Author Archives: @donaldmac

Is Data Discovery like a Joke ?

A few weeks ago, @startupvitamins tweeted a great quote :-

A user interface is like a joke. If you have to explain it, it’s not that good.

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

We are NOT all Data Scientists, we just need to be Data Savvy

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

Gut + Data = Better Gut (today and tomorrow)

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

A middle ground for “Pre-packaged BI Applications”

There was an interesting discussion last Friday on Howard Dresner’s weekly #BIWisdom tweetchat about pre-packaged BI applications.

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

Weather web sites and the future of Business Intelligence

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

Static BI Adoption and a Proposed Solution

Cindi Howson of BI Scorecard has just published her annual “Successful BI Survey”* and the results are sadly all too familiar.

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

Why have SAP gone Lumira mad ?

Is it just me or do SAP talk more about Lumira than all their other BI products put together ?

If I am right, then it is a little odd because when I have talked to people at SAP the strong message I get is that Lumira is a tool targeted at analysts, and analysts, I would argue, are a small minority of the potential BI users in an organization.

Despite some of the more outlandish claims in the market, we are NOT “all analysts these days”. The vast majority of us have jobs to do which involve running part of a business, and although we need data and information to do this, this does not make us analysts or suitable users for an analyst tool.

Continue reading

The two key types of BI usage

Today, Timo Elliott at SAP tweeted that for BI:

“nobody seems to agree with me, but it’s not about the type of user, it’s about tasks”.

Timo I do agree, and yes it all comes back to the BBC weather site again :-)

Although you can sub-divide BI a thousand different ways, the most important distinction (if you want to deliver successful BI) is between “analyst BI” and “end-user BI”.

I know that sounds like types of user but actually it is not, it is about modes of usage and one user can use both of these at different times.

Continue reading

A Visionary Example of the Future of End-user BI

My ex-colleague from BusinessObjects, Bill Schmarzo (that is his picture to the left), has just posted a blog entry (here) which should be compulsory reading for anyone in BI and especially for those who are working with BI and “big data”.

Let me explain why …

The opportunities opened up by “big data” are very significant and no-one should be ignoring them. Unfortunately, if you follow the hype in the market today at best you will only realize a small percentage of the value and at worst you will swamp your organization with inappropriate, time-consuming access to data they don’t actually need. Continue reading

“The war on self-service BI”

Mark Cooper (that’s his avatar on the left) posted a great article on the SAP Community Network (SCN) earlier in the week.

The title “the war on self-service” immediately drew me in as I have long advocated that self-service BI (in all its forms) is probably the biggest inhibitor to the wider adoption of BI in our organizations.

I was not disappointed. Mark summarizes the problem beautifully:

“The most commonly attempted [self-service] scenario in BI projects is … the one that I commonly see fail because the project tries to reduce reliance on IT and enable the business to do their own reporting”

Bingo! In a sentence, this is the reason we have had with too much BI shelfware and too little BI adoption for the last 15-20 years.

To explain, there are two issues: Continue reading