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.
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
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
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
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.
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.
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'.
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 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.
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
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
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.
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
More on IBM Cognos
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.
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.
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.
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.
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
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
More about Coremetrics
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.
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.
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)
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.