The model was ready to be deployed but kept on hold for the last two years. The client is going through an IT transformation. Which begs the question — was model development worth the effort or could this have waited until the transformation was complete? Should the client have engaged with us only after their transformation was complete?
Short answer — definitely no. And here’s why –
Organizations often fail to realize the benefits that are generated throughout the life-cycle of an analytics project and heavily focus on the final integration into their systems. The integration, though critical, is not the sole value generator of the analytics project.
A good analytics team could help your organization derive tangible value from every stage of the analytical project — from data collection, data preparation, hypothesis creation, exploratory analysis, pattern identification, model development and validation. Insights from each of these stages could contribute towards and even increase the project return on investment (ROI) much before the actual integration.
Most of our initial client conversations begin with an apprehensive client explaining us that they are unsure if they are capturing data properly. Hesitatingly their next statement would be that besides routine report generation, data is not being used to take informed decisions, and even less cross-functional data-driven decision-making is happening. These organizational problems are not unique.
Value Generated — First step in setting a data-driven, decision-making culture.
Our next round of meetings would usually involve analyzing the available data. This would lay out a high-level summary of what’s available and what’s not across systems.
Value Generated — Identify and plug the gaps, and/or rectify the inconsistencies in the ways data is captured across systems.
Knowing and clearly understanding the organization’s data, enables the management to visualize the possible data analyses that could be undertaken and the scenarios that could be planned for.
Value Generated — This step completes the managements’ transformation into data-driven decision makers.
In the exploratory data analysis stage of the analytics process, the decision maker here understands who their customers are? Where do they interact with them? What drives this interaction? How good are our internal processes? What are the bottlenecks? How is the supply chain performing? What parameters impact sales? Which marketing and sales channels and methods are working better and why? Are employees productive?
Using historical data, the management would be equipped to answer all these questions. If not definitively, at least directionally. This would drive informed decisions even before the models and algorithms become a part of the organizational system.
A comprehensive audit of the benefits driven by an analytics initiative across project stages would show that most actionable insights can be gleaned even before the implementation of the model. So, let’s use analytics to take better, improved and informed decisions.
Let’s not wait till the water runs dry
We might watch our whole lives pass us by!