Category: Data & Reporting

Having and maintaining excellent data quality is a goal for many organizations. Good data will make your decisions more sound, protect, and stabilize your business. Bad data may alert you to the wrong priority, show an inflated crisis, or even point to a problem without one. Understanding the state of your data and developing a plan to improve your data will improve your storytelling and give you more confidence in your decision-making and prediction accuracy. If the data tells you the wrong story, you are inappropriately allocating resources that could hurt your business or clients. 

Let’s start by understanding data quality basics. Regardless of the size of your data set or data system, these basic principles should be considered when estimating your data quality. You must understand that committing to improving your data quality, while necessary, is a major commitment. Data improvements are not a one-time project, but an ongoing effort to enhance your business infrastructure. The larger your data set, the harder the challenge is to clean. Many fear data quality because they don’t understand the data quality basics. Let’s give you some basic guidance. 

Data quality is broken down into six principles. Each principle is important and should be considered collectively. These principles all start with the letter “C”. 

Clean - Cleaning or cleansing is about detecting and correcting corrupt or inaccurate records. It is a column-by-column and row-by-row effort to repair missing data or discrepancies. The best place to start is to pick an area of your most critical data - data you use most frequently. Start with rows of data that are missing or blank. Fill in those empty holes. Next, clear the data in the same categories with spelling errors or old data categories. Update them to your new data standards (see the compliance section). Any data errors must be addressed and resolved consistently and ongoing for data to be clean.

Complete - Completeness refers to the comprehensiveness or wholeness of your data. For example, which data set is the most comprehensive if you have merged an old data set with a new one? You would need to bring the rest of the data set to the same level of completion as the best of your data set. Ensuring every data row has exactly the level of completeness as every data row. Completeness is not achieved until all rows and columns have the correct and appropriate data, including no missing or blank data. 

Compliant - Compliance ensures that sensitive data is organized and managed to meet all enterprise business rules, legal, and governmental regulations. Earlier, I mentioned your data standards. Your data standards may come from established industry standards or business rules you create around meeting these data basics principles. Review the various types of sensitive data you collect. How is it being collected, improved, and reported? Who has access to change the data set? You will want policies and procedures around data access, collection, and reporting. To help you begin to design a plan for data cleaning, most importantly, you want to detail by field name, type, and answer the data you collect. 

Consistent - Consistency means, regardless of the platform or formats the data lives in, all data reflects the same information across all systems within the organization. As part of your cleaning, redundancies need to be removed as a part of the preparatory step known as data normalization. The format of the data also must be your standardized format.

Credible - Credibility views the reliability of your data. In other words, the data must be perceived as verifiable as coming from a reliable source to be credible. To what extent can your data source be relied upon to ensure the data correctly represents your business status and client profile?

Current - Data is often time-sensitive, so it must be up-to-date across all systems, considering any changes that may render it obsolete or worthless. Integrations and APIs can help move data between systems. Caution - if your data is dirty, you are moving dirty data directly between systems. Try disabling integrations into you clean the data in your primary system before you enable your integrations. 

If your organization makes data driven-decisions, you must take the time to understand the current state of and improve your data to be seen as credible and current. While overwhelming, it is important to move your business forward. Start small and expand from there. Understand the state of your existing data and then create a standard. Next, choose a place to start to improve your data. 

Contact Pensivetastic today to discuss and collaborate on a path forward for your company and data. Supporting you is what we do. We’re here to help you get where you want to go.