I agree with Martin Butler when he calls for a far more rigorous analysis of return on investment before deciding to invest in BI & analytics.
- "Empower your customers and users with smarter insights"
- "Better data-driven decisions"
- "Actionable Insights"
- "..helps people see and understand data"
- "..create visualisations, dashboards and apps that answer your company's most important questions"
All good but rather anodyne "Mom & Apple Pie" Statements.
There are always key success factors involved in any business, government organisation or non-profit. You must know these before you decide what data is required to help you manage i.e.
Unless you know these all the BI & Analytics tools in the world won't help at all. What decisions do you have to make, what must you achieve by making those decisions, who will be involved in the key tasks and activities involved, what resources will you allocate to these teams and over what time period will the project run?
Only if you know these key factors well will you be able to decide which analytics and BI tools are most appropriate.
Too often buyers of BI & Analytics tools rush into the "easy to use", "just point, click and get answers", eye-watering visualisations " wow look at that demo" products.
Next time a BI/Analytics vendor impresses you with their products ask them how they use their own tools to perform better. Ask them if their diving stock price is a result of their great product. Or their stagnant market share, or life-threatening cash-burn.
And lest I forget to mention it- "It's the data that is the key". Incomplete or out-of-date, or corrupt, or irrelevant. BI and Analytics depends on good quality data which is why "Self-Service Analytics" is potentially a red-herring. Unless this is operated in a secure and scalable managed data platform you will end up with silos of incompatible data and insights.
The CDO, Data Scientist, DBA, are essential people in addition to line-of-business people pulling at the bit to get the show on the rtoad.
Fools rush in where angels fear to tread....
Let’s be clear about what we want from business analytics. We need more accurate and more timely decisions that cost less to process – and that’s it. It might be called decision automation, or at least assisted decisions. We ponder over reports and dashboards to help us make decisions, and we deploy predictive models to help automate decisions. And so it would be useful if we had some understanding of how business analytics might be operationalized. These can be summarized in the following way: