30.06.21

The 4 pillars to a forward-thinking data strategy

  • News

Chris Lewis, Head of Analytics at international law firm DAC Beachcroft, spoke recently about how analytics helped create new, innovative services for his organisation. Alongside his insight into how a data-driven culture can lead to the formulation of market-leading legal products, Chris also spent some time looking to the question of what next? When so much has been achieved, with a data-led mindset embedded across the business, Chris’s view on what the next stage of analytics maturity looks like is interesting to all of our customers, even those outside the legal sector. This is a written summary of the four pillars that Chris put forward as part of the recent Catalyst BI webinar that you can watch in full here.

1. Data literacy – Identify and give access to training for key heavy data users across sectors.
The workforce need to feel comfortable understanding and interpreting data. Not just that, but they need to understand what impact the data has and why it is important. This is essential to drive engagement with analytics at every stage of the process. It is important to note that this is a process and not a goal – data literacy is never truly ‘done’ but continually built on and developed on over time.

2. Data integrity – Expand data integrity reporting, identifying, and alerting users to data conflicts.
For Chris, ensuring trust in the data is an essential part of getting people to engage with it. This is achieved by a continual focus on ensuring the integrity and quality of the analytics they consume. Data integrity encompasses much more than basic data entry accuracy. It revolves more around ensuring systems are understood and used properly. Another way of looking at the role of data integrity is in ensuring that new services that are released are landing effectively – that they are working and being used as intended, that the usage statistics can be trusted and a clear picture of the value they offer is visible to aid further development.

3. Analytical community – Defined communication channels
To respect the concept of analytics being increasingly owned not just by an IT hub, but by the wider business as a whole, it is important to develop a community outside of the core IT team. It will be different across different organisation sizes and cultures, but essentially Chris’s advice is to find those most invested in data from across the business and create ways for them to be involved in the development iteration process. Sharing ideas, best practice, problem areas and providing them with a formal channel that they can share with their team so that every invested party has that line of communication with the analytics team.

4. Advanced analytics – A move towards data science
Analytics is maturing quickly. We are at a point where our customers can achieve diagnostic and descriptive analytics solutions with ease and the demand now is for predictive and adaptive analytics. Chris has already implemented R and elements of data science into priority aspects of his solutions and is looking forward to the next stages of this rollout. The Qlik Sense technology and our own in-house development skillset in these areas are the key differences that are making it so we can have these advanced analytic conversations now in a way that was extremely challenging just a few years ago.

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