In today’s data driven world, high quality data is key to success and essential in driving client communications, strategic planning, and reaching sales targets. To achieve a high-quality data standard, there are 5 key components which are paramount in preparing for data analysis and ongoing maintenance: accuracy, completeness, reliability, relevance, and timeliness. Here at Lloyd James, we take great pride in our data quality control and believe that data is only ever data, unless it’s high quality, accurate and brought to life in the right way. Click here to see how we can improve your data quality.
Why is data quality important?
Using poor quality data can have significant business consequences leading to inaccurate analytics, operational disasters, and failing business strategies. Data quality problems that are not rectified can also cause expensive mistakes such as products shipped to the wrong address, lost sales opportunities, and fines for improper financial or regulatory compliance reporting. Given the huge amount of information that businesses now possess, analytics will only ever be as good as the data supplied in the first instance, so ensuring a high-quality data input is fundamental to achieving and developing a better understanding of your business, customers, and the wider market.
What are the most common data quality issues?
There’s no point in reporting on or contacting customers based on data that is wrong, and data can very quickly become inaccurate with an average 30% decay rate each year. Human error is typically the root cause of inaccurate data in CRM systems, and it is also one of the hardest data quality issues to detect. Research by the Royal Mail suggests that poor quality contact data is responsible for the loss of 6% of annual revenue for companies that fail to recognise the importance of data accuracy, therefore data hygiene is essential to any data management strategy. Why compromise revenue & reputation by continuing to use inaccurate data. We make the process easy.
Identical copies of the same record can take a toll on your CRM and computing systems and may also produce incorrect insights when not acted upon. Again, human error could be the main cause in creating duplicate data, but it could also be an algorithm that has gone wrong. A data deduplication tool will resolve this by using a mix of algorithms, analysis, and human intuition to detect possible duplicates based on match level scores. The dedupe tool will identify when records look like a match and when to remove them.
If data is not organised in a pre-defined manner or if it’s stored in different formats, systems may not recognise items belonging to the same category resulting in missing or differing variables. For example, if there is no postcode included in an address, the rest of the details may not be of interest as the geographical location cannot be determined. By using a data integration tool, this will help convert unstructured data into a structured format ensuring consistency throughout the database.
Human error can have a detrimental impact on business operations and is perhaps one of the biggest challenges to achieving high data quality. Even your most skilled employees can make errors such as typos, and misplaced information, leading to various data quality issues, and even incorrect data sets. The use of AI based systems, as well as more complex algorithms, will help to reduce this issue within businesses and ensure that human error is minimised.
Failure to comply with data security and compliance requirements can result in substantial fines and, more importantly, loss of customer loyalty. GDPR does not define security measures that companies should have in place, but it requires you to have a level of security that is appropriate to the risks presented by your processing. Guidelines provided by regulators present the need for a robust data quality management system and we can help you improve on this.
In most CRM systems errors can appear even with close supervision. Common data errors include formatting issues, misleading column headings, and spelling errors which can go undetected. This ambiguous data can introduce inaccurate reporting and analytics which can result in catastrophic business decisions.
By tracking down issues as soon as they arrive with autogenerated rules and predictive DQ resolves this ambiguity. By doing this it means that your data will continue to be high-quality, and your analytics will reveal trusted outcomes.
According to research, most companies use only 20% of their entire data pot when making business decisions, leaving 80% to sit and go cold. Bringing this hidden data together is valuable to understanding customer behaviour, as customers engage with companies in various forms such as in-person, by telephone and online. Although using data which indicates when, how, and why customers interact is of utmost importance, it’s very rarely utilised. We’re on a mission to change this and our Resident database can help provide more insight into customer data you have.
Poor data recovery
Research shows that employees spend approximately 30% of their working hours just looking for the data they need to action business processes. And what’s worse is 40% of searches made my employees result in not finding the requested data in the first place.
These are just some of the most common data quality issues found in modern businesses, and to achieve high quality data, quality data management is key. Lloyd James can help you implement a proactive data quality solution to address these common issues, ensuring mistakes are minimised, clear procedures are followed, data consistency is maintained, and data quality is frequently measured.
Contact our team of experts today to discuss your data quality concerns.