Tired of a Lack of Data Accuracy and Rarely Seeing the Single Source of the Truth?
Posted on Apr 15, 2021 by Ian McGowan
Poor data quality is an ongoing headache for a majority of C-level executives. That being said, how can they make valuable informed decisions without accurate reporting? Scattered values, outdated records, and inconsistent data formats and standards all impact the quality of your data.
Are your operations suffering as a result of inconsistent and incomplete data? How can you forecast with certainty, manage growth for your business if your data is inaccurate?
Processing data of poor quality can lead to large-scale impacts such as revenue losses, increased costs, and even reputational damage. Our clients previously had similar challenges, and here are the six dimensions of data quality we recommended they consider:
- Accuracy. The data you hold must be accurate and verified. You don’t want “opinions” where the data is created by a single source—without some viable comparisons or verification, how can you truly believe and depend on the data? For example, a sales report that does not match with your billing report from your finance department.
- Consistency. Do you continue to send out promotional messages to a customer that has already unsubscribed from your marketing emails? This is where consistency comes in: all data containing the same information must be in sync with each other across all your systems.
- Completeness. This doesn’t necessarily mean that each and every piece of data must be collected: it simply requires that the data you have must be comprehensive and able to give you the insights you need. Missing data is not an issue here, as long as it can easily be mitigated and the information is usable.
- Uniqueness. There must only be one dataset representing a single object or event. Any duplicate representations may affect your data analytics and yield inaccurate results. One example would be having different customer records for a single person: to address this, there must be an additional routine to detect duplicates.
- Timeliness. Making real-time business decisions will require insights from real-time data. Put it this way: the figures you plan on presenting at a Monday meeting will be outdated if you don’t have all the sales data from the previous week. You need an accurate single version of the truth to give you the assurances to make informed decisions for your business.
- Validity. There are different sets of conditions that a dataset needs to satisfy before being considered valid, and it varies depending on the field. But, to put it simply, all the data you currently have in your database must be traced and connected to other data points in your business.
Based on our experience in processing large volumes of data from global organisations, we need to follow these six dimensions to deliver the required SLAs. Our solutions remove the issue of processing inaccurate and inconsistent data, therefore helping our clients make the correct informed decisions for their businesses.
Ask yourself: how can I be absolutely sure that my data is accurate, consistent, complete, unique, timely, and valid? Our data experts can help you measure the quality of your data and provide solutions where there may be some gaps.