Four ways to illustrate the value of predictive analytics

My new (and first!) TDWI Best Practices Report was published a few weeks ago. It is called Predictive Analytics for Business Advantage. In it, I use the results from an online survey together with some qualitative interviews to discuss the state of predictive analytics, where it is going, and some best practices to get there. You can find the report here. The Webinar on the topic can be found here.

There were many great questions during the Webinar and I’m sorry I didn’t get to answer them all. Interestingly, many of the questions were not about the technology; rather they were about how to convince the organization (and the senior executives) about the value in predictive analytics. This jives with what I saw in my research. For instance,”lack of understanding of predictive analytics” was cited as a key challenge for the discipline. Additionally, when we asked the question, “Where would you like to see improvements in your predictive analytics deployment?”, 70% of all respondents answered “education.” It’s not just about education regarding the technology. As one respondent said, “There is a lack of understanding of the business potential” for predictive analytics, as well.

Some of the questions from the audience during the Webinar echoed this sentiment. For instance, people asked, “How do I convince senior execs to utilize predictive analytics?” and “What’s the simple way to drive predictive analytics to senior executives?” and “How do we get key leaders to sponsor predictive analytics?”

There is really no silver bullet, but here are some ways to get started:

  • Cite research: One way is to point to studies that have been done that quantify the value. For instance, in the Best Practices Report, 45% of the respondents who were currently using predictive analytics actually measured top- or bottom-line impact or both (see Figure 7 in the report). That’s pretty impressive. There are other studies out there as well. For instance, academic studies (i.e., Brynjolffson et al., 2011) point to the relationship between using data to make decisions and improved corporate performance. Industry studies by companies such as IBM suggest the same. Vendors also publish case studies, typically by industry, that highlight the value from certain technologies. These can all be useful fodder.
  • Do a proof of concept: However, these can’t really stand alone. Many of the end users I spoke to regarding predictive analytics all pointed to doing some sort of proof of concept or proof of value project. These are generally small-scale projects with high business impact. The key is that there is a way to evaluate the impact of the project so you can show measurable results to your organization. As one respondent put it, “Limit what you do but make sure it has an impact.” Additionally, think through those metrics as you’re planning the proof of concept. Additionally, someone in the organization is also going to have to become the communicator/evangelist to get people in the organization excited rather than fearful of the technology. One person told me that he made appointments with executives to talk to them about predictive analytics and show them what it could do.
  • BI foundation: Typically, organizations that are doing predictive analytics have some sort of solid BI infrastructure in place. They can build on that.  For instance, one end user told me about how he built out trust and relationships by first establishing a solid BI foundation  and making people comfortable with that and then introducing predictive analytics. Additionally, success breeds success. I’ve seen this countless times with various “new” technologies. Once one part of the organization sees something that works, they want it too. It grows from there. 
  • Grow it by acting on it: As one survey respondent put it, “Analytics is not a magic pill if the business process is not set up.” That means in order to grow and sustain an analytics effort, you need to be able to act on the analytics. Analytics in a vacuum doesn’t get you anywhere. So, another way to show value is to make it part of a business process. That means getting a number of people in the organization involved too.

The bottom line is that it is a rare company that can introduce predictive analytics, and behold! It succeeds quickly out of the gate. Are there examples? Sure. Is it the norm? Not really. Is predictive analytics still worth doing? Absolutely!

Do you have any suggestions about how to get executives and other members of your organization to value predictive analytics? Please let me know. And please visit the tdwi site for more information on predictive analytics and to download the report

<note:  This blog posting first appeared on my tdwi blog>

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

Two Weeks and Counting to Big Data for Dummies

I am excited to announce I’m a co-author of Big Data for Dummies which will be released in mid-April 2013.  Here’s the synopsis from Wiley:

Find the right big data solution for your business or organization

Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you’ll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You’ll learn what it is, why it matters, and how to choose and implement solutions that work.

  • Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals
  • Authors are experts in information management, big data, and a variety of solutions
  • Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more
  • Provides essential information in a no-nonsense, easy-to-understand style that is empowering

 

Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization.

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

Four Findings from the Hurwitz & Associates Advanced Analytics Survey

Hurwitz & Associates conducted an online survey on advanced analytics in January 2011. Over 160 companies across a range of industries and company size participated in the survey. The goal of the survey was to understand how companies are using advanced analytics today and what their plans are for the future. Specific topics included:

- Motivation for advanced analytics
- Use cases for advanced analytics
- Kinds of users of advanced analytics
- Challenges with advanced analytics
- Benefits of the technology
- Experiences with BI and advanced analytics
- Plans for using advanced analytics

What is advanced analytics ?
Advanced analytics provides algorithms for complex analysis of either structured or unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text analytics, and other advanced data mining techniques. Among its many use cases, it can be deployed to find patterns in data, prediction, optimization, forecasting, and for complex event processing. Examples include predicting churn, identifying fraud, market basket analysis, and analyzing social media for brand management. Advanced analytics does not include database query and reporting and OLAP cubes.

Many early adopters of this technology have used predictive analytics as part of their marketing efforts. However, the diversity of use cases for predictive analytics is growing. In addition to marketing related analytics for use in areas such as market basket analysis, promotional mix, consumer behavior analysis, brand loyalty, churn analysis, companies are using the technology in new and innovative ways. For example, there are newer industry use cases emerging including reliability assessment (i.e. predicting failure in machines), situational awareness, behavior (defense), investment analysis, fraud identification (insurance, finance), predicting disabilities from claims (insurance), and finding patterns in health related data (medical)

The two charts below illustrate several key findings from the survey on how companies use advanced analytics and who within the organization is using this technology.

• Figure 1 indicates that the top uses for advanced analytics include finding patterns in data and building predictive models.

• Figure 2 illustrates that users of advanced analytics in many organizations have expanded from statisticians and other highly technical staff to include business analysts and other business users. Many vendors anticipated this shift to business users and enhanced their offerings by adding new user interfaces, for example, which suggest or dictate what model should be used, given a certain set of data.

Other highlights include:

• Survey participants have seen a huge business benefit from advanced analytics. In fact, over 40% of the respondents who had implemented advanced analytics believed it had increased their company’s top-line revenue. Only 2% of respondents stated that advanced analytics provided little or no value to their company.
• Regardless of company size, the vast majority of respondents expected the number of users of advanced analytics in their companies to increase over the next six to 12 months. In fact, over 50% of respondents currently using the technology expected the number of users to increase over this time period.

The final report will be published in March 2011. Stay tuned!

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.

What is advanced analytics?

There has been a lot of discussion recently around advanced analytics. I’d like to throw my definition into the rink. I spent many years at Bell Laboratories in the late 1980s and 1990s deploying what I would call advanced analytics. This included utilizing statistical and mathematical models to understand customer behavior, predict retention, or analyze trouble tickets. It also included new approaches for segmenting the customer base and thinking about how to analyze call streams in real time. We also tried to utilize unstructured data from call center logs to help improve the predictive power of our retention models, but the algorithms and the compute power didn’t exist at the time to do this.

Based on my own experiences as well as what I see happening in the market today as an analyst, I view advanced analytics as an umbrella term that includes a class of techniques and practices that go well beyond “slicing and dicing and shaking and baking” data for reports. I would define advanced analytics as:

“Advanced analytics provides algorithms for complex analysis of either structured or unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text analytics, and other advanced data mining techniques. Among its many use cases, it can be deployed to find patterns in data, prediction, optimization, forecasting, and for complex event processing/analysis. Examples include predicting churn, identifying fraud, market basket analysis, or understanding website behavior. Advanced analytics does not include database query and reporting and OLAP cubes. “

Of course, the examples in this definition are marketing-centric and advanced analytics obviously extends into multiple arenas. Hurwitz & Associates is going to do a deep dive into this area in the coming year. We are currently fielding a study about advanced analytics and we’ll be producingadditional reports. For those of you who are interested in completing my survey, here is the link:

Five requirements for Advanced Analytics

The other day I was looking at the analytics discussion board that I moderate on the Information Management site. I had posted a topic entitled “the value of advanced analytics.” I noticed that the number of views on this topic was at least 3 times as many as on other topics that had been posted on the forum. The second post that generated a lot of traffic was a question about a practical guide to predictive analytics.

Clearly, companies are curious and excited about advanced analytics. Advanced analytics utilizes sophisticated techniques to understand patterns and predict outcomes. It includes complex techniques such as statistical modeling, machine learning, linear programming, mathematics, and even natural language processing (on the unstructured side). While many kinds of “advanced analytics” have been around for the last 20+ years (I utilized it extensively in the 80s) and the term may simply be a way to invigorate the business analytics market, the point is that companies are finally starting to realize the value this kind of analysis can provide.

Companies want to better understand the value this technology brings and how to get started. And, while the number of users interested in advanced analytics continues to increase, the reality is that there will likely be a skills shortage in this area. Why? Because advanced analytics isn’t the same beast as what I refer to as, “slicing and dicing and shaking and baking” data to produce reports that might include information such as sales per region, revenue per customer, etc.

So what skills are needed for the business user to face the advanced analytics challenge? It’s a tough question. There is a certain thought process that goes into advanced analytics. Here are five (there are no doubt, more) skills I would say at a minimum, you should have:

1. It’s about the data. So, thoroughly understand your data. A business user needs to understand all aspects of his or her data. This includes answers to questions such as, “What is a customer?” “What does it mean if a data field is blank?” “Is there seasonality in my time series data?” It also means understanding what kind of derived variables (e.g. a ratio) you might be interested in and how you want to calculate them.
2. Garbage in, Garbage out. Appreciate data quality issues. A business user analyzing data cannot simply assume that the data (from whatever source) is absolutely fine. It might be the case, but you still need to check. Part of this ties to understanding your data, but it also means first looking at the data and asking if it make sense. And, what do you do with data that doesn’t make sense?
3. Know what questions to ask. I remember a time in graduate school when, excited by having my data and trying to analyze it, a wise professor told me not to simply throw statistical models at the data because you can. First, know what questions you are trying to answer from the data. Ask yourself if you have the right data to answer the questions. Look at the data to see what it is telling you. Then start to consider the models. Knowing what questions to ask will require business acumen.
4. Don’t skip the training step. Know how to use tools and what the tools can do for you. Again, it is simple to throw data at a model, especially if the software system suggests a certain model. However, it is important to understand what the models are good for. When does it make sense to use a decision tree? What about survival analysis? Certain tools will take your data and suggest a model. My concern is that if you don’t know what the model means, it makes it more difficult to defend your output. That is why vendors suggest training.
5. Be able to defend your output. At the end of the day, you’re the one who needs to present your analysis to your company. Make sure you know enough to defend it. Turn the analysis upside down, ask questions of it, and make sure you can articulate the output

I could go on and on but I’ll stop here. Advanced analytics tools are simply that – tools. And they will be only as good as the person utilizing them. This will require understanding the tools as well as how to think and strategize around the analysis. So my message? Utilized properly these tools can be great. Utilized incorrectly– well – it’s analogous to a do-it-yourself electrician who burns down the house.

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 

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