CSR Data Quality – Common challenges and improving techniques
One of the main challenges that face CSR and sustainability managers today is being able to collect and manage enormous amounts of data whilst guaranteeing data quality.
CSR and sustainability managers need to first and foremost address their data quality challenges in order to be able to interpret and communicate data effectively and accurately to customers, investors and stakeholders – individuals who are keen to make informed decisions and to understand more about a company’s commitment to social values.
Missing and inaccurate data directly affects a company’s sustainability report and desired outcomes, negating all the previous hard work to communicate the wealth of data available into something tangible and meaningful.
Guaranteeing the quality of all your data is no easy challenge, but with a couple of pointers you will be able to significantly improve – and be confident about – the quality of your data.
So let’s start with a bit of necessary basic knowledge.
Data is considered to have six quality dimensions:
- Completeness – Ensuring that no data is missing from the data set, as any gaps or missing information will affect the desired outcome of all operations.
- Accuracy – The degree to which the data reflects the real world, consisting of trueness (how close the measurement is to the true value) and precision (level of accuracy applied to the data).
- Uniqueness – Avoiding any double entry or repetition of data for the same KPI, market, region or site in the data set.
- Validity – Reporting the data in the right way, such as using the correct unit of measure (grams, kilogram, kilometres, and miles).
- Consistency – When recording data points across the organisation, it is essential they have the same definition, covering the same thing in the same way every time.
- Timeliness – The data needs to cover the correct time period and needs to be reported and verified in time to meet deadlines.
In this article we aim to cover in details two of these six aspects: accuracy and timeliness.
Why do they matter? Because they are fundamental prerequisites when it comes to data analysis. Any error related to these two figures could compromise the integrity of your data and reports.
A high degree of accuracy can make all the difference and guarantee a strong and reliable dataset that will create compelling and trustworthy reports.
Three key ways to improve the quality of your data.
1. Implement a continuous monitoring process – It is important to:
- Check the data as you go along with the collection and managing process, rather than leaving it until the last moment when your deadline is close. Check if you have any missing data and chase those responsible down the line on a regular basis to avoid any surprises at the end of the year;
- Create a robust process to spot anomalies and to validate your data. A strong set of validation rules in place is necessary to improve the state of your data;
- Get all your CSR software potential by maximising the validation functionality within your own system. Most CSR software solutions have a range of options for validating data on entry. By setting tolerance, pre-set limits and by automating validation processes, CSR managers can get all their data checked and signed off without the need to overtake any extra arrangement.
2. Increase accountability for the data – It is important to:
- Create a clear chain of ownership for the data with different level of validation, clearly defining who is responsible for validating the data. For instance, data entered locally at any factory site, will then need to be validated by the regional manager and finally by the global manager who will validate for the second time and provide a final check;
- Differentiate entering and validating tasks. It is important that the person entering the data is also not the one responsible for validating the data too. Differentiating tasks will naturally provide a further level of control and will increase the chance to have accurate data from the very beginning;
- Improve the transparency of the data. Share the data and make it visible throughout the whole organisation, making everyone feel involved and up-to-date with what you are trying to achieve.
3. Engage with everyone responsible for the data – It is important to:
- Make it easy to report or validate on time. Reporting and validating processes can often look and be quite complicated. By making them easier and more intuitive you will increase the chances to get reliable data;
- Show the data in use by creating graphic representations, making them visible and engaging to all employees;
- Involve everyone in the reporting process. Again a visual representation can definitely help.
For the past 10 years SustainIt have been helping companies and corporations all over the world to improve the quality of their data and achieve their desired outcomes.
Our DataWise service supports businesses, walking them through every aspect of their data collection, management and reporting, and ensuring a deep understanding of the fundamental steps and requirements that need to be met in order to produce a consistent and accurate CSR report.
If you would like to have a chat with us and find out more about how we could help in your specific case, do not hesitate to give us a call on +44 (0)117 325 4168 or drop us an email at firstname.lastname@example.org.