The Data Framework
A unique approach to change
Our approach to data-driven business transformation varies from others – for instance the DAMA-DMBOK framework – in the following ways:
- We focus on the continuous link between data activities and tangible results
- We ensure consistency between the necessary components (both strategic and operational pillars) needed to work in a data-driven way
- We also ensure consistency in data-driven efforts, helping our clients to invest ‘just enough’ in a breadth of capabilities instead of overinvesting in only one component
- We view data as a crucial component of the execution of processes within every organisation
This final point is essential. An effective and efficient business must make demands on its data, viewing it as an essential business tool for creating value and meeting organisational requirements. By adopting this attitude, you can deliver improved products and services that attract and retain customers, ensure more efficient processes, benefit from better and faster decision-making, and increase agility. The importance of data-driven work might vary from organisation to organisation, but to some extent it applies to every organisation.
The seven stages of our data framework
So how can you begin to generate value from your data? What do you need, where should your focus be, and what first steps should you take? Our data framework is designed to help you answers these questions, with a consistent focus on the creation of business value and the assurance of consistency. The framework consists of seven mains elements which we’ll discuss below:
- The (business) strategy
- The data strategy and priorities
- Understanding and prioritising data
- Performing data management and analysis
- Creating business value
- Data policy and principles
- Building capabilities
The value of the framework lies in the coherence and alignment of these different parts. And the fact that any efforts around creating data-driven ways of working are directly aligned with the creation of new business value. This means that all data activities must demonstrably contribute to your organisation’s long- or short-term goals. By ensuring this, it automatically becomes easier to keep track of whether or not sufficient value is being created. All of this is encompassed by the first stage of our framework, which covers business strategy. At this stage, it’s essential to determine which goals within your strategy require data-driven processes. Once this is established, the next component is building a data strategy to ensure you can use your data to realise those goals.
The middle layer of the framework emphasises the importance of understanding your organisation’s data. To successfully manage data sources, you need to know what your organisation has, where it is stored, who it is shared with, and how it can be obtained. After this, the framework focuses on the continuous implementation of data management and analytics. When data is critical to your processes, it must be continually managed. This doesn’t mean one-off projects to improve data quality or ensure compliance with legislation, but instead focusing on being in control of your data at all times. Analytics then ensures that your data is translated into the kind of insights that fuel decision-making. This is the very essence of data-driven working – using data to make better decisions, faster, whether that’s through automation, human intervention, or a mix of both.
It’s important that you continue to make the link between your data activities and the value they deliver at this stage. For instance, are your customers more satisfied? Are your operational costs lower? Has compliance improved? Doing so will create awareness and willingness to invest throughout the organisation. But you must make sure that investments remain well balanced with these returns. The last two factors of the data framework support the above components, providing data policies with associated principles and building the necessary capabilities. A data policy with principles is important to ensure that data management and analysis are performed in an efficient and consistent manner. And that the right tools are available to those that need them. It is of course essential that your organisation has the right capabilities. This includes data governance capabilities, people with the right knowledge and skills, and modern technology such as a data platform.
However, these capabilities should not take priority over tangible results. We often encounter organisations that begin by building a huge ‘Christmas tree’ with roles and functions around data, or those that invest substantially in a data platform right out of the gate. As a result, returns are often unclear or disproportionate. This leads to a continuous need to defend the usefulness and necessity of the data platform. Despite the fact that organisations that strive to be data-driven should be looking to invest substantially.
The moral of this story is that a data-driven change process must be completed step by step. You should continually monitor the results of your investments and create awareness and support throughout the organisation. And start small. This rule applies not just to investments but to building up competencies, too. Make sure there is ‘just enough’ in place, in terms of policies, structure, and knowledge to start with your initial activities – and then scale up when you begin to see results. The overall approach supported by our framework guarantees that your efforts will be tested for their contribution to your set goals, so even if things aren’t going according to plan, you can make iterative adjustments as you go. Would you like expert advice on your data-driven transformation? Get in touch to see how we can help.