According to Steve Jobs, “Quality is more important than quantity. One home run is much better than two doubles.” And this has never been truer than when discussing and managing the data your organization creates, receives, and monetizes for specific outcomes.
All data is not created equal! However, the data's value is specific to your business's circumstances and your current critical needs. For example, you are about to book an order for 100,000 widgets deliverable in 30 days; while it is important to know that you have the raw materials to produce the widgets, at the moment, it is more important to know that you have the plant capacity to produce them in 30 days. Without the ability to produce, all the raw materials in the world won’t help you.
Even more important is, “Do you trust your data?” For example, are you sure your inventory system is accurate, and has your manufacturing system been updated in real-time to reflect two machines out of service?
While data is important, I defer to Mr. Jobs, who says that “the quality of your data is more important than quantity.” Quality data means better business intelligence on which to make better decisions, and better decisions equal higher growth.
So, where do you start with achieving data quality? This blog is intended to provide you with general insights and specific actions that you can take to build a data quality framework to help your business manage data quality like a pro for better business outcomes.
What is Data Quality?
According to Techopedia, Data quality (DQ) is the degree to which a given dataset meets a user's needs. Data quality is an important criterion for ensuring that data-driven decisions are made as accurately as possible. High-quality data is of sufficient quantity—and has sufficient detail —to meet its intended uses.
Additionally, data quality consists of the following five (5) key elements:
Data Accuracy: the extent to which data represents real-world events correctly.
Credibility: the extent to which data is considered trustworthy and true.
Timeliness: the extent to which data meets the user's current needs.
Consistency: the extent to which the same data occurrences have the same value in different datasets.
Data Integrity: the extent to which all data references have been joined accurately.
Who “Owns” Data Quality in My Organization?
This question represents one of the most difficult aspects of data quality. Because the answer is everyone! Yes, everyone in your organization owns the quality of the data they provide, encounter, and utilize in their roles. For example, the sales team owns the quality of the data associated with their prospects and customers. Marketing owns the quality of the data associated with your brand and the markets in which you operate. Finance owns the quality of the data that represents your financial conditions. But each has a responsibility to validate the data from the other and report suspected issues.
Let us be clear, you cannot assign every person in your organization to data quality management. However, each person is responsible for the five key elements of data quality listed above and reporting data issues when they are discovered.
Since not everyone in your organization can be individually responsible for data quality, the business must build data quality into the capture and management of data at its source. It is easier and cheaper to make data corrections at the source than at the end when the consumers are interacting with the data. Automated data quality systems can be implemented to validate and correct data as part of the capture, or ingestion, process.
Additionally, assigning data stewards that have delineated responsibility for the quality of specific data sets ensures that a second or third set of eyes are confirming the five key elements of data quality embedded in your process.
How Does Poor Data Quality Impact Business Decisions?
Ensuring data quality builds trust and enables your business to operate at the highest caliber possible. When information consumers within your business trust your data quality, they can make better, more effective, and more efficient decisions. Combining these three decision elements will catapult your business to new levels of productivity and profitability.
Using our example above of the 100,000-unit widget order, when your manufacturing department knows it has the capacity to build to order and inventory management has the insight into the raw materials necessary, then the best decision can be made to meet the customer’s needs. This ability to meet your customer’s needs builds loyalty and increases brand value in the industry. And who doesn’t want that for their business?
Building a Data Quality Framework
So, how do I manage data quality in your business? Methodically! There is no magic wand to achieving data quality. A coordinated effort must be made to define data qualitymetrics and deploy a data quality strategy. The strategy must have executive-level sponsorship, middle-management adoption, front-line execution, and enterprise-wide communication. Data quality can be achieved when these four aspects are built into your data quality strategy.
Your strategy must incorporate a data governance program that combines people, process, and technology.
Sponsorship, adoption, and execution require the people in your organization to understand the importance of data quality and to work toward a common result.
The processes within your organization must be evaluated and updated to ensure that data stewards are kept informed of the issues within their area of responsibility and have defined mitigation and corrective processes to follow to resolve data quality issues.
To make the process of data quality manageable, technology must be considered. Many tools perform data profiling and automated data quality checks and can even take corrective action depending on the autonomy you provide to the technology. However, even with technology autonomy, there are always processes and people supporting that automation.
Making Data Quality Standards a Part of the Business DNA
Human change management is likely the most challenging part of building a robust and sustainable data quality strategy within your organization. This is because people control the processes and technology implemented in your data quality strategy.
Participation, ownership, accountability, and education will be critical to making data quality part of your organizational DNA.
Ensuring that all aspects of your business see your data as a critical asset and treat it in the same manner as cash, or other assets, goes a long way in building a sustainable data quality solution.
Redundancy is also important in the sustainability of data quality.
Implementing continuous improvement for your processes and technology will ensure that you frequently validate these components of your solution.
Ensuring that you have the depth and breadth of human resources needed to meet the challenges of the complexity that can exist within your data quality solution is also essential.
Incorporating all of these features and ensuring ongoing validation of your solution will create the data-quality DNA your organization needs.
What is the ROI of a Data QualityInitiative?
This is a question often asked and seldom answered. Data quality is like insurance; you complain about paying the premium until you actually need the insurance.
It is difficult to place a quantitative value on data quality. What is the loss of revenue, profit, customer loyalty, and market reputation in being unable to deliver on the 100,000-widget example? Or worse, promising the customer that you can and then having to explain why you can’t? ROI can be very subjective in nature and not a good way to measure the need for or the success of your data quality strategy.
While the initial cost may be moderate, the long-term value will repay the investment many times over in both small and big ways. The sheer confidence and trust that your decision makers place in the information they receive will allow them to identify new opportunities with existing customers, or even better whole new markets, to expand your business and significantly increase your bottom line.
The decision to embark on a data quality strategy effort should not be made lightly. Ensuring greater outcomes for the business requires an understanding of the problem, its impact on the business, and a commitment across the organization to work together to solve the problem.
Generally, this is not an effort that can be undertaken with only internal resources. An old adage says, “you are never an expert in your hometown!” Those who know you best don’t see the full value of what you have to offer. Therefore, leading an internal data qualityinitiative can be challenging, and it is recommended that you find a data quality partner that can carry the banner or bring expertise to create synergy and collaboration within your organization to achieve your data quality goals!