Six skills for predictive analytics

Today, I participated in a webinar with Actuate on the skills needed for business analysts to perform predictive modeling.  This is a hot topic and there were hundreds of participants on the call.  In my part of the presentation, I outlined some major trends in predictive analytics (including the fact that the tools are much easier to use) as well as six different skills which I thought were important for business analysts building predictive models.  I grouped them into two buckets.  One was the skills needed to frame a problem.  The other group were the skills needed to explain/defend analysis.  These skills were:

  • Critical thinking
  • Domain expertise
  • Data sense
  • Understanding the tools
  • Some level of understanding of the techniques
  • Storytelling ability

I’m sure there are more than these six.  However, what was interesting was that we got a lot of questions from the audience around these skills –  thinking that the message of the webinar was that you don’t need to be quantitative to perform predictive analytics. We got questions about overfitting and other technical considerations in predictive analytics.  I think some people thought that we were advocating the complete dumbing down of predictive analytics and that anyone off the street could build a predictive model.

My point in the Q&A around this was as follows:  Statisticians and data scientists are a scarce resource.  I believe that there are some kinds of predictive analytics that business analysts can perform, hence freeing up the big guns for the more complex work.  I still think that business analysts should be trained in the tools and techniques so they can use them to their fullest and be able to defend their analysis.

Any thoughts?  To hear more about these skills and predictive analytics, register for the webinar to view the archived version!

 

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.

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