Data is critical for informed decision making
Data is critical for informed decision making, strategic positioning, problem solving and improved organisational processes. The past 10 years Data Science has been used in the distillation of data to develop business intelligence that enabled visionary strategic responses. We have seen this applied in targeted digital marketing where it appears like “the big brother” is listening and risk management responses to cyber-attack.
Organisations’ use and management of ‘data’ varies due to size and complexity of operations. This difference can impact competitiveness and business sustainability. It is critically for sustainability that organisations understand its data intelligence and data risks, that is, they need to understand their data maturity.
What is Data Maturity?
Data Maturity is a measurement of an organisation’s awareness, management, and utilisation of data, which morphs into understanding data risks and data intelligence. It is a journey, and therefore it is important to map-out where you are in the data distillation journey. Data maturity provides insight on whether the organisation is using and managing data resources effectively to achieve its goals. It is important to note that as organisations evolve, so is the maturity level. Therefore, a regular assessment of the maturity level is necessary to ensure that data resources are used effectively.
Data maturity level ranges from is the organisation using data for regulatory reporting purposes (historic or reflective), as a risk response or to achieve organisational objectives. There are many elements that influence an organisation’s maturity level, that is, location in the journey:
Data Governance
Data governance is the overall oversight of data by the board and its committees including assigning responsibility for specific data activities. Data governance includes policies and procedures put in place to address all organisational data activities, like data controls, storage, usage and collection. This will include any frameworks for specific data activities, like a framework for data storage. Governance response should go beyond ordinary policies like data privacy and security.
This is a good indicator of an organisation’s data maturity as it shows that the organisation is data aware. The structure of the data governance will give more insight on the organisation’s management and monitoring of data risk and usage of data to gain business intelligence.
Data Strategy
Data exist and it is critical in gaining competitive advantage. As part of the governance, the organisation should adopt a strategy on how to use data. This will mean that the organisation understands:
- What data it needs to inform decision making;
- What data it needs to drive strategic positioning; and
- what data it has on hand or it needs to acquire.
Once a data strategy is developed, it will also inform what data resources or enablers the organisations needs to have in place, that is, people, infrastructure and tools.
Data Risks
Data is currency, and it is susceptible to several risks including risk of theft, fraud due to manipulation, data losses, etc. It is important for the entity to have a data risk management plan. The data risk response will start with an assessment of data risks so that an adequate risk response plan can be developed. It is important to note that all data activities carry risks, for example in collection or capturing of data, there is a risk of human errors and not having controls in place mean that the decisions will be based on incorrect data. The risk assessment process will of course rate and measure all risk so that critical risks are prioritised.
A data risk management plan is a good indicator of the data maturity level.
Use of data in risk management: Data is a useful tool for organisations risk management practices. It can be used to gain insight on risks and inform risk responses.
Data Enablers
All functions contribute towards the data universe. There are multiple data activities carried out by different people through-out the organisation. Therefore, it is important to ensure that all role players understand their contribution and the impact of their activities in creating reliable and useful data inventory. Therefore, there are many enabling factors to reach a high data maturity level (DataCamp (datacamp.com) refers to these as key levers in its data maturity framework (Kosourova, 2023)):
- Infrastructure – do you have the right infrastructure for capturing data, running analytics, and storing data?
- People (do you have resources with knowledge of data science and data analytics? Are you people aware of their contribution to the data inventory? Are they integrating data insights in decision making?
- Tools (what tools do you have in place to analyse data and present it in a useful manner)
These are resources that create an enabling data literacy culture.
It is important to note that organisations can perform their own assessment of their maturity level using any of the existing maturity models, which are available online. This will be useful in ensuring that the organisation is ready for a data drive future. Organisations should aspire to obtaining assurance from an auditor on their data maturity level. This will add value as experts could provide insights on data risks and opportunities.
Bibliography
Kosourova, E. (2023, May 8). Blog | what-is-data-maturity-why-it-matters. Retrieved from datacamo.com: https://www.datacamp.com/blog/what-is-data-maturity-why-it-matters
Author Bio:
Matlhogonolo Nolo Mogapi is the Managing Director of Bagaka Group, an audit, risk and accounting firm using data analytics to enhance audit quality. She is a Charted Accountant (SA) and a Certified Internal Auditor with a Master of Business Administration degree from GIBS. The result of her MBA research was a publication of an academic article with two others in the European Business Review. She is serving a second three years term as a member of the IRBA’s Committee for Auditing Standards.
