No one would expect an individual that has never exercised a day in their life and subsists on junk food to win the Boston Marathon. While we know this to be true, many organizations believe that gathering and leveraging low-quality data will suddenly provide keen insight and transform their business from average players to market leaders.
When we say this out loud, it sounds preposterous that any company would feed on insufficient data – or junk food – and expect to generate keen insight to help them make critical decisions and achieve operational efficiency. Therefore, data quality is essential to your business and must be managed the way an elite marathoner manages their training and diet.
What is Data Quality?
Data quality is the state of quantitative and qualitative information and its value when used for planning and decision-making. Acquiring quality data is why many organizations engage in data cleansing exercises, designed to ensure the data they’re dealing with is accurate, relevant, and consistent. Data quality can be measured in several different ways. The most appropriate ways to measure it involves:
Accessibility: Gaining reasonable access to data regularly will help your organization understand the operational condition of your business and its market. If data is not available to be processed or is not gathered consistently, your results will be insufficient for proper data processing and decision making. An automobile cannot run on fuel that is not available.
Completeness: To correctly leverage data for decision making, that data must be complete. When determining the methods and sources from which your data will be gathered, requirements must ensure that raw data is comprehensive and relevant. This principle is similar to attempting to build a house with incomplete blueprints.
Timeliness: Gathering year-old data to make decisions for tomorrow is an exercise in futility. Markets change, technology improves, and opportunities disappear. Making sure your data is gathered and processed quickly will help you and your organization make faster decisions within relevant timeframes and augment your competitive edge.
Accuracy: Ensuring your data is accurate is considered by many analysts to be the heart of the battle. If your mechanisms for gathering data ensure accuracy, whether from marketing campaigns, Internet-connected devices (IoT), or qualitative and quantitative studies, you can more effectively refine that data and put it to work via data visualization.
Steps to Improving Data Quality
Ensuring data quality doesn’t just happen. A strategy and ongoing effort must be implemented to ensure the data your organization is using is relevant, accurate, and complete. Highly competitive organizations investing in and end-to-end enterprise operational success platform have a keen eye on data quality. To be among those highly competitive organizations, you must take several steps to ensure the necessary data quality so that decisions are made with the right kind of information.
Define Data Quality
One department’s definition of data quality might be different from another department’s definition. Getting everyone to sing from the same page can be challenging but establishing key definitions and consistent methods of gathering clean data will mean high-quality, consistent data on the back end.
Ideally, establishing consistent methodologies and data gathering tools will be necessary if gathering qualitative and quantitative data. If collecting data from RFID tags and other devices, you must ensure everyone is collecting the same data the same way with the same consistency and frequency.
Collaborate with Stakeholder Departments
Gaining buy-in from other departments responsible for collecting data or scheduled to use the data regularly is a necessary first step. Getting buy-in from different areas can also mean securing the budget required to build and house quality data. It will be easier to gain operational support regarding resources if you can make a case for why and how different departments will benefit from high-quality data.
Establish KPIs Around Quality
When determining which data will be gathered, departments must understand what must be measured. Establishing standards around measurements and key performance indicators (KPIs) will ensure the necessary data is identified, collected, and transferred in a relevant way. While different operational areas have different objectives and requirements, the data gathered across those multiple areas should be consistent and relevant.
Once those KPIs are established, regular audits must be conducted to ensure accurate data is collected and shared tomorrow, next month, and next year.
Define Data Tools
There are multiple tools available to gather, process, analyze, and report data. While there are several mechanisms to choose from, the overarching approach can be segmented into two general categories: tools that gather data and tools that process data.
There can (and should) be more than a single tool that gathers data. This approach will be a natural byproduct when collaborating with different departments responsible for collecting the data. Survey and market study tools are ideal for gathering data related to customer and stakeholder preferences and market opportunities. More automated methods of gathering data, including IoT devices such as apps and RFID tags, are an excellent way of collecting real-time data in the field. Both methods can be calibrated to ensure relevance, even if the raw data is different.
Methods to analyze and report findings are available in the form of business intelligence or data visualization tools. Mechanisms that can take and process quality data from several different sources while providing easy-to-understanding dashboards are what competitive organizations rely on to make the best decisions for their business.
Implement Data Governance
Putting several requirements in place regarding data quality will begin your data preparation phase and eventually lead to data governance, and finally, to data integrity. Implementing data governance ensures that organizational areas don’t gather and refine data irrelevant to the overall governance framework and establishes a culture that values data quality. The practice of collecting, processing, analyzing, and leveraging data by specific stakeholders and decision-makers will enable businesses to identify and achieve particular objectives and establish a culture that values data quality.
At the end of the day, an organization’s decisions are only as good as the data it analyzes. The data it analyzes is only as good as the data it collects and refines. In this sense, data validation is critical, as the information that goes in the front end is, ultimately, what comes out of the back end. Defining and ensuring accessibility, completeness, timeliness, and accuracy will result in clean data an organization can regularly rely on to make critical decisions to refine the operation, tap new markets, maximize marketing campaigns, generate more revenue, and grow the business.