Like many other industries there has been a rise in “full stack”* solutions within the insights industry. The current breed of “full stack” solutions largely are technology based, with the consultancy piece offered as an add on at the top of the stack. These approaches are about technology first, with the ‘how do I make sense of the data and humanise it’ element being left to the client or provided as an additional engagement at a fee if required. And all too often the consultancy piece does not extend beyond training in how to use the various DIY tools. The focus is on software training rather than meaning making. Having an increasingly growing ‘stack’ of customer and sector data at your disposal does not automatically equal an increasing understanding of people and how to leverage that understanding for greater insights and ultimately improved performance.
SO HOW CAN YOU MAKE DATA MEANINGFUL?
Data becomes more meaningful when the stack is flipped by always starting with the client business need or problem to be solved first, then identifying what data points (from client internal sources and external) are required to help address the problem and, finally, what/if a technology solution will aid the solution. This inversion of the “stack” is required in order for businesses to achieve their customer centricity goals, as it changes the starting point to be about understanding people rather than being about how best to assemble (stack) multiple data sets. It is about 'humanising data' rather than 'assembling data'.
We have given humanising data a name – ‘Human Analytics’ - “the synthesis of multiple data streams into insights to fuel behaviour change”
The key to creating insights from multiple data sources is to;
1) GET THE BUSINESS PROBLEM RIGHT
It might seem obvious, but many businesses can become overwhelmed by their data or obsessed with fast results after investing heavily in expensive data analytics. Brands must start by taking a step back to identify the problem to be solved , the broad parameters of the problem and the business decisions to be made
2) AVOID DATA CONFUSION
Next, analyse if the information at hand helps solve the broader business problem or adds to the data confusion. Consider data streams collectively, not in isolation, to see the holistic customer story and solve the business problem at hand.
3) COMMERCIALLY FOCUSED FRAMEWORK
Define the principles that are required to guide analysis and develop a relevant framework to organise disparate data sources in order to address simply and with focus the business questions and issues.
4) ONGOING DATA/ANALYTICS ARCHITECTURE
Providing tools and an ongoing data and analytics architecture to ensure continued and future focused value creation from your data assets
GENERALISTS PLEASE APPLY
This reversal of the stack requires different types of consultancy models and different types of consultants. We need to be willing to invest in consultants with breadth and depth of varied experiences. Those who approach data gathering as data agnostics. Those who can see the forest past the trees. Those who are comfortable with unchartered territories, applying their generalist knowledge to situations they may not have encountered previously. Numerous studies point to generalists displaying greater problem solving abilities, perceptiveness and creativity than specialists. Consultants who can transform data into human analytics - real actionable insights about people.
A reversed stack allows consultants to build on learnings and experiences across industries, business issues and client challenges to ensure the stack is built on a solid foundation.