Data Maturity: Decision Making
Where are we on our analytics journey? It’s a question we get asked all the time. In this series we will look at key questions to help you determine just this! We’ve graded common responses based on three levels of maturity: walk, run and fly.
How is data embedded in your current decision-making process?
Walk: Data is mostly used for reporting purposes, and feeds into performance reviews and activity planning.
Run: Data is embedded in the day-to-day activities and planning for the C-suite and we are looking at expanding it out across all departments.
Fly: Alongside data being used on a day-to-day basis, we have a data team and business analysts who help support more data-driven decisions.
See which answer most closely matches your current situation and scroll down for some crucial advice.
Your answer: Data is mostly used for reporting purposes, and feeds into performance reviews and activity planning.
Our advice: While reporting is of course a valuable aspect of data analysis, it is essential to think beyond this. Consider how data can be leveraged to optimise various areas of your business as part of a longer-term plan. Map out your customer journey and touchpoints and look for areas of key intervention where a data-driven approach might make a difference. What are the early signals on customers taking actions? What information from them do you need to better personalise their experience.
Your answer: Data is embedded in the day-to-day activities and planning for the C-suite and we are looking at expanding it out across all departments.
Our advice: When expanding your data efforts, it’s crucial to focus on one business area at a time. By concentrating your resources and efforts, you can iterate quickly, identify challenges, and deliver tangible results. Avoid creating an extensive list of data projects, as this can lead to a longer time-to-value. To prioritise your projects, it’s important to quantify the problems you aim to solve. Identify areas where data can have a significant impact, both strategically and operationally. Look for opportunities that can generate quick wins and demonstrate the value of data-driven decision-making. Some examples would include Cost of Acquisition, Dynamic Segmentation, User Retention, and Marketing Attribution. While your ultimate goal may be to have data feed into every department, by taking things in bitesized chunks, you can better demonstrate the value from data and justify more investment in this area. You also highlight this approach to stakeholders who may be reluctant to change.
Your answer: Alongside data being used on a day-to-day basis, we have a data team and business analysts who help support more data-driven decisions.
Our advice: You have everything you need in place to democratise data across your organisation. At this stage one of the most important aspects is buy-in from your C-suite management. Often we see situations in which data analysts present their insights but their recommendations are not taken on board by senior management, and so the organisation fails to become truly data-driven. Here are some key strategies to ensure senior management trusts the data:
Data Quality Assurance – Trust in data is essential. Ensure that the data collected is accurate, consistent, and reliable. Implement rigorous data quality assurance processes to identify and rectify any discrepancies. Regularly audit and validate data sources to maintain a high standard of accuracy. While there will often be a small discrepancy in numbers, this discrepancy should be consistent and explainable. (It might be for example two systems using two different time zones for reporting) A data dictionary is the foundation of all data quality assurance and governance!
Data Governance – Following a similar theme, as you grow it becomes increasingly important to build a robust data governance framework that defines roles, responsibilities and processes for managing data.
Consistent Reporting – Standardise reporting formats and metrics to ensure consistency across different reports and analyses. This consistency helps senior management easily understand and compare data from various sources.
Data Literacy Training – Provide training programs to enhance data literacy among senior management. When executives understand how data is collected, analysed, and interpreted, they are more likely to trust the insights derived from it.
Demonstrate Business Impact – Clearly articulate how data-driven insights have contributed to successful business outcomes. Highlight instances where data has influenced strategic decisions, leading to positive results. Tie data initiatives to key performance indicators (KPIs) and business objectives, demonstrating a direct correlation between data-driven actions and business success.
Monitor Usage – You can only go as fast as the slowest person. People fear change and are somewhat likely to slip into old ways of working. Monitor who is using visualisation tools, ensure that people don’t have their own spreadsheets and offer additional training and support to ensure that data is being utilised and understood.
If you want to understand where you are on your analytics journey. Our Data Maturity review offers a holistic and independent assessment of the strengths and weaknesses of your current technology, processes and people.