Driving value from data is not without its challenges. Poor quality data can often inhibit the potential positive impact that data can have on an organisation. In this blog post, we take a closer look at the world of data quality and integrity and explore how organisations can overcome many of the challenges associated with poor quality data.
Why is data quality important?
In their 2021 CPO Survey, business advisory firm Deloitte found that data quality was the biggest challenge organisations faced with regards to adopting digital technology. The report goes on to state that “Increasing the number of analytic engines consuming bad “fuel” just creates more waste and polluted processes… Process Quality and Data Quality go hand in hand and generating quality data is an imperative that can no longer be ignored”.
Good quality data ensures that the decisions driving your business forward are based on the most accurate and up-to-date insights and intelligence and helps to improve operational performance by minimising errors.
What causes poor quality data?
There are a number of factors that can impact the quality of an organisation’s data.
- Siloed Data Sources
Organisations are often working with multiple data sources, scattered across various locations and platforms. Data siloes result in poor transparency, access, and efficiency. Siloed data sources can often contain duplicate datasets, resulting in inconsistencies.
- Inconsistent Formats
Non-standardised formats can result in a range of quality issues that can present an inaccurate view of the data. For example, one dataset might use “DD/MM/YY” date format, while another use “MM/DD/YY”.
- Manual Errors
Organisations that manually process their data can often encounter data quality issues that have occurred as a result of human error. General errors such as spelling mistakes or incorrect entries can drastically impact the value of the dataset.
- Incomplete Datasets
Working with incomplete datasets can often result in inaccurate conclusions, due to the omission of critical information.
- Poor Data Strategy
In a recent blog post, we spoke about the importance of data strategy and its importance in driving value from data. Failure to implement an effective data strategy can often lead to many of the issues listed above.
Improving your data quality
There are a number of steps that organisations can take to address their data quality issues.
- Understand Your Business Objectives
Understand how your business is using data and how it impacts operational performance and decision-making. This will help to establish clear priorities and objectives with regards to your data quality project.
- Identify Existing Issues and Potential Causes
Profile your data to identify any existing issues and their potential causes. Some points to consider when profiling your data include: Where is your data stored? How complete is your data set? Is the formatting consistent?
- Select the tools needed to address these issues
There are a variety of tools and processes that can be used to address data quality issues. Certain data quality issues may require multiple actions while others might only require one. Such processes include Data Deduplication, Standardised ETL, Data Enrichment and Data Augmentation.
- Establish a Data Strategy
Maintaining data quality is a recurring ongoing process. In addition, to regularly review the quality of their data, organisations should take steps to create a data strategy that ensures the consistency and accuracy of their data is maintained on an ongoing basis. An effective data strategy ensures that there are processes and standards in place to prevent data quality from deteriorating again in future.
Experts in data
Analytics Engines offer end-to-end guidance, consultation, and support in response to your data challenge. We are experts in data, and we work in close collaboration with you and your team to understand your specific objectives and challenges. We develop solutions that provide your business experts with the resources they need to make critical business decisions more effectively.
If you’d like to improve the quality of your data, arrange a no-obligation introductory call with one of our data experts using the form below.