointed solutions to fix the data quality issues will not work in the long term.

Hence, it is important that organizations first put in place a best-fit governance layer, based on a robust and well-articulated and published data strategy. Roles and Responsibilities definition, along with the minimum standards, policies, processes and procedures, and a well-rounded metadata management approach and plan, along with the data quality thresholds and KPIs, with informative dashboards and scorecards are critical success factors in the data management journey. Ensuring these foundational pillars are in place will result in data being as per the required standards, and fit-for-purpose, thereby enabling organizations to use data to create meaningful insights and derive the desired business outcomes.

So, in this chicken and egg story, if the egg or even the chicken came first, it most definitely has to be Data Governance!