Data is the language of business. Indeed, for many observers, data is a new type of business, that is commodity, tool, product and so much more. The volume and importance of data grows exponentially with each passing day. Businesses increasingly rely on data to drive their decision-making. McKinsey estimates that data-driven businesses outperform rivals by 2300% in customer acquisition, 900% in customer retention, and 1900% in profitability. However, with that growth comes complexity, and issues of incomplete and inconsistent datasets. Ensuring the integrity of your data is important if you are to thrive in this era of data. Poor data integrity can hurt the bottom line. Businesses spend between 10% and 30% of their revenues trying to achieve data integrity. However, many businesses fall into pitfalls as they navigate through this journey. Heres how to avoid those pitfalls.
What is Data Integrity?
Data integrity refers to ensuring the completeness, accuracy and quality of data over time and in different formats. This is not a one-time action, it is a constant and evolving process.
Although data security is related to data integrity, the two are not related. Data security contributes to data integrity by protecting data from internal and external threats and maintaining user data privacy. The threats that data faces are from human error, inconsistencies in the different formats, collection errors, and cybersecurity or internal privacy breaches.
Overall, data integrity encompasses physical integrity, or, in other words, storage; logical integrity or completeness, accuracy and quality; and compliance, such as keeping it within regulatory requirements such as GDPR. Todays distributed data systems present us with a unique problem: in order to gain in performance, they have reduced built-in support for ensuring logical integrity. Consequently, we have to develop novel ways of achieving logical integrity.
Data integrity helps businesses make better decisions, improve performance, and become more robust and accessible.
Avoiding Pitfalls
Assess accountability: you have uniform standards, otherwise your data system will have inconsistencies. To get there, there has to be accountability regarding data management. Accountability means knowing who is responsible for data integrity.
Be consistent: Its surprisingly common for businesses to have out-of-date and overlapping systems. Consistency is crucial here. You have to have standardised formats for specific types of data. Everyone has to work from the same relevant dataset. Inconsistency reduces data quality because the duplication of records or even inaccessibility of data, makes it impossible to ensure it.
Complete Records: Incomplete or inaccurate records are a nightmare and it becomes harder to spot the more data you have. A common solution is to unify data from different systems, but this has the disadvantage of masking errors and burying inaccuracies. Your data has to be complete and capable of meeting future demands.
Audit your data: We have stressed that data integrity is not a one-time event, it is a constant and evolving process. A key aspect of this process is trying to make sure that your data integrity is not diminished by anything you do today, even if that thing improves todays data integrity problems. Mistakes are hard to unwind and errors that are allowed to build up can be very costly to deal with. So, you need to have an audit system in place to track your data, see when changes were made and by whom. You also have to review your audit trails to see that they are fit-for-purpose.
Implementation
As you map out your process with flowchart symbols, it’s tempting to think that an understanding of what needs to be done is the natural place to stop. It isnt. Implementation is necessary. You need to develop a global plan for data integrity, a plan built on four things:
- Investment in integration: investing in integration will save your business money and time in the future, when your datasets are much bigger. Embrace solutions like ETL applications, and data preparation to achieve consistency through better organized data, and cleansing it to eliminate inconsistencies.
- Get a Data Steward: Appoint a data steward for specific datasets, or the entire organizational dataset. Provide data stewards and employees with periodic training, so errors are minimized. Create an accountability and data management system. As your datasets grow, you will want to create a data catalogue to enhance accessibility and build trust.
- Monitor and validate audit trails: monitoring audit trails is essential for being able to quickly fix any errors that arise. This process will ensure that the data the business relies on can be validated.
- Adopt an Empiricism: empiricism is about testing and iteration. As we said above, you need to regularly review your audit trails to keep them fit-for-purpose. You cant assume that your data is accurate, complete and of requisite quality. You should remain vigilant and skeptical.
Conclusion
Data integrity requires keeping on top of changes and quickly dealing with issues as they arise. With the right systems, your business will be able to maintain the quality, accuracy and completeness of data, so that your business can make the best decisions.
