A proven approach to effective MI

A proven approach to effective MI

If you work in the IT department of a large corporate then this approach probably isn’t for you. You’ve probably got Cognos running and this actionable, pragmatic approach won’t fly.

If however you are one of the many mid-sized firms in the UK, or a division of a large corporate, then this approach may work for you – and it is the complete opposite of the Big Data approach, where it is journey of discovery.

Step 1 – focus on what you are going to ‘do’ with the data

Collecting data is a hassle. It is expensive and unless there’s a clear purpose to it creates confusion not clarity.

confusion-information

Focus on only collecting data that will change behaviour. Ensuring only actionable data is collected and analysed for its integrity and quality.

Doing this needs business experience and business insight. And by that I mean real experience of real situations where the correct use of data changes the focus and behaviour.

It is as much an art as a science; psychology blended with business understanding, especially as the business world suffers from an information glut. Information is everywhere, but so much of it is complex or unclear that it’s hard to know what anything means or why you should care. This is not only disconcerting, but confusing, frustrating and overwhelming.

This feeling – information anxiety, information overload, or analysis paralysis – causes people to put off decisions so they have more time to think.

Time drags out as they try to process information. It takes so long to find, understand and prioritise options that business processes slow to a crawl. By the time anyone takes action, situations have turned into crises and stress levels are high – the worst possible environment for good decision-making!

Make it easy – have a clear focus on the WHAT. Keep the stress levels low. Focus on the K of KPIs and make sure those KPIs drive change.

Step 2 – get the data in one place.

By focusing on the WHAT, the HOW should be a smaller task. A reduced data set reduces the effort required in the extraction and transformation process. Fewer databases to integrate with, less data to move and less manipulation required.

All reduce cost and time.

If you are automating the data collection, then our next step would be to analyse the data sources; understand the structure of the data, in order to build the Aggregation Layer: the common platform that all of the data will be drawn into.

Getting that database design right is critical to the performance of the MI dashboard and its future. Get it wrong and it causes gridlock quickly.

We frequently draw the different data sources together into one database. It requires a unique blend of BA, data and development skills.

Step 3 – design the dashboard UI around your user base

The last step is to rearrange the letters in the HOW to WHO. Understanding who is accessing the data, when and where is essential. Is it on the tablet before they visit a retail store? On a desktop whilst writing a report? Or during a board meeting under the table?

Creating UX-optimised environments for your users both in – form – (tablet, desktop and mobile) and – function – (graphs, tables, KPI presentation) is the difference between success and failure.

Step 4 – call Skyron

Absolutely these steps are replicable. As you can see they do need a blend business insight, technical understanding and excellent UX. That’s what we do. We create Beautiful Software then enables our clients’ businesses.

 

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