Business Analytics

IT Tech Trends Survey: what does the future hold for business analytics?

2011-07-17T17:44:22-04:00

The latest IT Tech Trends Survey is now open.

We're looking to hear what you feel are the major drivers and motivators in the tech industry right now. This year we've devoted a considerable chunk of the survey to investigating business analytics - its role in the workplace and where we currently are on the adoption cycle. Typical questions:

Where is it used in the organization?
Where does your organization need to be?
What are the concerns you face?

Think you have the answers? Want to share your perspective? Complete the Tech Trends survey now!

In 2010, more than 50% of the respondents told us mobile application development will overtake other types of development by 2015. And nearly 70% felt cloud computing will become the primary way organizations will acquire IT in the next five years.

So, what exactly happens to the results of this survey? Last year the survey garnered press coverage across the globe, allowing IBM to highlight its offerings for the top trends identified.

We also use the survey here at developerWorks to help shape the strategy for our content and site. It helps us ensure we're delivering relevant content that best fits the need of the 4m visitors who visit us each month.

Take the survey now and pass the word along!

Google: social analytics is a key differentiator

2011-06-23T18:02:52-04:00

According to a recent article in Advertising Age, Google’s social strategy does not involve building social networks to compete with Facebook but rather it is focusing on using social data to build better applications:

“As an example of the current strategy, [Eric] Schmidt talked about getting more information from YouTube users in order to offer more targeted video.”

YouTube already has a fairly robust recommendation engine:

but from Schmidt’s comments, development around this area of exposing social analytics is where they see real business value. This is backed up by moves such as YouTube’s purchase of movie recommendation site Fflick.

How can analytics be used to derive value?

For instance, predictive analytics solutions (like IBM SPSS) can traverse a large inventory of content and make associations based on a visitor’s past behavior and the behavior of their friends in the network. Match this with sentiment analysis, which can be used to look at the conversation around a video to determine whether it is loved or loathed (or somewhere in-between), and suddenly you have a more immersive viewing experience.

This doesn’t just apply to Google and video. Foursquare is apparently taking this approach to differentiate itself as Facebook encroaches into its space with its Places offering.

Whilst analytics can offer differentiation in a hotly-contested area, the issue of privacy has to be addressed. The interfaces can get so good at offering recommendations that they border on being plain creepy. Couple this with the growing paranoia around the extent to which our digital lives are tracked, and suddenly these interfaces appear more Big Brother rather than benevolent Jeeves. One way to address this issue is to be as transparent as possible when exposing social analytics.

So if Eric Schmidt’s comment can be taken at face value, I’d suggest it’s in the context of a growing trend in looking to maximize the value in existing networks rather than racing to build new ones. Social analytics, when handled deftly, can unlock this latent value in social data.

Do you agree?

The YouTube recommendation engine: a lesson in transparent analytics

2011-06-19T00:54:03-04:00

Recommendation engines are all the rage. Whether it is in the realm of social commerce (see IBM Coremetrics Intelligent Offer) or in location-based social applications like Foursquare.

As the attention span of the browsing population shrinks below that of your average goldfish, so the need to create razor-sharp, perfectly honed navigation systems increases. There’s a demand on publishers to use whatever information they have to provide a more contextualized browsing experience.

That’s all well and good, but have you ever looked at a recommendation and wondered what on earth was the system thinking when it picked it? You’ve spent months on the site exploring hardcore thrash metal so why on earth are you being offered a book on floral knitting patterns?

I just went onto Youtube and noticed that they have actually got pretty transparent with their recommendation engine:

Notice the ‘because you watched’.

As we use more analytics systems to build interfaces, being explicit about how decisions are made becomes increasingly important:

Show what determined the recommendation: This answers the question of why on earth am I seeing this recommendation? In this instance YouTube bases this on what you have watched previously, but this could just as well relate to what others with similar interests have liked (the Amazon approach).

Allow you to interact with the recommendation: YouTube allows you to remove recommendations from the list that you don’t think are appropriate. One thing it doesn’t do is spell out whether that feedback is factored into future recommendations. Some systems (such as Pandora and Netflix) use a thumbs-up/down or rating system with the implicit understanding that this information will be fed into the calculations of future recommendations. As James Taylor, the Decision Management expert pointed out to me some years ago, recommendation engines have their limit. If I booked a once-in-the-lifetime trip to Bermuda last year, there’s no point in showing me vacations to Antigua six months later. Allowing me to vote this kind of recommendation down can help systems disentangle one-shot whims from longer term patterns of behavior.

The question of privacy: Being transparent about analytics systems and and how exactly visitors are being tracked can go a long way to allay the growing public fears around the growing mountain of data produced by the internet in general and social networking sites in particular. Indeed, here in California there has been considerable press around a bill to increase the privacy of social networks. Justin Brookman, director of the Project on Consumer Privacy at the Center for Democracy and Technology has said, “I think the idea of telling people what is going on and giving them control over their information from the beginning is a good idea for social networks and others places as well”. Privacy advocates are asking publishers to be more open about how data is being used.

As user interfaces become more reliant on analytics tools to offer a more personalized experience, there are significant advantages to displaying upfront exactly why we are being shown the recommendations we see.

Whatever happened to artificial intelligence?

2011-06-03T15:20:18-04:00

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.

Measuring social business ROI: results from an IBM Jam

2011-05-20T17:30:17-04:00

In February this year, IBM hosted a Jam (a 72-hour online forum with participation from IBM and beyond) on the topic of Social Business.

In case you’re not familiar with the term Social Business, here is definition I hear a lot in IBM corridors:

1: a business that embraces networks of people to create business value
2: a business that is engaged, transparent, and nimble

This goes beyond social media, which is largely the domain of marketing/comms departments to touch on the very fabric of the enterprise, including internal collaboration and social networking with partners and suppliers.

As is the case with just about any discussion around Social Business, the thorny issue of ROI came up. How do you measure the value of this undertaking?

These are some of the metrics Jam participants suggested:

How often the brand is mentioned in social media (marketing/support/product management)
How engaged customers are by how often they comment on or share information about the brand (account management/customer support)
How many customers are being exposed to messaging (marketing/support)
How many customers are active advocates for the brand (support/account management)
How the efforts of these advocates are resulting in new customers or increased traffic (account management/marketing)
How many issues are being successfully resolved—and how quickly (support)
How satisfied customers are and what kind of feedback they are providing (support/account management)

I’ve added in parentheses the departments that have a major stake in those metrics. As you can see, this goes way beyond customer acquisition and the normal domain of marketing/communications. Bottom line: social business monitoring goes way beyond tracking Facebook Likes and Twitter Followers.

Are there key metrics you think should be added to the list? I’d love to hear!

Read more on the Social Business Jam (pdf)

'Measurement, analysis and learning' key bottleneck for marketers

2011-05-18T19:46:01-04:00

In Unica’s recently published ‘State of Marketing 2011’ study, measurement and analytics was identified as the top bottleneck for the 279 marketers polled:

This is the first year that this has registered as the top pain point. I’d suggest one reason for this, could be the maturity of web marketing and the emphasis this discipline puts on measurement.

Interestingly, the survey finds that the key issue marketers face is turning data into actions. There’s a problem with obtaining data, and there’s a problem with converting that data into valuable outcomes.

This relates to an earlier IBM study which showed that businesses who can act on business insights generally perform better. Looks like many marketers realize this but are struggling to turn this into reality themselves.

Peruse the report in its entirety:

Unica's Annual Survey of Marketers 2011

View more presentations from Daryl Pereira.

Submit papers now for Business Analytics Forum

2011-04-06T19:46:30-04:00

This year Information on Demand will be held on October 23-27 in Las Vegas. If you have something you would like to share with this premier business analytics forum, the call for speakers is still open for another 2 weeks.
As a speaker, you could potentially receive a free pass to the conference and will receive guaranteed access to the Speaker Lounge.

The call for speakers closes on April 15 and applicants will be notified by June 3 on whether their session proposal has been accepted.

Full details available here

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

2011-03-04T10:00:00-05:00

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.

Can Twitter sentiment analysis predict outcomes (like the Irish election)?

2011-02-25T12:25:01-05:00

When I was growing up, election coverage was characterized by an exuberant political pundit leaping around large cardboard charts of the UK with the kind of coloring normally reserved for the weather report. The ‘exit polls’ we were familiar with only updated about every four hours and only included those people who were prepared to be cornered by the political researchers hanging around near polling stations.

Fast forward to 2011.

We currently have a general election unfolding in Ireland. The Irish online news site The Journal has been crawling over Twitter, that political social network du jour, using the conversations that happen there to predict which way the election will sway. And so far the headline graphic looks like this:

It’s a great case study in the current status of analytics and throws up some wonderful points that have relevance beyond the Irish political scene.

Data is everywhere

Researchers no longer need to go in search of data. Whilst I don’t deny the added color and in-depth insight from questionnaires, focus groups and other tools used by human researchers (whether in the political or commercial realm), there is rich data out there that you don’t have to force out of people. Social networks like Twitter and Facebook give us access to voluntarily-provided information on social groups. We no longer have to bug people to provide us with data.

Growing importance of social media analysis

Let’s face it, we’ve seen a huge growth in the use of social networks over the last two years (not sure why I pick that time frame, maybe tied up with when Twitter/Facebook buttons first starting appearing in ads and on TV). We’ve taken our social lives online. And the beauty of being online is that everything can be tracked. We leave traces. and when you aggregate all of these, patterns start to appear. Is this level of analysis creepy? The privacy issue definitely has to be considered, however I’d contend that the information is so much more valuable in aggregate (effectively anonymized) than it is at the individual level.

Sentiment analysis can throw a curve-ball

Here is what the volume of conversations around the Irish election shows us:

Now look at the sentiment:

Fine Gael have by far the most conversations. However, much of this conversation is not positive. I’d say from a marketing perspective this is something we need to pay more attention to. Far too often we’re still using raw numbers as a determinant of campaign success. We need to add the sentiment layer on top to understand more of the nature of the conversations we ignite.

Presentation is everything

The first image I highlight in this post is so immediately descriptive. Newspapers have been producing wonderful infographics for decades. In the business world we still end up with reports that look more like this:

(not meaning to pick on anyone, this is just an image that came up in a search)

How much further our story goes if we take time to package it up. Business analytics will only move further into the mainstream if the findings are presented in an easily-consumable fashion.

So, having stuck my neck out in favor of The Journal’s Twitter Tracker, I’ll have to come back next week with some post-election analysis. In the meantime, back to Twitter to watch this election unfold.

IBM Watson: counting down to the Jeopardy challenge

2011-02-14T17:41:08-05:00

I've covered this before, but there is palpable excitement in the air as there are literally minutes before an IBM computer competes against Jeopardy all-time champions Ken Jennings and Brad Rutter (see here for local US show times).

Delaney Turner over on the IBM Software Blog does an excellent job of running through the different ways you can connect and learn more about every aspect of IBM Watson and this fascinating project.

For those interested in some of the specs of Whatson, check out this technical post on Wikibon.

On February 9, Nova aired this breathy backgound piece on Watson’s four year build-up to this event with an in-depth look at technology used and the team that created it.

I thought it’s worth sharing the chapters:

Chapter 1: smartest machine on earth – preview

Watch the full episode. See more NOVA.

Chapter 2: the challenge

Watch the full episode. See more NOVA.

Chapter 3: programming intelligence

Watch the full episode. See more NOVA.

Chapter 4: Watson’s audition

Watch the full episode. See more NOVA.

Chapter 5: machine learning

Watch the full episode. See more NOVA.

Chapter 6: playing the game

Watch the full episode. See more NOVA.

For more information, follow IBM Watson on Twitter.

Business analytics reduces cardiac surgery mortality rate by 50 percent

2011-02-14T01:16:33-05:00

A wonderful case study came out last week of how Sequoia Hospital is using business analytics software to inform heart surgery doctors on when to operate and what is the best post- and pre-op care option.

The hospital is using IBM SPSS predictive analytics software to sift through heavy data spanning healthcare databases, medical precedents and real-world medical cases:

"For instance, the software revealed that an anticoagulant drug often given to patients after a heart attack dramatically increases the chances of serious postoperative bleeding. Based on that information, Sequoia was able to put a protocol in place to stop the drug at least five days prior to surgery to allow the patient's platelets to recover and significantly reduce bleeding events."

Why can business analytics play such an important role in healthcare?

Statistics is at the core of modern medicine. Whether it is measuring the response of a sample group against a control group to decide whether a new drug outperforms the placebo, or whether it's tracking through a national database of asthma sufferers in search of factors leading to increased instances of attacks, the medical profession relies heavily on statistics-based predictions. Software such as IBM SPSS solutions have the tooling to crawl through this data and help medical professionals make more informed decisions.

As more hospitals and medical facilities switch to electronic record systems, the amount of medical data that computing systems can access mushrooms. This requires more sophisticated, powerful applications (such as those that can tie together unstructured data from various sources). The payback is a larger sample group, diminished margin of error, and a performance increase in the delivery of healthcare. Whereas in the past a hospital would have only had its own records as evidence when deciding on a course of action, now state-wide or nation-wide information can be mined.

More information on how IBM SPSS solutions is transforming the medical profession

What would YouTube want with a recommendation engine?

2011-02-07T13:19:03-05:00

Techcrunch recently reported that Google (as the owner of YouTube) is looking at the purchase of Twitter-based movie recommendation site Fflick. Judging by the Fflick site today, this is more than just idle rumor:

What are the implications for YouTube?

On the one hand it signals a more concerted effort from the beefy video sharing site to play nicer with the other social networks in the playground (or at least hold hands with Twitter whilst working out a relationship with arch-rival Facebook). It could mean we see the kind of functionality in YouTube present on other video networks like Livestream: a display of all the Twitter backchannel related to a piece of content. For instance:

See the running commentary down the right? This is more 'chat' than 'comments' with tight integration with Facebook/Twitter.

On the other hand, it also opens up the possibility of YouTube to start mining user data to offer recommendations. How useful is this on a video site? Just look at the Netflix story. The popular US video rental service has made a big deal of its ability to guess what movie you want to add to your rental wishlist. It bases its recommendations on what you've seen in the past, how you rated it, what others like you have seen (and a bunch of other variables even including the day of the week on which you're viewing the site!) Netflix prizes this technology enough to have made it a central part of the site navigation and even paid a team from AT&T $1 M for coming up with a winnning algorithm in 2009.

YouTube has a much bigger collection of content, a wealth of behavioural data through its huge viewing figures. It generally knows less about its visitors than Netflix does as the site doesn't require you to login to engage. Potentially, that's where the Twitter piece comes in to play: you give up some of this information about yourself each time you tweet. Fflick provides the service to tie the tweet back to the video. Fflick also provides the service to pick through your Tweets and use these to determine what content you might like to see next.

This kind of application of predictive analytics is hot right now in the social media space. Foursquare is believed to be using predictive analytics to keep Facebook at bay in the location-based-services sector.

Social media is making us increasingly impatient and we are starting to demand more from our interfaces. Add to that the growing market for hand-held devices that offer precious little space for content, let alone navigation, and you have a compelling case for services using whatever technology they can to pinpoint what you probably want to do next, and serve that up. If they don't engage, the next video-sharing site is only a short URL away.

Lotusphere 2011: building collaborative business intelligence with Cognos 10

2011-01-28T21:41:42-05:00

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?

2011-01-21T13:44:30-05:00

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 8x8 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)

The growing role of predictive analytics in data center management

2010-12-21T11:46:15-05:00

Crisis management is generally a costly business. Switching gears away from your forward-thinking strategy and pulling in resource to deal with issues not on the radar can really stymie growth and efficiency.

Especially in the IT management space.

In the past, the role of the IT manager was largely reactive: as soon as a problem occurs, they would have to jump in and manage the crisis. This was, and continues to be, a costly exercise for IT departments - often costing organizations millions of dollars annually.

Investment in predictive analytics has the potential to drastically reduce the surprises faced by IT management. In a recent article in Enterprise Networking Planet, Drew Robb shows how predictive analytics can be used to monitor networks across enterprises and mine behavioral patterns to get out in front of potential issues like usage spikes and plan for them before they occur. As IT moves towards virtualization and cloud models which allow for flexibility in terms of resource allocation, predictive analytics really comes into its own as a tool to help manage these spaces. For instance, with a cloud-based installation, resources can be deployed or changed in minutes, rather than weeks. If you have multiple users and applications on the installation, predictive analytics can be used to determine where resources should be apportioned prior to any impact on service levels.

Maintenance isn't only the area where predictive analytics play a role.

Steven Sams, IBM’s vice president of Global Site and Facilities Services points out that by 2012 global data storage capacity will need to be 6.5 times what it is today (fueled largely by internet cloud-based services). He recently explained to Forbes' Quentin Hardy how predictive analytics can be used by data center managers to plan for this growth:

"Tech planners need the same kind of big pattern-finding software more commonly used by designers, chief executives, and finance types. Among the new analytic offerings from IBM are cash flow-based scenario software, for figuring out whether to build, consolidate, or do nothing"

Obviously these decisions can have serious implications on business operations and costs. Sams highlights a Chinese bank that has managed to go from 38 to 2 data centers with a cost saving of $180 million a year using this technology. To better serve this market, IBM has launched a predictive analytics tool for use by the Global Business Services division on data center engagements.

As we move into 2011 and beyond, predictive analytics can play a major role in the way IT departments manage data centers and their operations. Given what's at at stake, expect to see a lot more interest in this area.

Foursquare to use predictive analytics to beat Facebook?

2010-12-17T11:29:02-05:00

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.

Optimize SEM and SEO Lead Gen Campaigns with Web Analytics (Webinar)

2010-12-13T13:46:33-05:00

Integrating search engine marketing (SEM) and search engine optimization (SEO) projects and teams is a best practice that can deliver a powerful Virtuous Cycle. Built on the foundation of an analytics platform such as Coremetrics Continuous Optimization Platform, an integrated approach to SEO and SEO can significantly improve the ROI from your web presence.

Multiple surveys and studies have indicated that SEO projects consistently provide extremely attractive returns on investment. Yet eCommerce and online marketing teams frequently struggle to quantify SEO ROI: both prior to the project as part of an internal budgeting process, and after the project to evaluate its success. Using a recent case study of a global powersports company, we will demonstrate how Coremetrics Digital Agency worked with the client to optimize their lead generation engine by integrating Search Engine Marketing with Coremetrics' Web Analytics. Building on this SEM experience we then targeted keyword phrases with the potential for the highest, measurable SEO ROI.

We will show the virtuous circle at play between SEO and SEM:

For instance, you can see significant improvements to your SEM campaigns by applying lessons learnt from analyzing your SEO efforts (such as which keywords drive most interactions).

Attend this upcoming seminar with Coremetrics' John Zoglin, Senior Director, Search Marketing Services to learn more.

Date: Wednesday, December 15, 2010
Time: 1:00 PM EST | 10:00 AM PST
Register now!

More about Coremetrics

Using structured data analytics to make better business decisions

2010-12-08T15:31:56-05:00

In 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

2010-12-03T20:05:59-05:00

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:

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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

2010-11-22T16:17:24-05:00

A 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

Cognos 10: what does social networking bring to business intelligence?

2010-11-18T15:31:51-05:00

In my previous life as a webmaster I was called on to develop monthly web performance reports for consumption by the whole marketing organization. At one time these had been documents that were mailed around, but we decided the best approach was to build a web interface with charts and diagrams that would be updated monthly.

We showed standard metrics. Stuff like this:

Each month I'd send out an email with a link to the latest report with my notes on site performance each month. For instance, I'd point out from looking at the graph on the left that although traffic had dropped this month, this is a seasonal variation. For the graph on the right, I'd say I wasn't sure why our search traffic had grown: this is something I'd investigate with the various individuals running search campaigns (meaning for 90% of the people on the email distribution, the answer would end up in an Inbox far, far away).

How much smarter we could have been if we'd have had access to a system like Cognos 10 that marries business intelligence/analytics with social networking capabilities that allow you to add that layer of insight on top of the data.

For instance, here's a standard chart:

and here's the same chart with the addition of related Lotus Connections discussions:

Going back to my examples above, if I was showing yearly traffic figures, I can use this discussion area to record what I know about seasonal variations. Now if someone receiving the report didn't agree with my evaluation, they are free to comment on it. As for the discussion I'd need to have with my search marketing folks about why the search traffic has spiked, I can set this up from the same page:

...with the thread of the discussion unfolding below the graphs and charts to which it relates. Anyone wishing to follow up on the status of the question can go to that page and scan the thread to see the outcome.

I should point out that the Cognos folks have taken this a step further: integrating activities as well as discussions. The data is now more 'actionable'. Let's say you are looking at global sales data and you notice a slump in a certain geographic region. You can use the new functionality to setup an Activity to address this, with a number of associated tasks assigned to different sales people or teams. Over time you can evaluate their actions against the performance data all from within the same interface.

And while we're talking about the sales team, another new feature in Cognos 10 makes it easier to access reports while on the go, directly from your smart phone:

One feature I'd love to see in future releases of Cognos is the ability to tie conversations/activities to given points on a graph, as opposed to just having these attached to the page of a report. As an example, the popular SoundCloud music hosting service has gained a lot of traction by allowing music enthusiasts to comment on a particular point in a music track:

(each blue bar represents a separate comment)

Maybe something for a future release?

Delaney Turner has a post with more information on Cognos 10, including a link to an excellent interactive demo.

Also check out the Cognos product pages.

How CIPCO use IBM SPSS Statistics to improve energy supply

2010-11-17T13:29:26-05:00

SPSS have put out a case study showing how the Central Iowa Power Cooperative (CIPCO) switched from using Excel spreadsheets to IBM SPSS Statistics to optimize those decisions an energy utility provider has to make: capital planning, utility rate setting, power purchases, emissions tracking and more.

Given that energy requirements: supply, demand, price can shift on an hourly basis, tracking these across the 3,000 power nodes CIPCO provides is no trivial matter. As CIPCO's Lisa Sell points out, "IBM SPSS Statistics gives us the power and flexibility to keep track of everything, with very little manual manipulation." The growing wind farm industry in Iowa adds even more uncertainty into the equation. Sell and her team use Statistics to analyze the dynamic pricing of wind-generated energy and its effect on the rest of the system.

In addition to forecasting and planning the IBM SPSS solution also helps CIPCO keep compliant with the annual power generation and cost reporting required by government agencies, as well as calculating the profitability of the various plants.

Read the full case study

IBM's marketing automation solutions: a primer

2010-11-15T14:14:50-05:00

Christopher Hosford over at BtoB Magazine ran an interesting piece on IBM's foray into the field of marketing automation focusing on the recent spate of acquisitions here at IBM. I thought it would be worth expounding on how each of these acquisitions fits into the notion of a holistic marketing automation solution - using an example that hopefully most of us can relate to: internet retail.

Coremetrics

Internet retailers use web analytics to explore which parts of their site are most effective, which channels are driving most visitors and what are the common paths taken by visitors who buy. Conversely, analytics can also highlight problem areas such as product lines that receive heavy traffic but little conversion to sale, expensive marketing channels that provide little revenue-generating traffic and navigational bottlenecks. You can take this further using a solution such as Intelligent Offer, which exposes the analytics to the visitor: much like the recommendation engine used by Amazon bookstore on their individual listing pages to say 'if you like this book, you may be interested in these books too'.

Unica

An internet retailer that exploits different marketing channels, eg. email, web, social networks, can use Unica's Interactive Marketing solution to track responses across the different channels and use this data on past behavior to tailor future messaging. It also allows you to uncover those prospects that have been most responsive and are more likely to cross over and become customers.

Netezza

Netezza can help the internet retailer wherever there are large sets of structured or unstructured business data. For instance you can use Netezza for bid price optimization of search marketing campaigns where you might have 100s or 1000s of keywords covering product inventory, coupled with multiple text ads and landing pages, leading to millions of permutations. Predictive analytics can help you determine what is the optimal paid search campaign structure.

Sterling Commerce

When it comes to order processing, Sterling Commerce can help internet retailers ensure consistency across different channels (eg. keep consistency across different web sites with different experiences). As one example, the system can help dealing with coupons and the correct application of discount codes across all channels.

I should point out that these are only individual examples. Each of these acquisitions have plenty of other offerings, many of which touch on different components of marketing automation.

I'd be remiss not to mention Cognos, SPSS and ILOG, all of whom offer business analytics offerings that can be customized in a marketing automation context.

IBM’s Business Analytics solutions are set to mature as these acquisitions are woven further into the fabric of each other and the expansive IBM quilt of offerings. Early indications are positive however, as IBM's Business Analytics revenue has grown 12% over the last year to a net income of $3.6 billion. This would suggest we're in for some interesting times ahead!

BtoB Magazine article on IBM's marketing automation solutions

Jeff Jonas interview with Forbes: IBM's business analytics strategy

2010-11-09T10:25:52-05:00

Jeff Jonas, chief scientist at IBM's Entity Analytics group, recently sat down with Forbes' Kym McNicholas to explain IBM's strategy in the growing business analytics space.

Here are some of the key themes Jeff discussed:

Enhanced customer service
Business analytics allows businesses to make smarter decisions at the point of interaction, whether that is an application for a credit card, or an order coming in to a call center. Another facet is the growth of geolocation services like Foursquare and Gowalla giving us much richer information on people's movements. Using this data, when people search for information on the web, we can incorporate analytics into the results, making them more local, more relevant.

HR and the hiring process
Tying together an applicant's information across disparate systems can help detect issues, such as whether that person has previously shoplifted from the store to which they are applying (it happens!).

Data security
As companies have more and more information on their customer base, keeping this data secure is a growing problem. Analytics can help: for instance companies can see who accesses data to help uncover data thefts and violations. Analytics tools can also be used to add a further level of encryption, so even if data is stolen, records cannot be matched back to individuals.

Forbes were kind enough to create a video of the interview too:

If you don't know Jeff, among his many achievements, led the development of facial recognition software used to thwart aggressive card count teams (such as the MIT team covered in the book 'Bringing Down the House') for the casino industry.

Read Jeff's full bio.

Read Jeff's blog.

In the news: advanced business intelligence from Cognos 10

2010-10-25T20:14:09-04:00

The big news today from the IBM IOD Global Conference and Business Analytics forum is the unveiling of Cognos 10.

The most common feature brought up on news sites and blogs is the ability Cognos 10 opens up to access business intelligence on a mobile device: be it a Blackberry, iPad or iPhone. Bloomberg picks up on a case study from the car rental industry:

"Hertz Global Holdings Inc., the largest car-rental company, will gather survey data from customers via text messages, and use IBM’s software to analyze responses. Employees can then receive real-time information on potential problems, such as wait times at car-rental locations, and adjust accordingly. Office Depot Inc. also is using the software to gather, analyze and report store-performance data."

Meanwhile, Dr Dobbs focuses on the move away from charts and graphs to "broader analysis tools supported by built-in images and user help videos". They also highlight advances in predictive analytics and 'what if' scenario evaluations.

Over on PC World, there is coverage of the tie-up with other parts of the IBM portfolio. Linkage with Lotus Connections, IBM's social networking platform, will "allow users to engage in conversations about business information and get more value out of the software". Cognos 10 also includes a statistics engine from the SPSS suite.

ITBusinessEdge points out the importance of business intelligence in the current economic climate: "business executives are looking for simplified access to more relevant information they can trust".

More information on Cognos 10
More information on the IBM Business Analytics Forum
#baforum

IBM Business Analytics Software protecting children in care in Nevada

2010-10-25T15:21:48-04:00

As the Business Analytics Forum gets underway in Las Vegas, eWEEK feature an article on how IBM's analytic software is helping Clark County Family Services Department in Nevada improve the delivery of social services. Prior to using IBM analytics, spreadsheets were used to monitor care workers and the level of service provided. Due to population increase and the need to conform to state legislation and policies, the department needed a solution that would make reporting easier, help the department comply with new regulations, and measure business performance.

According to Eboni Washington, a IT supervisor in the Family Services Department,

“Before this we had a lot of children not being seen each month. And now we have an automated system, rather than some workers keeping a hand count of who they have seen and what they have done each month.”

For more information, read the full article in eWEEK.

#baforum

Business Analytics Forum at IOD

2010-10-24T18:14:06-04:00

Welcome to this new IBM Business Analytics blog to coincide with the major IBM business analytics event about to kick off in Vegas.

Whether or not you are attending, here are some useful links where you can get more information on the event:

Keep posted for more updates as this event unfolds.

#baforum