Over the last few weeks, we’ve discussed at length the various considerations needed in the data-driven decision-making process. From getting to the heart of the problem and preparing your data, to the unique insight data visualisations can provide and the fundamentals of UI, we hope our series has highlighted some of the benefits and considerations of using data to make better decisions.
In today’s blog, we conclude our series and consider some of the key takeaways.
Responsibility for making decisions is not solely the responsibility of the data solution, nor is it solely the responsibility of the individual. Discussing The Eagle’s final approach during the Apollo 11 mission, MIT Professor David Mindell pointed to the “trusted, transparent, collaboration with equal load sharing between the human and the computer”. Getting the balance right is so important and we thought Prof Mindell provided a great illustration of that cooperative approach in action.
Effectively using data to power decision-making first requires a comprehensive understanding of what it is you hope to achieve. Begin the data journey not by looking at the data itself, but by considering your core business objectives.
At Analytics Engines, we work closely with our clients to uncover the real issues and challenges they face, which enables us to develop data solutions that truly meet their needs. Our ‘PRECISION for Pharmacy’ healthcare product is one example where this principle of defining the issues and developing collaborative processes underpinned the success of the project.
With your problem defined, the next consideration is the data itself. There are several factors that can limit the insight that businesses can draw from their data, such as data quality, ownership, conflicting stakeholder interest and privacy.
When preparing your data, the most important thing to remember is Rubbish in, Rubbish out. The better the quality of your data, the greater the depth of insight that can be gained. The more data you have, the more representative those insights become.
At Analytics Engines, unifying data in a meaningful way is second nature to us. We’ve helped numerous clients overcome many of the issues mentioned above and provided them with data solutions that enable meaningful action within their organisation.
In the follow-up post, we looked at data visualisations and how best to simplify data to make it comprehensible to the end user. For our data scientist Liam Brannigan, visualisations and machine learning are not simply seen as the outputs of data analysis but are integral tools to be used throughout the data analysis process.
“Perspective is important. Data visualisations can provide us with insights we might have otherwise missed, or they can help identify unseen avenues for progress”
Creating a data solution that enables the user to draw meaningful insights requires a great deal of consideration. The principals of good design are as relevant in the digital world as they are in the physical world.
Design with the end user in mind (however many that might be) and remember that the analytics needs of one can vary significantly from the analytic needs of another. A good UI should be clear, capable and intuitive and should integrate seamlessly with everyday workflows.
“Good design is making something intelligible and memorable. Great design is making something memorable and meaningful.”
– Dieter Rams
Utilising data in the decision-making process seeks to remove as much guesswork as possible and create strong foundations upon which decisions can be based. The better the quality of your data, the greater the depth of insight; the more data held, the more representative those insights become.
To find out more about how we have enabled organisations to make better decisions, both in the public and private sector, contact us using the form below.