Agile Analytics – Reducing Time to Value
*Blog image by Jakub Szepietowski
Last week we looked at 5 trends set to impact business analytics over the course of 2016 and beyond. One of the areas which I think will be integral to the success of business analytics projects is the adoption of agile methodologies. The main headline here? Agile analytics gives businesses more value and quicker.
Too many business leaders know there is value buried in their operational data without being able to capture it in the short-term in any systematic way. There can be multiple reasons for this: initiative overload, a lack of budget, and (or) inadequate skills and capabilities in-house. However, more often than not, the overriding reason comes down to running up against the ongoing activities and plans of the corporate IT function, be it the timing of enterprise-wide programmes, product roadmaps and yes, just plain old politics.
This doesn't have to be the case for analytics-led initiatives. There are ways that short-term business needs and the long-term goals of corporate IT can be joined at the hip. The answer lies in agile analytics.
What is agile analytics?
The essence of agile analytics is on speed to value. The approach involves rapid prototyping where you build and test your solution as you go based on models, insights and actions that can be drawn from data sets. Contrary to more traditional approaches this comes with a number of benefits:
1. Time to insight and value. You don't need to wait for large amounts of development work to start accessing your data and using it to inform decision-making.
2. Reduced Investment and Risk. Unlike traditional approaches agile analytics avoids hefty upfront investment and platform building. It relies on affordable light-touch self-service analytics tools allowing more time to think through long term platform investment plans and requirements.
3. The solution fits the business' and users' needs. Often large projects take so long to implement that by the time they're in place they do not reflect the latest needs of the business. The development of a prototype not only helps sustain the data capture and tracking on an ongoing basis but can also act as an ongoing model that informs the requirements for future enterprise IT builds.
4. IT's time is saved. Because self-service tools such as Tableau and Alteryx are allowing business users to interact with their data directly, IT's time is used much more efficiently. They do not have to spend hours accessing and structuring data for others. Instead, they can act as an advisor and support service as a full enterprise wide solution is defined.
How is Agile Analytics being applied?
I have recently seen a number of our clients benefit from this approach. At a corporate investment bank, there was a 3 year programme to build a client analytics platform. They have used agile analytics to deliver working models within 6-9 months which are delivering value in regards to client profitability, revenue maximisation, and network analysis. This approach is now informing the firm's overall approach to building an enterprise platform.
At a global consumer business they have used an 'interim' enterprise reporting tool using agile analytics that improves forecasting and inventory across its 180+ markets. One year after deployment, over £100m has been captured in improvements in working capital. The interim solution prototype put in place is now acting as the requirements model for when they build their global platform.
In both cases, the investment into agile analytics has been a small percentage of the overall platform build, and the projects broke-even within the first 6 months of going live.
How you can get value from Agile analytics
The beauty of agile analytics is you don't have to be an industry behemoth with deep pockets to benefit. The approach can be used as part of any requirements gathering process, whether it's testing hypotheses or building out prototypes.
So the next time you get pushed back from IT resource constraints – take the initiative yourself! Next week I will be looking at how you can get started with your agile analytics project.
Have you got any good tips for how to approach analytics projects? Join the discussion below.