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April 30, 2007
Presentation Skills for Business Intelligence - Nine Points of Roguery.
A long post today, I hope it is interesting.
It’s funny how an idea can be dormant for ages, then suddenly crops up everywhere again. I used to have a simple method for structuring presentations – specifically, where I had to present results of an analysis, and often a related proposal. Most of us in the BI world do this regularly. I had not shared the technique much, but in recent weeks I have found myself describing it in detail several times, sitting down with hassled analysts helping them pull together summary presentations.
The method is simply an outline that you can use to structure your presentation for best impact. I used to call it The Nine Points of Roguery – there is an old fiddle tune of that name – but please don't think I am suggesting that you should be roguish with your clients. Still, the method does describe nine points, as follows:
• Make 3 points that your audience will already understand
• Enhance and extend these three points
• Introduce three new findings from your work
Easy! I’m going to use an example to illustrate some of the ideas. Imagine that I have been tasked with examining customer data quality for a client and coming up with some suggestions for improvement. Here goes …
Make three points your audience already understands
You will connect best with your audience when you share common ground. By speaking briefly to a few familiar points, you show understanding of their needs. You can even make it clear that you know that they know. Of course, with your business experience, you understand this even better than I. Do not overdo it – flattery will get you nowhere – but it is good if your audience feels you address them as equals. You are all smart people, tackling a non-trivial issue.
What three points should you make? Naturally, the details depend on context, but do choose engaging, substantive, topics. Get to the core of your audience’s problems. If you need more structure, try the following:
Strategic impact. How does the current topic affect your audience’s long-term goals? How could a successful project help? What would failure look like?
Example: Direct marketing is a critical component of your client’s customer acquisition strategy. Poor data quality wastes money by inappropriately marketing to the wrong customers. It also risks alienating the public and damaging the company’s reputation.
A tactical concern. Do not spend too long on strategy: you will be aiming too high. What immediate concerns face your listeners? What decisions will they make today or tomorrow? Choose a tactical problem that concerns them directly.
Example: From mergers and acquisitions, your client has multiple customer data sources. There is an immediate need for a single version of a customer across the enterprise.
An obstacle. Why is the current issue not easy? Get into detail: is there a financial, technical or human barrier to success? Your listeners understand that difficulties exist. Still, you are reassuring them, in effect, that it is not stupid to be in their situation.
Example: Their most important source system is effectively legacy software. It has been used for many years, but is not compatible with more modern CRM or data quality applications.
Enhance your three points
You and your audience now have a baseline of shared understanding. Next, you should show that you have explored their issues further. It can be tempting to pull a rabbit from your hat, dazzling your audience with some revelation that resolves their problems at one stroke. In fact, most often you will not have such an eye-opener. Even if you do, my advice is to wait. In all cases, you must build authority first. Your presentation is not the Sermon on the Mount. You cannot simply announce “Ye have read … but I say unto you …” unless your authority is unquestionable.
So, develop your themes. When you present new findings later, the audience will appreciate your knowledge and experience. You can build this influence in several ways. Indeed, using a variety of techniques will be more appealing.
Extend. Expand one of your original topics by considering how the matter changes with time, geography, scale or some other dimension. Was this problem easier in the past? Why? Does the passage of time have an effect, making things better, worse, smaller, or larger? Could this impact of this concern vary with geography? Perhaps the US division suffers more than the European division. You get the idea. You are building authority by going beyond the obvious.
Example: Cleaning your client’s customer data is not a one-off action. Accurate operational data may be critical, but so is the ability to analyze customer behavior over time. Because customer data changes constantly, the client needs good quality historical data too.
Contradict. I’m contrary by nature, so I like this one. However, regardless of my own predilections, finding contradictions is an excellent way in which to expand a topic. Few issues that you cover will be simply positive or negative. Your task here is to find the silver-lining in the cloud, or, vice-versa. The underlying message is, naturally, that not only is the subject not simple, but also that your understanding of it is not simplistic.
Example: Creating a single version of your customer data from your client’s various mergers and acquisitions is a great vision. However, that single version will be an even more valuable asset than before. As such it may require additional administration, greater security, high availability and disaster recovery planning.
Personalize. Your clients are human. (If not, mail me: I would love to know more.) People relate most directly to the needs and experiences of other people. So, in every presentation, be sure to expand at least one topic to cover personal impacts. How does this concern affect the daily work of the manager, the DBA, the salesperson? Use named individuals if you like, but at least ensure that your presentation is not abstract. It should be rooted in the effects on real people of the problems you are covering.
Example: It is increasingly difficult to find staff skilled in the company’s legacy applications. There remains an administrator, Julie, and one developer, Bob. Julie spends too much time preparing dumps of text files for integration with other applications. Bob is stretched developing new reports to keep up with changing compliance requirements.
Introduce three new findings from your work
By now you have demonstrated an understanding of your audience’s needs. Further, you have shown experience and authority. It is now time for new results and recommendations. The structure of this section will, again, depend on the specific context. However, if you struggle to get that right, I would suggest that you invert one of the patterns we used earlier. Start with an insight or recommendation at a personal level, and then show new tactical and strategic ideas.
Personal insight. Do your recommendations or discoveries directly affect individuals, whether employees or customers? If so, be prepared to talk to that very directly. Do not cover every impact: just choose one as an example. A well-chosen example can establish an authentic connection with the audience.
Example: Everyone in your audience has received junk mail. Many will have received duplicate mailings from one company. From your research, you can show that missing out a good target may be less costly exasperating a good target. So, you recommend not only consolidating and cleaning customer data, but also aggressively purging duplicates. By setting the proposal in a personal context, to which the audience can relate, you can make this case effectively.
Tactical recommendation. This should be the pivotal moment. It is when you make an actionable and material recommendation. You may have many tactical points – specific steps your client can take to achieve their strategic goals. Should you not present them all? I would suggest not: you risk overwhelming your audience. Better to choose the most impactful and representative tactic and speak to it well. Your proposal should relate to one of the issues you have raised earlier. This is also a good time to address ROI and costs associated with the problem and solution. Typically, it is easier to evaluate ROI for a tactical recommendation rather than an entire strategy. It may also be more credible to your audience.
Example: You recommend migrating the legacy system to a new line-of-business or CRM application. Naturally, there are many sub-recommendations to be found in the report. However, overall costs can be estimated here, and supported with SWOT, cost-benefit or gap analyses.
Strategic insight and observation. Now you can close the loop, referring back to your very first point. You have established common ground with your audience and demonstrated that you understand their strategic, tactical, even personal, concerns. You have specific recommendations based from your analyses. Now, you should show that your suggestions are not only tactical, but that they can have strategic impact too. Relate your point directly to the corporate strategies of your client. If your audience does not primarily comprise strategic decision makers, you can still make this point: just do not dwell on it for too long and be sure to relate any suggestion to their own work.
Example: Direct marketing is still critical to your client’s customer acquisition strategy. With improved customer data quality you can significantly move beyond that approach. You can use your customer data to grow stronger customer relationships. Perhaps now, with a single version of the customer to hand, an effective loyalty scheme is practical across all the divisions of the enterprise, which previously poor data quality prevented.
And that’s the outline. Nine simple points which help you balance the client’s current understanding with your new insights. If you try it out, do let me know how it works for you.
Posted by Donald Farmer at 3:19 PM | Comments (0)
April 16, 2007
Retailer found guilty of OLAP
"It's the most flagrant case of aggregation I have ever seen," said the prosecutor.
Ok, I'm kidding. Yet today I did find a headline in the Charlotte Observer: "Lenders accused of data mining." In this case, the financers in question were illegitimately searching a database of student borrowers. There is no doubt that the public have valid concerns over potential misuse of data, but it is awkward (for those who used the term in a rather more limited way) to see the good name of a useful technology tainted in the process.
This new usage - data mining as database search – is easy to see in a press release from Senator Russ Feingold. Data Mining, he says, is “is a broad search of public and non-public databases in the absence of a particularized suspicion about a person, place or thing.”
Most vendors who, until recently, described their technology as data mining now talk about predictive analytics. It is an attractive phrase for vendors and commentators, having a technical ring to it, without being intimidating. Currently I use this idiom myself, much more than data mining. Unfortunately, the term is not entirely accurate. Many uses of data mining, predictive analysis or knowledge discovery (an even rarer term these days) are primarily descriptive, to enable business analysts to understand their data better, without querying the model for predictions.
As it happens, while I may regret the inconvenience that a useful term has drifted from my own usage, I see no reason to complain. I have no time for those who talk about the “real” meaning of words. The current meaning of a word or phrase is determined by its usage and I am not going to fight that. Between friends, I may continue to have a gay old time chatting about data mining; but in public, I need to be aware that the meaning has moved on.
However, I do have to wonder what phrase the press will next appropriate to capture the public’s finely nuanced paranoia. I could take a guess. Senator Feingold, points out that data mining in his sense requires “a combination of intelligence data and personal information, including an individual's traffic violations, credit card purchases, travel records, medical records, communications records, and virtually any information collected on commercial, public or private governmental databases.” I think we may have to start looking around for an alternative to CDI …
Posted by Donald Farmer at 2:24 PM | Comments (1)
April 1, 2007
Beer and diapers revisited - not just an urban legend
These are days of turmoil and upheaval in the Business Intelligence world. So it’s good to hear of a story with a happy ending. And what could be happier than discovering that an urban legend is, in fact, true? You know the old tale of the supermarket chain mining their data to discover that sales of beer were uncannily linked to sales of diapers. And every time you hear it, someone is on hand to debunk it.
But wait. Of course, you already know, from coverage on the B-Eye-Network, that leading French retailer, Carremart, have invested heavily in a ground-breaking new BI system. Yesterday, at the end of March, they closed their first quarters’ books using the new technology and by early morning April 1st, they had already completed their first analysis. An inside contact mailed me immediately to say that they had indeed found a correlation between sales of beer and diapers. As you can imagine, I was hardly able to contain myself, so I telephoned Carremart at their Mond Rouge headquarters to interview Avril Poisson, the senior “Data Attendant.” You can read my interview with her below ...
First Avril, tell us about your interesting job title. What is a Data Attendant? As a Data Attendant, I look after the needs of the data.
So you’re a Data Steward? That’s such an old term. Yes, we used to have male “Data Stewards” and female “Data Hostesses” but we felt those terms set the wrong tone. “Data Attendant” describes my role better. The data has a long journey from source to destination. It’s my job to ensure that the right data is in the right place and is refreshed when necessary.
As an Attendant, then, it’s your job to load as much data as you can into a highly compressed structure as quickly as possible? Oh no, this is business data. We don’t compress it so much and it is always loaded first.
And if the data has a lot of baggage with it? Baggage? You must mean metadata. It is of course better if the metadata travels with the data. At least it should be tracked along with the data so they eventually can be linked. But honestly, nobody in this business cares that much – quite often the metadata arrives much later, and sometimes it gets lost altogether.
Tell me about this breakthrough analysis, Avril. “Beer and diapers” turned out to be true after all? Ah, not quite. I am sorry if you misunderstood. Perhaps a bad translation. However, our analytics experts were able to positively correlate sales of large quantities of beer with - how you say? – adult diapers.
Depends? No, we’re absolutely certain. The more beer sold, the more adult diapers were needed.
Interesting. What technology do you actually use in you analytics department? We use BI tools from our database vendor, Debacle.
Which consists of what exactly? Today it’s a layer of Fiebel CRM over a relational bed, bound with ConFusion middleware and ELTLE from Sumoptions. All presented at the last moment with a glossy presentation layer of Hyperinflation.
And have you found the Debacle solution to be well integrated? Certainly. For example, all the consultants arrived on the same flight. They even shared a minivan to our headquarters – it’s good to keep costs down. We had hoped to see their data mining guy, but he missed the 9am flight from San Francisco due to heavy traffic on the 101. A shame, but you can’t predict these things.
But six months on and you’re happy with final results? To be honest, the results are not quite final. In fact, we’re still installing the Debacle system.
Still installing? So, how did you arrive at your conclusions? With the traditional methods. We copied and pasted everything into Excel and drew some charts.
And are you confident in your analysis? Good question. How you say? Depends …
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Posted by Donald Farmer at 11:05 AM | Comments (12)
