Four Vendor Views on Big Data and Big Data Analytics: IBM

Next in my discussion of big data providers is IBM.   Big data plays right into IBM’s portfolio of solutions in the information management space.  It also dove tails very nicely with the company’s Smarter Planet strategy.  Smarter Planet holds the vision of the world as a more interconnected, instrumented, and intelligent place.  IBM’s Smarter Cities and Smarter Industries are all part of its solutions portfolio.  For companies to be successful in this type of environment requires a new emphasis on big data and big data analytics.

Here’s a quick look at how IBM is positioning around big data, some of its product offerings, and use cases for big data analytics.

IBM

According to IBM, big data has three characteristics.  These are volume, velocity, and variety.   IBM is talking about large volumes of both structured and unstructured data.  This can include audio and video together with text and traditional structured data.  It can be gathered and analyzed in real time.

IBM has both hardware and software products to support both big data and big data analytics.  These products include:

• Infosphere Streams – a platform that can be used to perform deep analysis of massive volumes of relational and non-relational data types with sub-millisecond response times.   Cognos Real-time Monitoring can also be used with Infosphere Streams for dashboarding capabilities.
• Infosphere BigInsights – a product that consists of IBM research technologies on top of open source Apache Hadoop.  BigInsights provides core installation, development tools, web-based UIs, connectors for integration, integrated text analytics, and BigSheets for end-user visualization.
• IBM Netezza – a high capacity appliance that allows companies to analyze pedabytes of data in minutes.
• Cognos Consumer Insights- Leverages BigInsights and text analytics capabilities to perform social media sentiment analysis.
• IBM SPSS- IBM’s predictive and advanced analytics platform that can read data from various data sources such as Netezza and be integrated with Infosphere Streams to perform advanced analysis.
• IBM Content Analytics – uses text analytics to analyze unstructured data.  This can sit on top of Infosphere BigInsights.

At the Information on Demand (IOD) conference a few months ago, IBM and its customers presented many use cases around big data and big data analytics. Here is what some of the early adopters are doing:

• Engineering:  Analyzing hourly wind data, radiation, heat and 78 other attributes to determine where to locate the next wind power plant.
• Business:
  • Analyzing social media data, for example to understand what fans are saying about a sports game in real time.
  • Analyzing customer activity at a zoo to understand guest spending habits, likes and dislikes.
• Analyzing healthcare data:
  • Analyzing streams of data from medical devices in neonatal units.
  •  Healthcare Predictive Analytics.  One hospital is using a product called Content and Predictive analytics to understand limit early hospital discharges which would result in re-admittance to the hospital

IBM is working with its clients and prospects to implement big data initiatives.  These initiatives generally involve a services component given the range of product offerings IBM has in the space and the newness of the market.  IBM is making significant investments in tools, integrated analytic accelerators, and solution accelerators to reduce deployment time and cost to deploy these kinds of solutions.

At IBM, big data is about the “the art of the possible.”   According to the company, price points on products that may have been too expensive five years ago are coming down.  IBM is a good example of a vendor that is both working with customers to push the envelope in terms of what is possible with big data and, at the same time, educating the market about big data.   The company believes that big data can change the way companies do business.  It’s still early in the game, but IBM has a well-articulated vision around big data.  And, the solutions its clients discussed were big, bold, and very exciting.  The company is certainly a leader in this space.

The Inaugural Hurwitz & Associates Predictive Analytics Victory Index is complete!

For more years than I like to admit, I have been focused on the importance of managing data so that it helps companies anticipate changes and therefore be prepared to take proactive action. Therefore, as I watched the market for predictive analytics really emerge I thought it was important to provide customers with a holistic perspective on the value of commercial offerings. I was determined that when I provided this analysis it would be based on real world factors. Therefore, I am delighted to announce the release of the Hurwitz & Associates Victory Index for Predictive Analytics! I’ve been working on this report for a quite some time and I believe that it will be very valuable tool for companies looking to understand predictive analytics and the vendors that play in this market.

Predictive analytics has become a key component of a highly competitive company’s analytics arsenal. Hurwitz & Associates defines predictive analytics as:

A statistical or data mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data (together or individually) to determine future outcomes. It can be deployed for prediction, optimization, forecasting, simulation, and many other uses.

So what is this report all about? The Hurwitz & Associates Victory Index is a market research assessment tool, developed by Hurwitz & Associates that analyzes vendors across four dimensions: Vision, Viability, Validity and Value. Hurwitz & Associates takes a holistic view of the value and benefit of important technologies. We assess not just the technical capability of the technology but its ability to provide tangible value to the business. For the Victory Index we examined more than fifty attributes including: customer satisfaction, value/price, time to value, technical value, breadth and depth of functionality, customer adoption, financial viability, company vitality, strength of intellectual capital, business value, ROI, and clarity and practicality of strategy and vision. We also examine important trends in the predictive analytics market as part of the report and provide detailed overviews of vendor offerings in the space.

Some of the key vendor highlights include:
• Hurwitz & Associates named six vendors as Victors across two categories including SAS, IBM (SPSS), Pegasystems, Pitney Bowes, StatSoft and Angoss.
• Other vendors recognized in the Victory Index include KXEN, Megaputer Intelligence, Rapid-I, Revolution Analytics, SAP, and TIBCO.

Some of the key market findings include:
• Vendors have continued to place an emphasis on improving the technology’s ease of use, making strides towards automating model building capabilities and presenting findings in business context.
• Predictive analytics is no longer relegated to statisticians and mathematicians. The user profile for predictive analytics has shifted dramatically as the ability to leverage data for competitive advantage has placed business analysts in the driver’s seat.
• As companies gather greater volumes of disparate kinds of data, both structured and unstructured, they require solutions that can deliver high performance and scalability.
• The ability to operationalize predictive analytics is growing in importance as companies have come to understand the advantage to incorporating predictive models in their business processes. For example, statisticians at an insurance company might build a model that predicts the likelihood of a claim being fraudulent.

I invite you to find out more about the report by visiting our website: www.hurwitz.com

Informatica announces 9.1 and puts stake in the ground around big data

Earlier this week, Informatica announced the release of the Informatica 9.1 Platform for Big Data. The company joins other data centric vendors such as EMC and IBM by putting its stake in the ground around the hot topic of Big Data. Informatica defines Big Data as, “all data, including both transaction and interaction data, in sets whose size or complexity exceeds the ability of commonly used technologies to capture, manage and process at a reasonable cost and timeframe. Indeed, Informatica ‘s stance is that Big Data is the confluence of the three technology trends including big transaction data, big interaction data and big data processing.” In Informatica parlance the transactional data includes OLTP, OLAP, and data warehouse data; the interaction data might include social media data, call center records, click stream data, and even scientific data like that associated with genomics. Informatica targets native, high performance connectivity and future integration with Hadoop, the Big Data processing platform.

In 9.1 Informatica is providing an updated set of capabilities around self-service, authoritative and trustworthy data (MDM and data quality), data services and data integration. I wanted to focus on the data services here because of the connection to Big Data. Informatica is providing a platform that companies can use to integrate transactional data (at petabyte scale and beyond in volume) and social network data from Facebook, LinkedIn, and Twitter. Additionally, 9.1 provides the capability to move all data into and out of Hadoop in batch or real time using universal connectivity to including mainframe, databases, and applications which can help in managing unstructured data.

So, how will companies utilize this latest release? I recently had the opportunity to speak with Rob Myers, an Informatica customer, who is the manager of BI architecture and data warehousing, MDM, enterprise integration for HealthNow. HealthNow is a BlueCross/BlueShield provider for parts of western New York and the Albany area. The company is expanding geographically and is also providing value added services such as patient portals. It views its mission not simply as a claims processor but as a service provider to healthcare providers and patients. According to Rob, the company is looking to offer other value added services to doctors and patients as part of its competitive strategy. These offerings may include real time claims processing, identifying fraudulent claims, or analytics for healthier outcomes. For example, HealthNow might provide a service where it identifies patients with diabetes and provide proactive services to them to help manage the disease. Or, it might provide physicians with suggestions of tests they might consider for certain patients, given their medical records.

Currently, the company utilizes Informatica PowerCenter and Informatica Data Services for data integration including ETL and data abstraction. HealthNow has one large data warehouse and is currently building out a second. It is exposing data out to a logical model in data services tier. For example, its member portal utilizes data services to enable members to sign in and in real time, integrate 30-40 attributes around each member including demographic information, products, and eligibility for certain services into the portal. In addition, the company’s actuaries, marketing groups, and health services group have been utilizing its data warehouses to perform their own analysis. Rob doesn’t consider the data in these warehouses to be Big Data. Rather they are just sets of relational data. He views Big Data as some of the other data that the company currently has a hard time mining, for example data on social networks and the unstructured data in claims and medical notes. The company is in the beginning phase of determining how to gather and parse through this text and expose it in a way that it can be analyzed. For example, the company is interested in utilizing the data that they already have together with unstructured data and providing predictive analytics to its community. HealthNow is exploring Hadoop data stores as part of this plan and is excited about the direction that Informatica is moving. It views Informatica as the middleware that can get the trusted data out of the various silos and integrated in a way that it can then be analyzed or used in other value-added services.

It is certainly interesting to see what end-users have in mind for Big Data and, for that matter, how they define Big Data. Rob clearly views Big Data as high volume and disparate in nature (i.e. including structured and unstructured data). There seems to be a time dimension to it. He also made the point that its not just about having Big Data, it’s about doing something that he couldn’t do before with it – like processing and analyzing it. This is an important point that vendors and end-users are starting to pick up on. If Big Data were simply about volume of different kinds of data, then it would be a moving target. Really, an important aspect of Big Data is about is being able to perform activities on the data that weren’t possible before. I am glad to companies thinking about their use cases for Big Data and vendors such as Informatica putting a stake in the ground around the subject.

Five Analytics Predictions for 2011

In 2011 analytics will take center stage as a key trend because companies are at a tipping point with the volume of data they have and their urgent need to do something about it. So, with 2010 now past and 2011 to look forward to, I wanted to take the opportunity to submit my predictions (no pun intended) regarding the analytics and advanced analytics market.

• Advanced Analytics gains more steam. Advanced Analytics was hot last year and will remain so in 2011. Growth will come from at least three different sources. First, advanced analytics will increase its footprint in large enterprises. A number of predictive and advanced analytics vendors tried to make their tools easier to use in 2009-2010. In 2011 expect new users in companies already deploying the technology to come on board. Second, more companies will begin to purchase the technology because they see it as a way to increase top line revenue while gaining deeper insights about their customers. Finally, small and mid sized companies will get into the act, looking for lower cost and user -friendly tools.
• Social Media Monitoring Shake Out. The social media monitoring and analysis market is one crowded and confused space, with close to 200 vendors competing across no cost, low cost, and enterprise-cost solution classes. Expect 2011 to be a year of folding and consolidation with at least a third of these companies tanking. Before this happens, expect new entrants to the market for low cost social media monitoring platforms and everyone screaming for attention.
• Discovery Rules. Text Analytics will become a main stream technology as more companies begin to finally understand the difference between simply searching information and actually discovering insight. Part of this will be due to the impact of social media monitoring services that utilize text analytics to discover, rather than simply search social media to find topics and patterns in unstructured data. However, innovative companies will continue to build text analytics solutions to do more than just analyze social media.
• Sentiment Analysis is Supplanted by other Measures. Building on prediction #3, by the end of 2011 sentiment analysis won’t be the be all and end all of social media monitoring. Yes, it is important, but the reality is that most low cost social media monitoring vendors don’t do it well. They may tell you that they get 75-80% accuracy, but it ain’t so. In fact, it is probably more like 30-40%. After many users have gotten burned by not questioning sentiment scores, they will begin to look for other meaningful measures.
• Data in the cloud continues to expand as well as BI SaaS. Expect there to still be a lot of discussion around data in the cloud. However, business analytics vendors will continue to launch SaaS BI solutions and companies will continue to buy the solutions, especially small and mid sized companies that find the SaaS model a good alternative to some pricey enterprise solutions. Expect to see at least ten more vendors enter the market.

• On-premise becomes a new word. This last prediction is not really related to analytics (hence the 5 rather than 6 predictions), but I couldn’t resist. People will continue to use the term, “on-premise”, rather than “on-premises” when referring to cloud computing even though it is incorrect. This will continue to drive many people crazy since premise means “a proposition supporting or helping to support a conclusion” (dictionary.com) rather than a singular form of premises. Those of us in the know will finally give up correcting everyone else.

Who is using advanced analytics?

Advanced analytics is currently a hot topic among businesses, but who is actually using it and why? What are the challenges and benefits to those companies that are using advanced analytics? And, what is keeping some companies from exploring this technology?

Hurwitz & Associates would like your help in answering a short (5 min) survey on advanced analytics. We are interested in understanding what your company’s plans are for advanced analytics. If you’re not planning to use advanced analytics, we’d like to know why. If you’re already using advanced analytics we’d like to understand your experience.

If you participate in this survey we would be happy to send you a report of our findings. Simply provide us your email address at the end of the survey! Thanks!

Here is the link to the survey:
Click here to take survey

Five Predictions for Advanced Analytics in 2010

With 2010 now upon us, I wanted to take the opportunity to talk about five advanced analytics technology trends that will take flight this year.  Some of these are up in the clouds, some down to earth.

• Text Analytics:  Analyzing unstructured text will continue to be a hot area for companies. Vendors in this space have weathered the economic crisis well and the technology is positioned to do even better once a recovery begins.  Social media analysis really took off in 2009 and a number of text analytics vendors, such as Attensity and Clarabridge, have already partnered with online providers to offer this service. Those that haven’t will do so this year.  Additionally, numerous “listening post” services, dealing with brand image and voice of the customer have also sprung up. However, while voice of the customer has been a hot area and will continue to be, I think other application areas such as competitive intelligence will also gain momentum.  There is a lot of data out on the Internet that can be used to gain insight about markets, trends, and competitors.
• Predictive Analytics Model Building:  In 2009, there was a lot of buzz about predictive analytics.  For example, IBM bought SPSS and other vendors, such as SAS and Megaputer, also beefed up offerings.  A newish development that will continue to gain steam is predictive analytics in the cloud.  For example, vendors Aha! software and Clario are providing predictive capabilities to users in a cloud-based model.  While different in approach they both speak to the trend that predictive analytics will be hot in 2010.
• Operationalizing Predictive Analytics:  While not every company can or may want to build a predictive model, there are certainly a lot of uses for operationalizing predictive models as part of a business process.  Forward looking companies are already using this as part of the call center process, in fraud analysis, and churn analysis, to name a few use cases.  The momentum will continue to build making advanced analytics more pervasive.
• Advanced Analytics in the Cloud:  speaking of putting predictive models in the cloud, business analytics in general will continue to move to the cloud for mid market companies and others that deem it valuable.  Companies such as QlikTech introduced a cloud-based service in 2009.  There are also a number of pure play SaaS vendors out there, like GoodData and others that provide cloud-based services in this space.  Expect to hear more about this in 2010.
• Analyzing complex data streams.  A number of forward-looking companies with large amounts of real-time data (such as RFID or financial data) are already investing in analyzing these data streams.   Some are using the on-demand capacity of cloud based model to do this.  Expect this trend to continue in 2010.

Operationalizing Predictive Analytics

There has been a lot of excitement in the market recently around business analytics in general and specifically around predictive analytics. The promise of moving away from the typical rear view mirror approach to a predictive, anticipatory approach is a very compelling value proposition. 

But, just how can this be done?  Predictive models are complex.  So, how can companies use them to their best advantage?  A number of ideas have emerged to make this happen including 1) making the models easier to build in the first place and 2) operationalizing models that have been built so users across the organization can utilize the output of these models in various ways.  I have written several blogs on the topic.

Given the market momentum around predictive analytics, I was interested to speak to members of the Aha! Team about their spin on this subject, which they term “Business Embedded Analytics.” For those of you not familiar with Aha! the company was formed in 2006 to provide a services platform (i.e. SaaS platform called Axel ) to embed analytics within a business.  The company currently has customers in healthcare, telecommunications, and travel and transportation.  The idea behind the platform is to allow business analysts to utilize advanced business analytics in their day to day jobs by implementing a range of deterministic and stochastic predictive models and then tracking, trending, forecasting and monitoring business outcomes based on the output of the model.

An example

Here’s an example.  Say, you work at an insurance company and you are concerned about customers not renewing their policies.  Your company might have a lot of data about both past and present customers including demographic data, the type of policy they have, how long they’ve had it, and so on.  This kind of data can be used to create a predictive model of customers who are likely to drop their policy based on the characteristics of customers who have already done so.  The Aha! platform allows a company to collect the data necessary to run the model, implement the model, get the results from the model and continue to update it and track it as more data becomes available.   This, by itself, is not a new idea.  What is interesting about the Axel Services Platform is that the output from the model is displayed as a series of dynamic Key Performance Indicators (KPIs) models that the business analyst has created.  These KPIs are really important metrics, such as current membership, policy terminations, % disenrolled, and so on.   The idea is that once the model is chugging away, and getting more data, it can produce these indicators on an ongoing basis and analysts can use this information to actively understand and act on what is happening to their customer base.  The platform enables analysts to visualize these KPIs, trend them, forecast on them, and change the value of one of the KPIs in order to see the impact that might have on the overall business.   Here is a screen shot of the system:

In this instance, these are actual not forecasted values of the KPIs (although this could represent a modeled goal).  For example, the KPI on the lower right hand corner of the screen is called Internal Agent Member Retention.  This is actually a drill down of information from the Distribution Channel Performance.  The KPI might represent the number of policies renewed on a particular reference date, year to date, etc. If it was a modeled KPI, it might represent the target value for that particular KPI (i.e. in order to make a goal of selling 500,000 policies in a particular time period, an internal agent must sell, say 450 of them).  This goal might change based on seasonality, risk, time periods, and so on.

Aha! provides tools for collaboration among analysts and a dashboard, so that this information can be shared with members across the organization or across companies. Aha! Provides a series a predictive models, but also enables companies to pull in the models from outside sources such as SAS or SPSS. The service is currently targeted for enterprise class companies.

So what?

What does this mean?  Simply this:  that the model, once created, is not static.  Rather, its results are part of the business analyst’s day to day job.  In this way, companies can develop a strategy (for example around acquisition or retention), create a model to address it, and then continually monitor and analyze and act on what is happening to its customer base. 

When most analytics vendors talk about operationalizing predictive analytics, they generally mean putting a model in a process (say for a call center) that can be used by call center agents to tell them what they should be offering customers.  Call center agents can provide information back into the model, but I haven’t seen a solution where the model represents the business process in quite this way and continuously monitors the process.   This can be a tremendous help in the acquisition and retention efforts of a company. I see these kinds of models and process being very useful in industries that have a lot of small customers who aren’t that “sticky” meaning they have the potential to churn.  In this case, it is not enough to run a model once; it really needs to be part of the business process. In fact, the outcome analytics of the business user is the necessary feed back to calibrate and tune the predictive model (i.e. you might build a model, but it isn’t really the right model).  As offers, promotions, etc. are provided to these customers, the results can understood in a dynamic way, in a sense to get out ahead of your customer base 

Is it Possible to Make Predictive Analytics Pervasive?

I just got back from the IBM Information on Demand (IOD) conference in Las Vegas.  A key message was that the future is in analytics and predictive analytics at that.  IBM has already invested $12B ($8B acquisitions, $4B organic growth) in analytics since 2005.  Its recent purchase of SPSS has enabled the company to put a stake in the ground regarding leading the analytics charge.

Predictive analytics uses historical data to try to predict what might happen in the future.  There are different technologies that can help you to do this including data mining and statistical modeling.  For example, a wireless telecommunications company might try to predict churn by analyzing the historical data associated with customers who disconnected the service vs. those that did not.  Attributes that might serve as predictors include dropped calls, calling volume (in network, out of network), demographic information, and so on.  An insurance company might try to predict future fraud using past claims that where the outcome is known.  Adam Gartenberg’s blog describes more examples of this.  IBM plans to make predictive analytics more pervasive in several ways. 

• Making models easier to build. It will make predictive modeling tools easier to use for those who build the models.  A good example of this is the SPSS PASW Modeler product that uses a visual paradigm to build various kinds of models.  I stopped by the SPSS booth at the show and saw the software at the demo area and it is nice with lots of feature/functionality built into it.  Training is available (and I would argue necessary), for example, to understand when you might want to use a certain kind of model. 
• Embedding the predictive model in a process.  Here, the predictive model would become part of a business process. For example, a predictive model might be built into a claims analysis process.  The model determines the characteristics and predictors of claims that might be classified as fraudulent.  As the claims come through the process, those that are suspicious, based on the model, would get kicked out for further examination.  

So, given these two approaches, can predictive analytics become pervasive? 

In the case of making predictive modeling tools easier to use, the question isn’t whether someone can use a tool, but whether he or she can use it correctly.   The goal of a tool like PASW is to enable business users to build advanced models. Could a BI power user who is accustomed to slicing and dicing and shaking and baking data effectively use a tool like this?  Possibly, if they have the right thought process and they pay attention to the part of the training that describes what type of technique to use for what type of problem.  It is a good goal.  Time will be the judge.

As for embedding predictive analytics in business processes; this is already starting to happen and here is where the possibility of making prediction more pervasive gets exciting.  For example, telecommunications companies can embed predictive analytics into a call center application to understand an action that a customer might take.  A call center representative can make use of the results of the model (without understanding the model or what it does).  He or she is simply fed information, from the model, (in real time) to help service a customer most effectively.   The model can be created by a skilled analytics person, but deployed in such a way that it can help a lot of other people across an organization.  One key will be the ability to integrate a model into the actual code and culture behind a business process.

Look, I don’t have a crystal ball (little predictive modeling humor there), but I am very excited about the possibilities of predictive modeling.  I did this kind of modeling for years at Bell Laboratories, way back when, and it is great to see it finally gaining traction in the marketplace.  Predictive analytics can be a truly powerful weapon in the right hands.

Four reasons why the time is right for IBM to tackle Advanced Analytics

IBM has dominated a good deal of the news in the business analytics world, recently. On Friday, it completed the purchase of SPSS and solidified its position in predictive analytics.  This is certainly the biggest leg of a recent three-prong attack on the analytics market that also includes:

• Purchasing Red Pill.  Red Pill is a privately-held company headquartered in Singapore that provides advanced customer analytics services -  especially in the business process outsourcing arena.  The company has talent in the area of advanced data modeling and simulation for various verticals such as financial services and telecommunications. 
• Opening a series of solutions centers focused on advanced analytics.  There are currently four centers operating now: in New York (announced last week), Berlin, Beijing, and Tokyo.  Others are planned for Washington D.C. and London. 

Of course, there is a good deal of organizational (and technology) integration that needs to be done to get all of the pieces working together (and working together) with all of the other software purchases IBM has made recently.  But what is compelling to me is the size of the effort that IBM is putting forth.  The company obviously sees an important market opportunity in the advanced analytics market.  Why?  I can think of at least four reasons:

• More Data and different kinds of data.  As the amount of data continues to expand, companies are finally realizing that they can use this data for competitive advantage, if they can analyze it properly.  This data includes traditional structured data as well as data from sensors and other instruments that pump out a lot of data, and of course, all of that unstructured data that can be found both within and outside of a company.
• Computing power.  The computing power now exists to actually analyze this information.  This includes analyzing unstructured information along with utilizing complex algorithms to analyze massive amounts of structured data. And, with the advent of cloud computing, if companies are willing to put their data into the cloud, the compute power increases.
• The power of analytics.  Sure, not everyone at every company understands what a predictive model is, much less how to build one.  However, a critical mass of companies have come to realize the power that advanced analytics, such as predictive analysis can provide.  For example, insurance companies are predicting fraud, telecommunications companies are predicting churn.  When a company utilizes a new technique with success, it is often more willing to try other new analytical techniques. 
• The analysis can be operationalized.  Predictive models have been around for decades.  The difference is that 1) the compute power exists and 2) the results of the models can be utilized in operations.  I remember developing models to predict churn many years ago, but the problem was that it was difficult to actually put these models in to operation.  This is changing.  For example, companies are using advanced analytics in call centers.  When a customer calls, an agent knows if that customer might be likely to disconnect a service.  The agent can utilize this information, along with recommendations for new service to try to retain the customer. 

 So, as someone who is passionate about data analysis, it is good to see that it is finally gaining the traction it deserves.

IBM Business Analytics and Optimization – The Dawn of New Era

I attended the IBM Business Analytics and Optimization (BAO) briefing yesterday at the IBM Research facility in Hawthorne, NY.   At the meeting, IBM executives from Software, Global Business Services, and Research (yes, Research) announced its new consulting organization, which will be led by Fred Balboni.   The initiative includes 4000 GBS consultants working together with the Software Group and Research to deliver solutions to customers dedicated to advanced business analytics and business optimization. The initiative builds off of IBM’s Smarter Planet . 

 

IBM believes that there is a great opportunity for companies that can take all of the information they are being inundated with and use it effectively.  According to IBM (based on a recent study), only 13% of companies are utilizing analytics to their advantage.  The business drivers behind the new practice include the fact that companies are being pressured to make decisions smarter and faster.  Optimization is key as well as the ability for organizations to become more predictive.  In fact, the word predictive was used a lot yesterday. 

 

According to IBM, with an instrumented data explosion, powerful software will be needed to manage this information, analyze it, and act on it.  This goes beyond business intelligence and business process management, to what IBM terms business analytics and optimization.  BAO operationalizes this information via advanced analytics and optimization.  This means that advanced analytics operating on lots of data will be part of solutions that are sold to customers.  BAO will go to market with industry specific applications

 

‘Been doing this for years

 

IBM was quick to point out that they have been delivering solutions like this to customers for a number of years Here are a few examples:

 

·        The Sentinel Group , an organization that provides healthcare anti-fraud and abuse services, uses IBM software and advanced analytics to predict insurance fraud.

·        The Fire Department of New York is using IBM software and advanced analytics to “ build a state of the art system for collecting and sharing data in real-time that can potentially prevent fires and protect firefighters and other first responders when a fire occurs”.

·        The Operational Risk data exchange (ORX) is using IBM to help its 35 member banks better analyze operational loss data from across the banking industry.  This work is being done in conjunction with IBM Research.

 

These solutions were built in conjunction with the members of IBM Research who have been pioneering new techniques for analyzing data.  This is a group of 200 mathematicians and other quantitative scientists.  In fact, according to IBM, IBM research has been part of a very large number of client engagements.  A few years back, the company formalized the bridge between GBS and Research via the Center for Business Optimization.  The new consulting organization is yet a further outgrowth of this. 

 

The Dawn of a New Era

 

The new organization will provide consulting services in the following areas:

·        Strategy

·        Biz Intelligence and Business Performance Management

·        Advanced Analytics and Optimization

·        Enterprise info management

·        Enterprise Content management

 

It was significant that the meeting was held at the Research Labs.  We lunched with researchers, met with Brenda Dietrich, VP of Research, and saw a number of solution demos that utilized intellectual property from Research.  IBM believes that its research strength will help to differentiate it from competitors.

 

The research organization is doing some interesting work in many areas of data analysis including mining blogs, sentiment analysis, and machine learning and predictive analysis.  While there are researchers on the team that are more traditional and measure success based on how many papers they publish, there are a large number that get excited about solving real problems for real customers.   Brenda Dietrich requires that each lab participate in real-world work. 

 

Look, I get excited about business analytics, it’s in my blood.  I agree that world of data is changing and companies that make the most effective use of information will come out ahead. I’ve been saying this for years.   I’m glad that IBM is taking the bull by the horns.  I like that Research is involved. 

 

It will be interesting to see how effectively IBM can take its IP and reuse it and make it scale across different customers in different industries in order to solve complex problems.  According to IBM, once a specific piece of IP is used several times, they can effectively make it work across other solutions.  On a side note, it will also be interesting to see how this IP might make its way into the Cognos Platform.  That is not the thrust of this announcement (which is more GBS centric), but is worth mentioning.