How to align data and analytics to achieve business outcomes
Over the past few months I’ve been engaged across industries to help organisations understand what businesses are trying to achieve from big data and analytics. I’m glad to report that the focus is no longer on the technology first; at last, the belief in the old idiom from “if you build it, they will come” (pinched from the 1989 classic film Field of Dreams) has all but disappeared.
The truth is that any data and analytics projects must begin by identifying analytic use cases before drilling down to the key business questions that could be answered by the analytics.
“Any data and analytics projects must begin by identifying analytic use cases”
I recently worked with a government customer to lead a series of workshops to identify what the organisation expected to achieve from analytics before creating an environment or loading any data whatsoever. On the other side of the coin, when I engaged with one of our retail customers they had invested resource into loading their data onto a big data platform in addition to their data warehouse, and were struggling to understand the full potential of the data as a consequence.
Having to work in the opposite direction (much like the retailer I just mentioned) is a viable alternative, and can often be the right thing to do when you’re looking to get more from the systems you already have available. When it comes to a new system or technology however, beginning the process by looking at use cases is definitely the ideal.
To achieve real benefits you have to map analytic use cases based on the available data, along similar lines to the following diagram:
The mapping was achieved with Teradata’s Retail Business Value Framework, a collection of hundreds of proven analytic use cases developed from engagements around the world.
Even from one simple mapping exercise, you can clearly see where additional analysis could be conducted, and where additional data sourcing projects should be planned for the future.
I am now working with a banking customer to take one specific use case (drivers of customer experience), break it down and refine it in order to identify the key events required to support customer journey analysis (e.g., website interactions, customer contacts).
A brief introduction to events (and their uses)
“Integrating events from different sources provides additional context, which helps to understand customer behaviour and develop meaningful customer journeys.”
Integrating events from different sources provides additional context, which helps to understand customer behaviour and develop meaningful customer journeys. Events occur across different channels, systems and devices. These channels are often developed independently of each other and serve different functional purposes. As a result, different types of data will be collected when events are processed or interactions initiated.
Events are a subset of the varieties of data that can be ingested into a data lake. Events are records that describe an interaction, such as between a bank and their customer at a moment in time. The event lake standardises the event data and organises it in a way that is meaningful for a wide range of users and use cases, creating a set of dimensions and metrics that are relevant for each event type.
If you build it, will they come?
Over the next few months I’ll be working with many more customers across industries and countries, helping them align their future plans for data and analytics to their desired business outcomes.
It is fair to say that at least in my corner of the world, the old ‘technology first’ mentality has become a thing of the past. Technology will continue to be necessary to achieve the business outcomes you’re looking to achieve, but it no longer leads the charge.