LinkedIn Q ‘how has your organisation used BI’

Question: How has your business utilized Business Intelligence (BI) and how do you see this developing in the future? Do you see this is a separate departmental function or a core attribute for senior managers and executives?

My answer:
I’ve seen a range of clients with different approaches to business intelligence, at various levels of maturity across different dimensions, including data quality, integration, automation, business buy-in, strategic vs tactical vs operational.

Most of these have been successful at some level in providing value through information delivery to support decisions. The really successful ones see that the exploitation of and insight into information comes from top down so that top management look at all the numbers (not just the typical financial ones).

The delivery of effective business intelligence to the senior management sponsorship then needs to be across their scope of influence – i.e. consistent across the company.

So to answer your question directly, a data-driven decision making mindset at the top level is needed for effective utilisation across the company (these top people don;t need to be ‘power-users’ but they will have the power to make things happen). Then the rest of the organisation will follow in their stead.

The leading state of delivery of business intelligence (i.e. the BI department) is a cross-organisation ‘competency centre’, ‘centre of excellence’ or similar where there is a core of skilled, experienced business intelligence architects and developers, who work closely with department-level business people and technologists to deliver optimised BI solutions to the business, using the most relevant areas of the organisation’s knowledge.

Business Intelligence is evolving towards an integration with search technology in one direction (give me the answer i need based on these search terms), integration with business rules, predictive analytics and business process management to give ‘decision management’ in another, and more integrated reporting and planning (which has been evolving for years) in another.

These aren’t as different as they seem and all the threads have one thing in common – essentially, a real need for a quality information management approach across the organisation (source systems, master data, data warehouses) which should not be underestimated

My definition of information as a service

Speaking yesterday with a colleague, we got around to discussing ‘information as a service’. My colleague is a vendor analyst by training before joining us so keeps a close eye on what the vendors are saying. His view was that the vendors are saying that information as a service is all about holding data in memory, speed and very physical features and benefits like that.

Now this may be the case but this seems like the typical techie approach – making the concept and the supporting/enabing technology into the same thing – when they’re not.

Information as a Service (in my world) is the concept, approach and architecture that brings consistent information to other systems in the organisation through a one to many ’service’ interface. So for example there is a customer service which all other systems should use to get their customer information. This could be real-time but doesn’t have to be. There could be a variety of levels of ’service’ within the same service (e.g. ID and name may be enough for one system, another may also need billing address, invoice address and preferred shipping info).

This may be delivered using web-service architecture, it may be delivered using ETL or EAI tools, it may be delivered using special ‘information as a service’ tools. The tools aren’t really important. What is, is that the information – whether master data, analytics or other information – is accurate, consistent and trusted in the data source and that it can be delivered to the systems that need it when they need it.

So information as a service is about getting the master data right, or the analytics right and the processes around that then using the software as an enabler, rather than the software being the be-all and end-all.

Understanding turbines

Vinnie Mirchendani blogged today about how GE are using essentially BAM on their leased turbines to predict downtime and then share cost savings with their customers. The original came from Booz, Allen.

http://dealarchitect.typepad.com/deal_architect/2008/02/ge-siemens-and.html

This is such a great product of operational intelligence. Vinnie mentions how it could be done for outsourcers – coming up with value pricing options for capex investments. Someone will do it first. Wipro or Tata?

It got me thinking about which other industries this could apply to -

• Premium car manufacturers (my german car is totally controlled by computer) could use the data they get from usage at services to offer more relevant warranties, discounts on services, upgrades?
• The capex model/value pricing will work on trains, buses and other public transport systems. The public firms would push this further
• Banks already use predicitve risk models to price credit & loans for their clients (then go and stick their money on flaky investments themselves)

Name change

I’ve just changed the name to information and decisions, to more accurately reflect the topics that I am interested in the management of. Nothing else has changed however!

‘how to make better decisions’

The BBC are showing a documentary (Horizon) this evening (12 Feb 08)  about making better decisions. It’s written up on their website.

Basically it’s a presentation of Geek Logic by Garth Sundem, a book about equations/models that can be used to make better decisions in daily life, such as should you go to the gym, or should you apologise about something. Mathematical modelling for those everyday occasions.  Its a nice point, if a bit tongue in cheek.

The question I have is how effective are these models? The more I look at the examples on BBC and play with the numbers the more I see the holes in the equations – particularly how sensitive they are to one variable over another. And the very simple decision guide (if B>1 then buy it) isn’t very useful at all.

The idea is nice but the implementation here is trivial.

EDIT

I’ve been thinking about this a bit more and the trouble I’m having isn’t with the equations – which pass basic mathematical modelling tests but I’m still not confident enough in them, but the data that passes as parameters for them. For a bit of fun, its great. To use a similar process to actually automate decision making requires much better control of the parameter values and outcome bands. These data need to be trusted on the way in in order to make sense on the way out. Subjectiveness in data entry is a killer for automating decisions – someone is making a decision on the value of most parameters. This needs to be objective!

I’m looking forward to watching the TV show this evening (or maybe on iPlayer) to see if it is a ‘just a bit of fun’ sell or they are using it as a real technique.

Real-time vs right-time

There has been an argument over the past few years about real-time vs right-time data amongst vendors and analysts, who approach the question from the feasibility of technology, talking about ‘federated databases’ real-time ETL. real-time purchase history. For techies these are valid questions and interesting arguments. But, if the data doesn’t reach the point of decision making in a form for someone – whether a customer service representative, a salesperson, a manager –  to make a decision RIGHT NOW then the value of the information in the data has been lost.  

If instead we approach the question from the point of view of the business process then we get a different result. By building rules into systems which build on models and historical data, this data can be turned into real-time insight. By telling people on the front line what decision to make as part of their interaction with a customer, rather than providing them with information that may be interpreted differently businesses can ensure that corporate strategies and tactics are implemented on the front line. These best practices are then shared as service across the business whenever a particular task is needed to ensure consistency. 

Examples of organizations who could use this form of real-time insight include corporate banks who use models based on Basel II rules to inform the risk profile of loans offered, telecoms companies who can offer appropriate upgrades to profitable and non-profitable customers, and media firms who can make their ad pricing more efficient by sharing the services which provide pricing guidance across the business.

Providing real-time insight requires good data quality and in particular the use of master data management services across operational and analytic systems. Predictive models can mine the historical data and identify the appropriate models and segments based on incoming customer information which a rules-based system can direct to the right prompt on the salesperson’s screen.  

How much need is there for ‘real-time data’ in this model? To make the most of real-time insight, businesses actually need to know as much as they can about a customer BEFORE the transaction actually happens – based on purchase history, website history, information provided online. After the fact is too late to actually impact the decision – meaning that traditional ‘real-time’ data approaches cannot be acted upon to change the direction of the transaction in your favour.  

Really useful real-time data means that the data is being used to make a difference to the business. This means real-time insight, over real-time transaction history. Real-time insight gives businesses the opportunity to align their operational decisions with strategic and tactical objectives, meaning that operational effectiveness is increased.  

goals again

I just realised that I’d published that and i haven’t really put any thought into my own goals – where I am and where I want to be. A task for the upcoming weeks.

goals for the coming year

I work for a well known consulting firm. The problem with this is, that everyone knows we do consulting, but have no idea about what we actually do. To that end, we’re aiming to get out and use conferences, workshops and articles in trade magazines to build up the profile of the firm in data quality and the personal profile of our guys as experts. It should be fun. I presented at Business Objects’ European conference earlier this year which went down well and some other guys have done similar, including pieces in the press. we just need to get it a bit more organised over the coming year.

Technorati signupp…

I’d better get signed up with technorati.

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Intro.

Hi and welcome to my blog. I want to use this to communicate the up and coming trends in biz intelligence, enterprise data management, including master data, enterprise information integration, operational intelligence, and historical insight including predictives. The overarching aim though is to focus on the areas of data integration which actually deliver increases in business performance. A lot of sales fluff goes into making up new acronyms and jargon. I’m not a big fan of all this.

To start, let’s go for a ’state of the nation’ around this wide area. What will BI, data integration actually consist of in the next year or two. Here’s a load of words. I’ll follow up on these in the future

Master data management(including customer, product, organisation, and the other big dimensions of a business)

Enterprise metadata

Operational analytics. (is this an oxymoron?)

Predictive algorithms (the future – David Bodanis when he addressed the firm last week)

Historical integration (traditional data warehousing? )
The big question is, as always, why? We’ll follow these up over the coming weeks.