Category Archives: business-intelligence

Whatever happened to artificial intelligence?

At a recent business analytics event, Lennart Frantzell demonstrated how (at least at a practical level) there has been a shift in business computing from Artificial Intelligence (AI) to Business Analytics:

Using a healthcare example, Lennart explained how 20 years ago AI was used to form a diagnostic method for treating snakebites in Australia. The approach was to look at the cognitive process doctors go through when treating snakebites and build a system of complex algorithms to mimic this process. The emphasis was on the algorithm – not the underlying dataset. Any sub-optimal decisions made by doctors (say as a result of bias in their individual experience) would also reflected in the system.

Fast forward 20 years. In order to treat HIV in Ethiopia, business analytics is being used to crawl over 41,000 HIV treatment histories. The EuResist system takes data from a new patient and matches this against patients who have been successfully treated in the past, so determining the most appropriate treatment. The treatment consists of a cocktail of drugs, in which the proportion of each drug in the cocktail can affect how successful the treatment will be. This obviously adds a layer of complexity to determining the ideal solution. What success are they seeing on this project? Over 78% accuracy, outperforming 9 out of 10 human experts. 

The key difference here compared to the snakebite project is the focus on data. The EuResist project pulls data from disparate databases into a flexible DB2 platform that can be analyzed using business analytics. The algorithms are simpler than those used in AI, but the results can be impressive because the reliance is on exposing trends in the data.

The separation of the algorithms and the data also makes it easier to create products that can be implemented with minimal customization, compared to large AI systems that need to be custom-built. Eg. the underlying technology and methodology for treating HIV in Ethiopia can be applied to looking at Asthma in Western Europe.

As we continue to produce more data (just take a look at the 389,000 datasets the US government makes publicly available), business analytics can play a significant role in turning this data into insight and solve problems that were previously out of the reach of artificial intelligence systems.

See more on this business analytics presentation.

Learn about IBM’s Business Analytics solutions.

McMaster University ties up with IBM Business Analytics to create energy-efficient buildings

Business analytics can play an increasingly important role in academia these days. Recently we heard about how business intelligence could be used to locate students in need of further assistance by mining data on lecture attendance and course performance. This week we see a press announcement detailing how Canada’s McMaster University is using business analytics to make its buildings greener.

So how exactly does the technology help reduce operating costs and cut greenhouse gas emissions?

On the one hand, a series of sensors, actuators and meters collect real-time data on energy consumption and temperature levels. When combined with dynamic-pricing data, this can give an accurate reading of exactly how much it costs to use a certain amount of energy at a certain time of day.

What can McMaster do with all this information?

  • Assess how much given building is costing them to run
  • Track energy usage and cost across the whole campus
  • Forecast what future costs and usage will be based on past performance
  • Simulate different environments and scenarios to understand more about energy usage and cost under different climate conditions
  • Optimize energy consumption based on the forecasts and simulations

Looking across the 60 campus buildings, the system will be able to identify under-performing buildings and the causes of energy inefficiencies. With the help of IBM Business Analytics technologies, McMaster will improve its decision making process and raise the bar for sustainable, cost-saving building management practices.

Lotusphere 2011: building collaborative business intelligence with Cognos 10

Over on the IBM Software Blog, Cognos Product Marketing Manager Brendan Farnand explains just why business intelligence solutions from Cognos have a place at the Lotusphere social business event:

"Everyone involved in a decision or a solution needs to know who else is involved, what transpired before they were asked to contribute and what other ideas are out there for that decision or solution."

Business intelligence shouldn’t happen in isolation. As I’ve pointed out before, many reports from sales figures to customer service levels have added value if key constituents can comment on the results and define follow-up actions. Pairing key functions from the Lotus suite with Cognos Business intelligence allows exactly that:


As I won’t be at Lotusphere this year, I’m looking forward to following Brendan on Twitter

If you can’t make it to Lotusphere, check out this Tech Talk webinar where Brendan highlights Cognos’ built-in collaboration and social networking functionality.

IBM Watson: is artificial cleverness the same as AI?

Let’s start with the obvious: this is the opinion of one mere human. Someone who would fail miserably at the US quiz show Jeopardy: it’s that ‘start-with-the-answer’ approach that just screws me up every time. Not being a native of this soil, I claim it’s just not part of my DNA.

But an IBM supercomputer called Watson (which was indeed conceived on US soil) appears to be performing awfully well at the contest and as such is causing a lot of media attention, much of it centered around the whole field of artificial intelligence (AI) and IBM’s involvement in this area.

As PC World reports, Watson overcame two Jeopardy all-time champs in a practice round recently. How does it do this? The silicon contestant has read countless encyclopedias and other tomes, contains natural language processing capabilities and can even determine how confident it is in its response. Couple this with industry-leading computational power and you have one efficient competitor.

IBM has a history in the development of pitting computers against humans on the cerebral battlefield. In the late ‘nineties, Deep Blue defeated chess grandmaster Gary Kasparov (although Kasparov disputes that he was indeed beaten). However the team behind the Watson project are quick to point out that the level of computing required to deal with the high-level semantic reasoning they are up against is different to the logic-bound nature of chess. Chess is a game of limited moves on an 8×8 grid; Jeopardy a game of infinite words.

I can’t help but think back to my Philosophy of the Mind classes where we studied the Turing test – that black box approach to measure AI proposed by Alan Turing in the 50s. Sometimes called the ‘imitation game’, the concept was that if someone could ask questions to a black box and not discern whether a computer or a person was inside, you could attribute intelligence to the machine on a par to that which us humans enjoy. This Stanford article does a good job of discussing the Turing Test and its objections in some detail.

One objection that stands out is that of origination: could a computer do more than just perform tasks (or deal with questions) set by humans? In the case of Watson, it was a team of people within IBM Research that came up with the idea to build a supercomputer to compete in Jeopardy. The motivations? Showcase technology. A fun work-related project. Team-building. The question is whether a computer could have had the ‘wisdom’ (foolhardiness) to come up with the idea of the project in the first place.

I’d suggest this level of decision-making is a quantum leap beyond the semantic analysis of IBM Watson.

Jonah Lehrer, in the provocatively-titled Proust was a Neuroscientist, uses the filter of art to illustrate what neuroscience is uncovering about the complexity of our intelligence. Within the poetry of Walt Whitman you find the idea that feelings and emotions are born in our bodies, not our minds:

"Antonio Damascio, a neuroscientist who has done extensive work on the etiology of feeling, calls this process the body loop. In his view the mind stalks the flesh; from our muscles we steal our moods."

You can’t separate our thought process from our bodily existence. This could be a problem for a computer lacking flesh and bones.

I don’t just bring this up in the vein of being a contrarian or mean-spirited towards what is quite an astounding piece of computing. I think there is a message here that relates to the technology at the core of Watson: business analytics.

Decision-making within the enterprise happens at different levels and business analytics doesn’t necessarily apply at all of those. For instance, business analytics is ideal at helping a marketer pinpoint prospects who might be interested in a particular offer. It’s less good at determining whether that same marketer should run a conference program if they’ve never run one before. We’re still not close to being able to automate that intuitive part of the decision-making process in business.

Last year I sat in a discussion around decision management and heard from a product marketing manager that a barrier to adoption of business analytics systems is the fear from decision-makers that this technology will take away their jobs (the very same people who normally sign the check on these kinds of purchases). This would suggest we in the field of business analytics need to do a better job of explaining that there are some decisions that can be automated and others that cannot. Business analytics consists of a set of tools that us humans can use to make smarter decisions, but like all tools, it has limits.

So whilst IBM Watson shows what computers can achieve in the human realm, it’s worth bearing in mind (no pun intended) that computers pose little threat to the human realm. The Jeopardy contest that is coming up on February 14 is a battle of one computer against 2 humanoids. If Watson wins, we’re not talking about the dawn of a new era where Jeopardy is played out by tin robots bearing the IBM insignia. We are talking about a triumph of a technology that has applications in healthcare and customer service and beyond – a technology that remains a tool in the hands of us mere humans.

More about IBM Watson, including some wonderful videos on its construction

(Image courtesy of The Doctor Fun Archive)

Foursquare to use predictive analytics to beat Facebook?

There’s a growing battle in the location-based services business between Foursquare and Facebook. Foursquare, with its past emphasis on gaming and status building (who wants to be the mayor of the local laundromat?) is now focusing on a more functional aspect: helping people decide where they should go next. According to a report in Brandweek (backed up by this article on a recent job ad), Foursquare sees offering recommendations as its chance to avoid being squeezed out of existence by Facebook, who, with over 500 million users, is the ostensible gorilla in the room.

How does it plan to do this? Brandweek suggests it will adopt predictive services which are common on sites like Amazon and Netflix:

"Those services crunch behavior data—what movies you watch and books you read—to suggest new products. Foursquare wants to do the same, only with recommendations of real-world activities."

For instance, let’s say you are a sushi freak living in Chicago who’s been active on Foursquare for the last year. You’ve been using Foursquare to capture badges for most of the top local Japanese eateries. Foursquare can see your penchant for fine sushi in the windy city and look across its network for others in your area who share the same passion. It realizes that there is a new joint downtown and can suggest you check this out.

How does this crunching work? The data is mined along a process which runs something like this for each individual visitor:

  • What are the past actions you have recorded
  • What patterns can be determined from your actions
  • Who else in the network is like you
  • Where are the gaps between your actions and their actions?
  • Offer as predictions these actions that people like you have performed

Note, this obviates the need for a user to fill in a vast registration form listing all their likes and interests. The system can figure this out by looking at past behavior.

In terms of making predictions, systems need to be smart enough to factor in elements that can cause shifts in our patterns of behavior:

  • Seasonality (no taste for raw fish when snowing)
  • Change in tastes (eg. pregnancy pushes sushi off the menu)
  • Removing system bias (eg. not only favoring well-established popular places, but allowing new entrants a chance to prove themselves)

Whether Foursquare makes a concerted move in this direction remains to be seen, but as web and mobile applications creep further into every aspect of our existence (with their inherent ability to track behavior), expect to see an increasing use of business intelligence and predictive analytics to create smarter systems offering us more relevant information.

Using structured data analytics to make better business decisions

imageIn the current edition of Analytics, a cross-brand team from IBM (Irv Lustig, Brenda Dietrich, Christer Johnson and Christopher Dziekan) explain IBM’s view of the structured data analytics landscape.

Key to this model are three categories of structured data analysis:

1. Descriptive Analytics: A set of technologies and processes that use data to understand and analyze business performance
2. Predictive Analytics: The extensive use of data and mathematical techniques to uncover explanatory and predictive models of business performance representing the inherit relationship between data inputs and outputs/outcomes.
3. Prescriptive Analytics: A set of mathematical techniques that computationally determine a set of high-value alternative actions or decisions given a complex set of objectives, requirements, and constraints, with the goal of improving business performance.

As the authors explain, this model can help businesses make better decisions, rather than just simply automate standardized processes.

Let’s use the example of a fictional global shoe manufacturer we’ll call ‘Footloose’ to see how each category could be used to increase business performance.

Descriptive analytics

These are your flexible dashboards that let you focus in on key areas of the business. For Footloose, this could be all the standard operations dashboards eg. like the one showing monthly shoe sales by region. Footloose should be able to see how actual sales fared against the forecast. Where there are deviations (say the sales of sandals in Spain has gone through the roof), they can use descriptive analytics to drill-down into the data. They may see that the growth is coming from the Madrid and possibly related to a major marketing push during a hot spell in that region.

IBM Cognos solutions offers this kind of descriptive analytics (including business intelligence) that can be implemented to measure and explore how a company is performing.

Predictive analytics

Here we use data from the past to make predictions about the future. For Footloose, this could include combining seasonal sales variations for a sports shoe with the longer term uptrend they have been seeing for the last few years. Footloose can also use predictive analytics to improve their web presence: they can launch a recommendation engine to suggest what a visitor might want to view next based on what they (and people like them) have looked at in the past (like the book suggestion service Amazon offers).

IBM SPSS offers a set of predictive analytic tools which allow business users to employ predictive insights at the point where decisions are being made.

Prescriptive analytics

How can we achieve the best outcome, whilst addressing any uncertainty in the data? Prescriptive analytics can help us answer this question. Let’s say Footloose has made its prediction about what shoe sales are likely to be over the coming year. Now they just need to figure out how to respond to those predictions. Sales of sandals are expected to remain high in Spain so they need to increase their distribution channel there. How should they achieve this? Increase the fleet of vehicles or buid more (costly) distribution centers.

Footloose can plug the data into an optimization model (costs of building a new plant, buying new trucks, gas) to calculate what would be the most efficient supply chain to deliver the extra required capacity.

IBM ILOG Optimization has technologies specialized for these kind of calculations where there are large data sets with potential uncertainty.

I’ve used this example to present a simplified view of IBM’s approach to structured data analysis and how IBM technologies can be used in tandem to improve business performance. A key advantage of these technologies is that their utility stretches across various industries and applications.

For a fuller explanation of this field, I’d definitely recommend reading the full article in Analytics Magazine

Critical issues in business analytics: James Taylor

Decision management expert and consultant James Taylor was ‘cornered’ recently at the IBM IOD conference and asked to explain the present and future of business analytics. An eloquent speaker and veteran in this field, James does a great job of highlighting the current growth and energy in this space, some of the confusion this has engendered, and the questions you should be asking yourself in determining whether analytics are right for you:

He also highlights three critical issues on which I’ll share my viewpoint:

Where can analytics be employed?

Decisions take place across the organization: from the CEO deciding who to appoint as the new sales director, down to the customer service rep asking if you want to take out a maintenance plan when you buy a new computer. At not all points does it make sense to employ analytics to inform the decision process. If you have highly automated business processes in your organization, then company-wide business analytics may make sense. Alternatively, it may just make sense to use analytics to sharpen up one department such as the marketing operation.

There is a political dimension to this which also has to be considered. It could be that the marketing department has a tight agency relationship who strongly pitch an analytics solution highly tailored for the marketing operation. Whilst this may be able to drive up efficiencies in marketing, it won’t help the support decision process (or possibly cross-sell or up-sell opportunities).

On the other hand, it could be that the IT department, in the interests of cost-cutting, prefer to go with a centralized solution with a narrower maintenance footprint. 

These considerations (which tend to be aggravated in larger organizations) need to be taken into account in addition to the theoretical/modeling questions:

"What are the decisions that drive my business? how do I apply analytics to make a better decision or drive my metrics in the direction I want?"

Figure out how to align Business, IT and Analytics

In the past it was tough enough to engage business and IT departments (which can be heavily siloed and have the kind of relationship you see between Siamese Fighting Fish). But now you’re throwing an analytics team into the tank.

Although I’d suggest that this analytics team can provide the glue that holds together those creative types in marketing and the IT logicians. It’s not unusual to find the analytics practitioners sitting somewhere in a department such as the corporate office. Whilst they may have strong knowledge of the tools, they are also plugged into the business imperative. As long as the importance of their role is realized and they are given due authority, they may well be able to spearhead the implementation of a business analytics solution and its systematic application.

Begin with the decision in mind

Why? So you don’t end up drowning in data that will do nothing to drive your business. James points out that you need to understand what is a good and bad decision (for instance be clear on what a positive or negative outcome looks like).

Just like the scientist needs to understand there is inherent bias in the questions she asks, so you should realize that the decisions you choose to focus on can have a profound effect on your business. For instance just applying analytics to short term decision making (such as maximizing quarterly sales) could pull you out of synch with any strategic objectives and hurt you in future years. If you go overboard using predictive analytics to decide what to offer individual customers next on your website based on their past behavior, you may end up looking like a creepy stalker. Keep an eye out for symptoms of unintended consequences!

James is one of the most prominent/prolific bloggers in the decision management space and can be found at JT on EDM and ebizQ.

In the video James references these IBM technologies: Cognos business intelligence, predictive modeling from SPSS, and business rules and optimization solutions from ILOG.

Analytics warning: what you don’t know could be harming your business more than you think

imageA post by Timo Elliot over on the Forbes blogging community posits that we have a tendency to be overoptimistic on our abilities. For instance, 93% of Americans think they have above-average driving skills.

This notion of our egos over-inflating our perceptions of our abilities carries over into the business world: many successful executives have similarly high opinions of their decision making skills and ‘hence under-invest in fact-based systems and processes that could help us correct our misperception’. The systems Timo is talking about here are business analytics and business intelligence systems.

Now if this is the case, there would be space for competitive advantage by those execs who put trust in these systems when it comes to making business decisions. And yes, in fact this is exactly the finding of a recent study conducted by IBM and MIT Sloan Management. Here is the bottom line:

Top performing companies are three times more likely to be leading users of analytics.

So the companies that are using analytics have a tendency to perform well in their segments. Michael S. Hopkins, editor-in-chief, MIT Sloan Management Review goes even further and suggests that these top performing companies are reaching to further their use of analytics:

"Interestingly, the top performers also turn out to be the organizations
most focused on improving their use of analytics and data, despite the
fact that they’re already ahead of the adoption curve."

If you are not in this top-performing coterie, beware. These are the companies that also stand to widen that gap in their performance against that of their non-analytics-based competition.

When it comes to implementation of business analytics, Timo’s post talks about sharing information and decision-making as widely as possible (garnering the ‘wisdom of the crowd’). We are seeing this feature creep into the latest generation of business intelligence tools. For instance, IBM Cognos has added social networking to the latest version of the flagship product. Although, as Timo points out, there needs to be organizational as well as technological change for this to be effective.

The IBM/MIT study offers further advice on rolling out business analytics solutions, such as tackling the biggest obstacles first. For instance, in the online marketing space, you may want to concentrate on implementing analytics on your largest marketing channel, or on the part of your website that receives the most traffic.

You should also determine first what insights you are after, and then figure out which data you need to help you to get to the answers. Again, in the field of marketing (you may have guessed this is my comfort zone), questions could be ‘What pages on the site normally lead to sales?’ or ‘What frequency of email nurturing works best?’. A good vendor should be able to help you frame the questions and get to the meaningful data – don’t be afraid to ask: ‘what should I be measuring’.  

Read the Timo Elliot post

Read the IBM/MIT study

More on IBM Cognos