It feels like we are moving towards a “BI for analysts only” culture, so I feel compelled to ask: Is analysis just for analysts?
Petr Podrouzek wrote the first in a series of thirteen articles about agile business intelligence (BI) a few days ago. In this first article, Agile for BI is recommended by the experts, but is it used in the real world?, Petr points out that through his six years of working on BI projects he has never seen agile BI in use. To further his argument he says that many BI projects start out with an agile approach but that this is quickly abandoned in favor of the traditional waterfall model, where requirements are collected and a data model is developed followed by data sourcing and development of ETLs.
As Petr points out, this slow and excruciating process takes up precious time from everyone involved. And at the end of the day the customers, the ones who depend on your dashboards and reports, are left disappointed.
Thanks to a tweet last week from Cindi Howson, I discovered The Battle of Business Intelligence: Data Discovery vs. Traditional BI written by Southard Jones from Birst. The article provides a nice overview of how Data Discovery evolved and moved into the BI mainstream but most interestingly it argues that “Data Discovery remains one small piece of the larger pie that is business intelligence” !
Thank goodness for that, finally someone else saying that Data Discovery is not the be all and end all of BI. We were beginning to think we were the only ones Continue reading
A few weeks ago, @startupvitamins tweeted a great quote :-
And no-where is this more true than end-user Business Intelligence (BI). It makes sense for analysts to carve out the time to sit down and learn generic data analysis tools as they form a central part of their jobs. But for the rest of us, with operational responsibilities, things are different, we need BI which we can just use, without explanation.
Here at Antivia we have been championing this for the last few years, (read pretty much any of our other blog posts to see what I mean). Our product DecisionPoint does exactly this, it allows you to quickly and easily create easy-to-use, no-training-required BI apps and dashboards that you don’t have to explain to end-users. Continue reading
The Big Data / Data Science storm seems to be reaching new heights. One of the articles sitting in my inbox awaiting my return from vacation upped the ante from the usual refrain of “everyone is an analyst these days” to a new level of: “We’re all data scientists now”.
To be fair to @juliakking who wrote the piece, she has a number of sensible things to say in the article and the headline may not have been hers, but nonetheless, I worry that exaggerating the level of data analysis expertise we should expect from non-data specialists often does more harm than good. After all, the fact that we can now all file our own tax returns on-line does not mean that we’re all tax accountants now. Continue reading
I love Dave Cherry’s equation “Gut + Data > Gut” in his article where he argues that gut feel is always better when complemented by data.
I would take it a step further and add that :-
Gut + Data = Better Gut
After all, gut feel comes from experience and what is experience if not the collection of data (often informally) about a particular business or organization? Continue reading
Unfortunately, I couldn’t be on the chat at the time but reviewing the conversation it felt like there is little belief that pre-packaged BI applications which you can just “plug in and use” are really viable, they came low on importance in Howard’s most recent #WisdomofCrowds survey and most people on the chat seemed to worry about the need for customization. Continue reading
20 years’ ago when I first started my career in BI one of the most common projects I would get involved with was the replacement of Excel based reporting systems, with a “proper” BI system.
The business justification was easy:
- Duplicated effort with multiple departments producing their own versions of the spreadsheet
- Time wasted in meetings deciding whose numbers to run with – do we go with finance’s definition of revenue or sales’ definition?
- No single version of the truth meaning a recipe for inconsistent decision-making Continue reading
Comparing Business Intelligence to weather web sites might seem a little odd, but I am convinced it is valid and more importantly highlights a path to the future for the way we use information in our organizations. Continue reading
Once again BI adoption is pretty much static at 22% (a little down on last year’s 24%, but probably statistically flat).
As I have written about time and time again in this blog, my strong feeling is that the reason for this flat-lining of BI adoption is that we have saturated the “analyst” BI market and we are going about “end-user” BI in the wrong way Continue reading