The Inform, Illuminate, Inspire big data model

The Inform, Illuminate, Inspire model defines the 3 major phases all Big Data & Analytics projects follow to succesfully generate actionable business insights. It’s a useful starting point and emphasises that successful Big Data projects need to be well structured and highly iterative. The 3 phases of the model are:

INFORM – identifying and integrating relevant data sources, whether internal or external

Inform provides the foundations for the project, an essential investment phase that enables what follows to deliver value. Without it the following two phases cannot happen. Identifying the right data sources is key. If done poorly then what follows will be compromised – Garbage In, Garbage Out (GiGo) still applies. It’s not the most glamorous phase of the project and can be time-consuming and expensive. There are increasing number of tools to automate the process but it can still be complex and some skills are in short supply. Many Big data projects fail because they don’t get this right or fail to rapidly add the new data sources the business and analysts require.

ILLUMINATE – using data science, analytics and algorithms to surface relevant data from multiple sources to answer key business questions

This phase is all about answering business questions with data and relevant statistics. Data Scientists and Analysts use a analytical methods best suited to the business domain and data under examination. Using the wrong methods can be fatal to the project. Much data is contextual so domain knowledge and specialisation are important.

INSPIRE – make the data tell a story and incite users and execs to want to act

There is often a gap between the data delivered by data experts to answer a business question and how it is received¬† by business users. This is partly a technical issue but also a cultural one. Business users are rarely as comfortable with data as data scientists or analysts. Some are fearful of how a greater role for data will impact what they do, and how they do it. Education is key if the busines case for Big Data is to be achieved. Visualisation is a critical part of this phase and choosing the right visualisations, ones that stimulate action, is also key. But it is also about managing change and even the nicest visualisation will not achieve thise. The data needs to tell a coherent story to frame, prioritise and direct business decisions. Inspiration just doesn’t happen, it needs facilitating and requires a blend of skills not always available within an organisation. Data scirntists and analysts can lead users to the relevant insights but they alone can’t inspire them to act!

We are developing an Analytics-as-a-Service (AaaS) platform to help ease access to scarce and expensive resources. It will be focused on retail but when established it will be extended for other sectors.

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