This would have to be one of the most asked questions of a data consultant.
99% of the time we enter a workplace to improve a client’s current data environment. They have identified the need to do things differently with data. However, there is no one size fits all solution.
So where do you start?
It really depends on what you are aiming to achieve, the level of data maturity within the organisation, the culture, the immediate priority, and alignment with the long-term plan. However, there is a level of logic to be applied.
If everyone ducks and weaves when decisions need to be made or responsibility for errors needs to be assumed, you need to define decision rights and establish data accountability. When tailored to organisational requirements, data governance is a powerful enabler of business outcomes.
A lack of clear linkage between existing projects and activities and how they underpin what the organisation is aiming to achieve highlights a need to take a step back and focus on strategic alignment. Data should enable the achievement of strategic objectives. Articulating how this will be achieved is key to providing value back to the business and securing ongoing investment if required.
The management and protection of personally identifiable information (PII) is critical, make that a priority.
If you are struggling with the flow of data across your organisation or between systems, your data architecture may need updating. The data architecture should provide your blueprint for managing and integrating data assets and their associated data flows, as well as the minimum requirements for data structures in IT systems.
If you are looking to derive reliable insights from your data but are struggling to get consistent outcomes, the quality of data in the pipeline may need some level of remediation. The quality of data is foundational to reliable outcomes – without minimum requirements, there will always be a lack of consistency. Inconsistent data quality and availability can result in employees spending significant time on non-value adding tasks.
If you can’t find data, the handling practices such as classification, description, storage, security, and governance of data comes into question. These all have a significant impact on the ability to search and retrieve data efficiently, and when used appropriately, can deliver significant improvements.
If you are describing your core entities (i.e., people, location, products) differently or using data about the same entity from different sources with different results, analysis should focus on limiting duplication through the definition of master data entities and establishing a shared understanding of business terms, so that you can maximise data reuse.
When there is inconsistency with how data is visualised or interpreted, a focus on uplifting data literacy may be required.
This list is far from exhaustive, but it highlights how the approach to enabling your business through data differs depending on individual circumstances and pain points. This is not cookie-cutter territory, in our experience data management approaches tailored to organisational maturity are always most successful. Sadly, there is no silver bullet, but by defining the right journey for each organisation, capability will build over time, benefits will be realised, and funding can be maximised.