Five Trends in Predictive Analytics

Predictive analytics, a technology that has been around for decades has gotten a lot of attention over the past few years, and for good reason.  Companies understand that looking in the rear-view mirror is not enough to remain competitive in the current economy.  Today, adoption of predictive analytics is increasing for a number of reasons including a better understanding of the value of the technology, the availability of compute power, and the expanding toolset to make it happen. In fact, in a recent TDWI survey at our Chicago World Conference earlier this month, more than 50% of the respondents said that they planned to use predictive analytics in their organization over the next three years. The techniques for predictive analytics are being used on both traditional data sets as well as on big data.

Here are five trends that I’m seeing in predictive analytics:

  • Ease of use.  Whereas in the past, statisticians used some sort of scripting language to build a predictive model, vendors are now making their software easier to use.  This includes hiding the complexity of the model building process and the data preparation process via the user interface.  This is not an entirely new trend but it is worth mentioning because it opens up predictive analytics to a wider audience such as marketing.  For example, vendors such as Pitney Bowes, Pegasystems, and KXEN provide solutions targeted to marketing professionals with ease of use as a primary feature.  The caveat here, of course, is that marketers still need the skills and judgment to make sure the software is used properly.
  • For more trends: http://tdwi.org/blogs/fern-halper/list/ferns-blog.aspx

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

Advanced Analytics and the skills needed to make it happen: Takeaways from IBM IOD

Advanced Analytics was a big topic at the IBM IOD conference last week. As part of this, predictive analytics was again an important piece of the story along with other advanced analytics capabilities IBM has developed or is in the process of developing to support optimization. These include Big Insights (for big data), analyzing data streams, content/text analytics, and of course, the latest release of Cognos.

One especially interesting topic that was discussed at the conference was the skills required to make advanced analytics a reality. I have been writing and thinking a lot this subject so I was very happy to hear IBM address it head on during the second day keynote. This keynote included a customer panel and another speaker, Dr. Atul Gawande, and both offered some excellent insights. The panel included Scott Friesen (Best Buy), Scott Futren (Guinnett County Public Schools), Srinivas Koushik (Nationwide), and Greg Christopher (Nestle). Here are some of the interrelated nuggets from the discussions:

• Ability to deliver vs. the ability to absorb. One panelist made the point that a lot of new insights are being delivered to organizations. In the future, it may become difficult for people to absorb all of this information (and this will require new skills too).
• Analysis and interpretation. People will need to know how to analyze and how to interpret the results of an analysis. As Dr. Gawande pointed out, “Having knowledge is not the same as using knowledge effectively.”
• The right information. One of the panelists mentioned that putting analytics tools in the hands of line people might be too much for them, and instead the company is focusing on giving these employees the right information.
• Leaders need to have capabilities too. If executives are accustomed to using spreadsheets and relying on their gut instincts, then they will also need to learn how to make use of analytics.
• Cultural changes. From call center agents using the results of predictive models to workers on the line seeing reports to business analysts using more sophisticated models, change is coming. This change means people will be changing the way that they work. How this change is handled will require special thought by organizations.

IBM executives also made a point of discussing the critical skills required for analytics. These included strategy development, developing user interfaces, enterprise integration, modeling, and dealing with structured and unstructured data. IBM has, of course, made a huge investment in these skills. GBS executives emphasized the 8,500 employees in its Global Business Services Business Analytics and Optimization group. Executives also pointed to the fact that the company has thousands of partners in this space and that 1 in 3 IBMers will attend analytics training. So, IBM is prepared to help companies in their journey into business analytics.

Are companies there yet? I think that it is going to take organizations time to develop some of these skills (and some they should probably outsource). Sure, analytics has been around a long time. And sure, vendors are making their products easier to use and that is going to help end users become more effective. Even if we’re just talking about a lot of business people making use of analytic software (as opposed to operationalizing it in a business process), the reality is that analytics requires a certain mindset. Additionally, unless someone understands the context of the information he or she is dealing with, it doesn’t matter how user friendly the platform is – they can still get it wrong. People using analytics will need to think critically about data, understand their data, and understand context. They will also need to know what questions to ask.

I whole-heartedly believe it is worth the investment of time and energy to make analytics happen.

Please note:

As luck would have it, I am currently fielding a study on advanced analytics! In am interested in understanding what your company’s plans are for advanced analytics. If you’re not planning to use advanced analytics, I’d like to know why. If you’re already using advanced analytics I’d like to understand your experience.

If you participate in this survey I would be happy to send you a report of our findings. Simply provide your email address at the end of the survey! Here’s the link:

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.

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.

Practical Operational BI

I recently had an interesting conversation about business intelligence and operational BI with Steve Mauss, President and COO of Knowledge Relay.  Knowledge Relay was founded in 1984 and has its roots in project management software for the aerospace/defense and government verticals.  Over the years, it has expanded its reach to support other industries such as utilities and construction as well as adding new data access and analysis capabilities.  The company leverages open source software.

 

Knowledge Relay

 

The goal of the company is to help organizations “improve operational efficiencies by reducing the time between collecting data and acting on it”.  In fact, Knowledge Relay promises that it will get your large or mid-sized company up and running on its platform in 30 days.  The company has several products that help it to do this.  One is called Data Integrator.  The other is Information Visualizer. 

 

  • Data Integrator.  Data Integrator comes with over 250 connectors out of the box to help companies connect to a myriad of data sources including CRM, ERP, databases, financial applications, and project management software. .  Data integrator also performs the Extract, Transform, and Load (ETL) part of the integration puzzle.  It uses a drag and drop interface to map source to target destinations. The company deals with data quality ty in a couple of ways.  Its ETL solution has built-in quality control as the data is migrated and transformed.  It also provides an applet that performs quality checks in the background (pre- and post-migration) and, if/when it finds a problem, it notifies (i.e. via email to computer or mobile devices) the responsible parties and asks them to fix it.  
  • Information Visualizer.  Information Visualizer combines multiple data sources into a single view.  It includes Gantt, PERT, X-Y line, pie, and text reports as well as some dashboards (which it OEMs from iDashboards).  The company also supports sharing of information via a job server that distributes PDF reports automatically via email, a web portal, or a number of print options.  I’ve included some of the visualizations, below.

knowledge-relay2

 

Knowledge Relay supports some pretty practical operational applications.  For example, an energy company might use the software to analyze regular maintenance tasks that involve the integration of project management, ERP, and MRO information.   The system will help management understand how it is performing on these tasks.  It can also be used to generate a list of items that the guy on the floor needs in order to deal with something like outage management. 

 

Operational BI

 

There has been a lot of hype over the past few years about operational BI and particularly real time operational BI.   Think embedding and automating analytics in a process so a person (or another process) can act on that information in real time. A good example of this is a call center.  In this use case, a call center representative can use the information about a customer he or she is speaking with to up-sell, cross-sell, and so on during a phone call.  How does this work?  When the call is received, the system retrieves the information on a customer and it is analyzed and passed to the call center agent who can then act on it. This sounds exciting, and it can be, although industry studies indicate that this is not yet mainstream technology. 

 

Operational BI, at its core, is really what Knowledge Relay is talking about – providing information to people to help them make decisions and take action in the context of a business process.  At Knowledge Relay, operational BI is about utilizing BI to analyze the operational aspects of a business and provide this insight, not only to executives, but also to the people on the front lines.   This analysis is embedded (and automated) into the process.  As Steve put it, “It’s not always enough to find out about an operational problem, it’s also important to identify (data quality and other) issues as soon as possible in order to avoid cascading problems throughout the system; problems that will later have to be undone just to get back to a baseline. “  There is a lot of value in this.

 

 

Follow

Get every new post delivered to your Inbox.

Join 1,190 other followers