We discussed few of the gaps in Data and Analytics in the real world. The “Last Mile of Analytics” refers to the struggle companies encounter in creating the behavioral changes that create value within organizations, from great analytics outputs.
If analytics is not going to change behaviors, why do we do any data and analytics project?
In general, companies face business challenges such as customer retention, employee engagement, goods consumption, etc. Data and analytics teams are engaged to solve these problems. These data scientists and engineers go to “a meeting” with business stakeholders in order to understand more about the challenge. After the meeting they go into isolation for a while (possibly weeks or even months) in order to build the necessary algorithms. They look at the data, current employee engagement, customers buying behavior, and so on. They put together a well designed and accurate algorithm to address the business challenge (i.e., churn prediction, forecasting, etc).
Yet after a while, when companies revisit the original challenges, they see that the same problems still exist. This is simply because despite the accuracy of the analytic algorithms, they are not delivering the necessary business value simply because:
- The results are not integrated into operations
- Frontline personnel or managers do not use the outputs because of mistrust (i.e., overwhelming false positive, lack of domain expertise input in the result, etc.
- Analytics results are presented in the form a dashboard only showing numbers and graphs.
To address the challenge of the Last Mile of Analytics, the whole process of building the solution based on analytics should be revisited. The solution is not only about the accuracy of the analytical model and algorithm but also about how to create a solution that delivers business value.
Starting from the Last Mile of Analytics
To address the challenges above, commonly known as the last mile of analytics, the analytic solution should start with the challenge itself (i.e., the proverbial last mile). This means designing the analytics solution with adoption in mind. Crucially, this means ensuring it is aligned with business values and it is delivering the business objectives.
This means the team of data scientists and engineers must work together with the business stakeholders and operational frontline users in order to understand different aspects of the business problem.
Defining clear goals to address these issues and working backward from the goal to define the full picture of the problem and the impact of each individual decision along the line is essential.
It is also crucial to involve frontline personnel by considering the value of their business rules as part of the solution as well as their domain expertise since they are handling hundreds and even thousands of requests on the daily bases.
The goal must be to create an iterative, incremental delivery plan that allows the business to benefit from the solution as soon as possible, learn about the outcome, integration challenges and organizational barriers sooner rather than later.
Integrating the analytics solution into the organizational operating model far earlier with the iterative deployments ensures that workflows, operational decisions and systems will effectively be using the results of the analytics.
Ideally, the business will measure the results of analytics and different variations of the models to understand the best performing options for different scenarios.
Finally, it is necessary to automate interactions with systems, legacies, forms etc., by applying the results back to the system and going beyond the numbers and graphs on a dashboard to ensure the results of analytics are integrated into the organisation’s operational model.
A decision-centric approach allows organisations to get the most out of data and analytics solutions. By letting these analytics solutions to be integrated throughout the organisation’s operational model, within both processes and workflows, and by applying the results back to operational systems, the decision-centric approach can ensure that not only do organisations start from “The Last Mile” while designing analytics, but also measure, monitor and challenge different models of analytics while operating, to ensure they can reap benefits from the solution early on.