Building Better BI

I was recently made aware of an article about Business Intelligence (BI) that struck me as extraordinary and smart. The author, Paul Holland, is a BI consultant who has a great deal of real-world BI experience. In the article, he emphasizes the role of people rather than technologies in BI success, which is correct and under-appreciated. Paul has given me permission to share his article with you here in my blog.


Building Better BI: Should I Ride the Donkey or the Bullet Train?

Recently, I was in conversation with a senior executive of a business who was explaining to me how spending many hundreds of thousands of pounds on a very shiny, new and aggressively marketed BI visualisation platform would enable them to access even more of their data than ever before.

Now it would have been impolite of me to point out that accessing data, any and nearly all of this company’s data, is not a problem to begin with and that this statement alone indicates a deeply flawed understanding and approach that many senior managers in need of analysing and understanding their business(es) seem to arrive at – namely, we should spend more money on cutting edge IT systems to gain a competitive advantage. Furthermore, these senior managers will often control the purse strings of an organisation or remain deeply influential in how a company invests in its IT infrastructure. On these occasions, such processes as they are, are little more than a fait accompli; your organisation will end up with a new and shiny silver bullet IT/Information system whether you like it or worse still, whether you need it or not.

Consequently, it should come as no surprise to many of you reading this that many organisations out there are putting down enough money to buy a nice apartment in Paris in order to buy a contemporary BI visualisation tool; the one you’ve been told enthusiastically by someone, ‘it will solve all your reporting problems, believe me, really it will’. What is a surprise though, to me at least, is that having sunk this money into something so powerful, and I’ve seen what I’m going to say next happen on many, many occasions, they simply then connect this expensive, shiny ‘BI system’ full of Promethean promise straight up to their existing data sources. Or in a similar vein will take the myriad of spreadsheets they’ve built over the years and just bang them into their shiny new system, spending an incalculable amount of time trying to get what they had before, working exactly like it did before, looking exactly like it did before. So, to reduce it to its crudest and least sophisticated form, they unplug the old thing and then simply plug the old thing into the new thing, thereby, producing a new old thing. Get it?

Job done then.

And so the rallying cry goes out through the organisation, “relax everybody, you are now going to get access to the most powerful information you’ve ever seen!”.

Only you are not.

You see, BI is not simply an IT system any more than an F1 car is simply a driving machine. It is the amalgamation and unification of a set of processes, different business units, strategies, people and skill-sets combining together within an organisation to produce meaningful and viable information. And this makes it most of all a ‘people thing’ rather than a technology thing. In fact, to be more precise I’m going to argue that BI is about what I call the three Ps; people, purposes and processes. So in my world, there’s no Ts in the Ps!

Certainly, from my experience, understanding and managing these three factors is what makes producing good BI much harder than simply buying what are increasingly becoming very large investments into visualisation systems or BI suites. And if you rely on the IT system delivering your reporting salvation, no matter how advanced and cutting edge you’ve been told it is, then you are probably heading straight for the interminable BI graveyard. Don’t just take my word for it, any review of literature on the subject will reveal you are in good company here. I know of one large organisation in England that has invested its scare resources in three, yes that is three, new shiny silver bullet BI visualisation tools in the last decade. All of them failures. In fact, two of them going virtually completely unused for the lifespan of their investment. This should serve as a cautionary tale to anyone thinking of getting an easy fix for their money.

Furthermore, what has most commonly been gained for this considerable investment is simply easier access to a morass of existing company data resplendent with all its inherent data quality problems. And all too often this comes with the added ‘benefit’ of actually increasing costs or workloads due to the subsequent addition of a ‘BI’ team to focus on making these old data connections work just like they did before. Typically, in a classic case of technological determinism, the system creates the organisation, the workflow and resourcing post facto, after the thing itself has been bought. So rather than simplifying things and reducing costs, you can end up with a larger team consisting of business or data analysts and IT people, all of whom will spend considerable time working towards returning you to, potentially, the state you began the exercise in. I mean what is the point in spending something like a quarter of a million pounds if you can’t replicate my trusty, ill defined spreadsheet I had before huh? I’ve actually seen people sit with their spreadsheet, which they call a ‘dashboard’, spending valuable time recreating the exact same thing in their sophisticated and powerful visualisation tool, usually sucking in the resources of two or three other office staff nearby. I’ve also been in training sessions where people have asked for the system to be redesigned so that they can recreate their local, specific and rather limited use, spreadsheet which they also then keep active afterwards to cross check the new view that has been created at great cost for them in their shiny new system full of Promethean promise. Is it just me or does this seem wrong, crazy even?

This type of behaviour, which I think is not uncommon across organisations (just look at the books and conferences out there), seems to me apropos of building something like a railroad then continuing to ride along it with your donkeys instead of trains. Sure your donkeys have better and easier access to the existing route, and you know your donkey so well too, you can keep it straight on the tracks but you absolutely do not need a train line to ride along on a donkey, do you? So why spend so much money on a railroad to keep your donkey’s running? Similarly, I ask myself then, why does any organisations spend close to £300k on hooking up a sophisticated visualisation platform only to recreate what you already had or have had before, a proliferation of rows and columns and some red and green (far too often) bar charts and pie graphs? I expect you to get bullet trains for 300k, not donkeys.

Now I’d argue that you don’t buy a new system just because you want better, or more efficient, or easier access to existing reports. You buy a system like this because your organisation has made a strategic decision to understand its business better, to measure activity and performance, to seek out inefficiencies and wasted resources or use of time; to understand, measure and refine its processes or proposition to its customers. Bear in mind though that often this is still looking backwards at your business, what’s happened already, but with a good strategy and the right people you can also use such a system to look forwards, to predict certain outcomes for you or to measure the magnitude of some change that is occurring in your business too. That’s the kind of power I expect to get at my fingertips for £300k.

I’m willing to argue that 90% or more of the companies, organisations and large corporations out there already have access to more data than they can possibly manipulate never mind contemplate. We’ve been collecting lots of data for a long time now and, furthermore, accessing this data has not really been a problem, people have been doing it routinely for decades. I think understanding how to define and make better use of this data is actually what we have been doing wrong for decades and for me it is this work that is fundamental to successfully building and deploying effective BI solutions in an organisation. This is where I think the focus needs to change, to move away in the first instance from the software, the data and databases and to focus your time and investment instead on engaging with people, purposes and processes. The three ‘Ps’, if you will.

In the work I do with clients, it’s the three Ps first rather than data which is the focus of any BI undertaking I am involved in. Even NASA and contemporary Astrophysicists know that people are really what you need to build a model, to confirm a hypothesis, to verify the data and help turn it into useful information. It is surprising in this day and age but there are some jobs people are just better at than a computer. That’s why Professor Brian Cox on the latest series of Skygazing Live ‘farmed’ out to viewers the task of analysing large amounts of astronomical data to identify patterns that might indicate a 9th body in the solar system. Surely science departments the world over have super computers and programmers to analyse this data, no? And yet it is deemed that people at home can do this job better. And that’s because data is just that, data, but with people you garner understanding, comprehension, nuances and connections about the subject of your inquiry too.

See it here: https://www.zooniverse.org/projects/marckuchner/backyard-worlds-planet-9/about/research

So, even with the greatest dataset, computers and powerful algorithms to hand some jobs are done best by people. And in keeping with this point of view that’s why when it comes to BI, I always start with people and not with data. Data will not build you an effective BI system, no matter how much data you cram into your data warehouse. But people who require information about their business to make informed decisions, to predict problems, to deploy resources efficiently, they will help you build an effective BI system, one that is fit for purpose, one that informs decision making and one that they themselves will have confidence in and in using too.

So what do I do then? It would be too much to detail here so I’ll outline my methodology briefly to counterpoint my arguments above.

Well you now know I don’t start with data when I help someone to build a BI solution. Instead, I start with the purpose, the reason why someone needs it. I investigate the processes that are the subject of the purpose which helps me understand the breadth of the subject area and systems related to it. I discuss these needs and work out with individuals and groups why they need that information, when they need that information and for whom they need or present this information to either internally or externally for the organisation. In unison with the relevant people we then grade the importance of each item/category identified for serving the purpose and collate it, thereby, building a record of prioritised needs for the technical team and any associated project members to continue working on. In short, we essentially build what I think of as an information landscape, an information map of requirements for the organisation that leads to the compilation of a set of contained business questions that address the purpose(s) that we started out with. I call this my ‘virtuous circle’, everything done should be harmonious with, and work towards, satisfying the purpose. Ultimately, this process also helps to delimit the scope of the design and solution, thus, helping to avoid the insidious ‘scope creep’ of a project. These processes also have the benefit of producing a definitive record of what has been included, excluded, assessed, defined and agreed upon by the business unit/owner of the solution.

It is only after all of this work has been done do we begin to sit down with technical database staff or such like and begin to identify the right data items to bring into the data warehouse and, subsequently, how that data will be treated when it comes into the data warehouse to ensure the veracity of the information that is produced. This process ensures that the data being brought into the warehouse matches the businesses’ definitions and meets the purpose of inquiry and not a technical definition of a field somewhere or a piece of data that could potentially be compiled of other or unknown things to the business.

I have no doubt that sometime in the future, this method/record will also prove invaluable to you when system problems occur, data items change in core systems and for those arguments that happen in meetings when people claim you have the wrong numbers or are measuring the wrong thing. You simply pull out your lovely fat A4 file and patiently take them through it and if you are feeling cheeky, you can ask them to show how their numbers were derived, who defined them, who agreed they are fit for this purpose etc. These definitions should, in practice, become the authoritative source for reporting in the area concerned. No more arguments about what something means, well, maybe a lot less argument! It also provides more confidence in using and sharing the reports built from this approach between departments, managers and analysts alike.

And of course we do end up talking about data with ‘the business’ no matter how hard I try and avoid it in the nascent stages. This is to a large degree historical in that often people are conditioned to see data as information and vice versa. Often the people responsible for providing requested information are also system/data gatekeepers in some part of the organisation. Understandably, they often make their own decisions about how to compile or consolidate different data to create a metric. They think in fields and tables and look ups and not in terms of information and the life-cycle it encapsulates for the consumer, how it will be consumed, its potential audience(s) and its purpose.

I know I’ve covered a lot of issues and ideas here already but consider this before I finish, I haven’t once yet mentioned the BI system itself, the software have I? No technology company fireworks and sexy quadrants, no industry white-papers, no product names, no slick features, no concurrent and fashionable packages or systems at all. And that’s because you don’t really need a silver bullet to begin building your BI suite or programme. You can do all these things I’ve suggested above without handing over a single cent to a software seller. In fact, remaining system or solution agnostic at an early stage will allow for more open thinking and for ideas to percolate to the top. So it will be no surprise to you by now to learn that I’m of the opinion that if you do some of these things I’ve suggested above prior to tendering for a system it will only aid your journey in finding the best aligned and most economical visualisation system for your organisation. And who knows, you may even find that the tools you have in house are capable of delivering the types of information and visualisations you need already which means you get to keep that hard earned £300k in your back pocket after all. Good for you and good for your business.

Paul Holland

5 Comments on “Building Better BI”


By Bill Droogendyk. June 15th, 2017 at 6:28 am

A craftsman can do so much more with ordinary tools of yesteryear than an apprentice can do with ‘state of the art “tools”‘ of today – compound that with the continued failure to institute good practice DV defaults in the software…

By Andrew. June 15th, 2017 at 11:10 am

Much of the problem he describes is exactly why I quit doing BI last year. The goals of BI vendors and the needs of BI users just don’t align at all. So I decided I didn’t care enough about it to fight those battles anymore and I got out.

But I’m always happy to see other people recognize the problem and do something about it.

By NSP. June 18th, 2017 at 7:23 am

This is a great article. I think most people who are knee deep in technology projects within large organizations can definitely relate.

By Terry Hayden. June 19th, 2017 at 9:55 am

Excellent article. So true!

By Nilesh Sakpal. June 21st, 2017 at 11:48 pm

Putting purpose before data while designing a BI software – this is so true!

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