Tracy Austin at TDWI

Doug Henshen at Intelligent enterprise blogged about a speech at the TDWI by Tracy Austin, former CIO of Mandalay Resort Group and former VP of IT at Harrah’s Entertainment, one of the most celebrated BI-driven enterprises in the world.

1. BI must be business driven, tied to measurable business goals. If BI is currently IT driven, find a way to evolve it into a business-driven initiative.
2. Data management has to be in place. If data quality isn’t there, no amount of cleaning will make it work. Just say no to more BI work until you can put proper data management and data quality in place.
3. Ensure the right mix of business and IT people. “Mandalay had competent people who were used to working in silos, but you can’t operate on your own when it comes to the data warehouse. You need architects and data modelers and people who can bring everything together and tie it to the larger strategy. Even if tools are there and the data is good but the people aren’t in place, it’s going to fail.”
4. Focus on quick measurable wins, not big, monolithic projects.
5. Create a formalized marketing plan. You have to sell the BI program to top executives and the entire organization. “You can even sell it to Wall Street, as we consciously did at Harrahs.”
6. Base BI investment decisions on business value. Ditch the smoke and mirrors or black-box approach. Have the rigor to commit to quantitative and qualitative deliverables and follow up with reports on progress toward goals.
7. Institute joint business and IT planning. Gather key business and IT leaders once a month so you get into a proactive mode. Let the business people tell you how they’re using BI and how they would like to be using it so you can plan the next releases.
8. Foster a business-savvy IT department. The more you can familiarize your IT people with the business drivers and business problems the better.
9. Develop the right BI architecture to meet the goals. Some organizations build as they go and end up with underpowered infrastructure. Some build everything at once in a big-bang project and they end up with overblown architecture. The best approach is to plan and architect in advace and then build as you go, spending one step at a time.
10. Optimize the human and information resources. Institute continuous measurement and continuous improvement. BI is not something you put in place and forget. You have to go back and reexamine the fundamental assumptions and success of existing projects.

These points are great. What is noticable about them is that as well as ‘BI’ they generally apply to what I’d see is good practice in general – whether for BI projects, EDM projects, data management projects, business rules, enterprise software, knowledge management projects.

In fact, rather than just talking about BI, I think Tracy is doing a great job of selling what I would read as agile (small a) development methods from a business focus. This so, so needs to be done as agile has been the domain of the IT crowd for far too long. Technology and the business are like ying and yang (or at least they should be) and doing agile business and agile technology sit so well together in allowing you to outperform your competitors.

(another)thing that’s clear hear is that 7/10 of the points are about culture, marketing, strategy and people. The hard soft stuff. Only 3 are about tech. I think Tracy’s approach to being a CIO is spot on here. Great piece.

‘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.  

Using BI to gain a competitive edge

This was in response to a post on linkedin  regarding the usage of business intelligence.

Traditional business intelligence on its own is nice. It lets you know what you have done as a business, and a little bit on where you could be doing in the future. I 100% agree with the other posters about MDM/governance and stewardship as essential enablers to efficent BI by the way. As other posters have intimated, this can be of significant value in making decisons.

However, how much impact these decisions have on the bottom line cannot often be measured, particularly within the typical ‘reporting tool’ environment that is normally seen in conjunction with business intelligence. These decisions are also typically tactical – which company to sell to – or strategic – which market to enter, which is only a small part of the scope of decisions made in the organisation.

 To make the most of the information and analytics you have to hand, operational intelligence and operational decision making needs to be integrated to either automate or suggest the decisions made by customer-facing people, and crucially record the effectiveness of this decision so that the model/parameters it is made based on can be adjusted. Without this closing of the loop and the positive feedback, the power of the information in the organisation (and outside) isn’t being used valuably.

Suggest you have a look at James Taylor on the website attached who is evangelistic about this sort of stuff. Links: http://www.smartenoughsystems.com