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.

EMC and Big Data- Observations from EMC World 2011

I attended EMC’s User Conference last week in Las Vegas. The theme of the event was Big Data meets the Cloud. So, what’s going on with Big Data and EMC? Does this new strategy make sense?

EMC acquired Greenplum in 2010. At the time EMC described Greenplum as a “shared nothing, massively parallel processing (MPP) data warehousing system.” In other words, it could handle pedabytes of data. While the term data warehouse denotes a fairly static data store, at the user conference, EMC executives characterized big data as a high volume of disparate data, which is structured and unstructured, it is growing fast, and it may be processed in real time. Big data is becoming increasingly important to the enterprise not just because of the need to store this data but also because of the need to analyze it. Greenplum has some of its own analytical capabilities but recently the company formed a partnership with SAS to provide more oomph to its analytical arsenal. At the conference, EMC also announced that it has now included Hadoop as part of its Greenplum infrastructure to handle unstructured information.

Given EMC’s strength in data storage and content management, it is logical for EMC to move into the big data arena. However, I am left with some unanswered questions. These include questions related to how EMC will make storage, content management, data management, and data analysis all fit together.

• Data Management. How will data management issues be handled (i.e. quality, loading, etc.)? EMC has a partnership with Informatica and SAS has data management capabilities, but how will all of these components work together?
• Analytics. What analytics solutions will emerge from the partnership with SAS? This is important since EMC is not necessarily known for analytics. SAS is a leader in analytics and can make a great partner for EMC. But, its partnership with EMC is not exclusive. Additionally, EMC made a point of the fact that 90% most enterprises’ data is unstructured. EMC has incorporated Hadoop into Greenplum, ostensibly to deal with unstructured data. EMC executives mentioned that the open source community has even begun developing analytics around Hadoop. EMC Documentum also has some text analytics capabilities as part of Center Stage. SAS also has text analytics capabilities. How will all of these different components converge into a plan?
• Storage and content management. How do the storage and content management parts of the business fit into the big data roadmap? It was not clear from the discussions at the meeting how EMC plans to integrate its storage platforms into an overall big data analysis strategy. In the short term we may not see a cohesive strategy emerge.

EMC is taking on the right issues by focusing on customers’ needs to manage big data. However, it is a complicated area and I don’t expect EMC to have all of the answers today. The market is still nascent. Rather, it seems to me that EMC is putting its stake in the ground around big data. This will be an important stake for the future.

What is Networked Content and Why Should We Care?

This is the first in a series of blogs about text analytics and content management. This one uses an interview format.

I recently had an interesting conversation with Daniel Mayer, from TEMIS regarding his new paper, the Networked Content Manifesto. I just finished reading it and found it to be insightful in terms of what he had to say about how enriched content might be used today and into the future.

So what is networked content? According to the Manifesto, networked content, “creates a network of semantic links between documents that enable new forms of navigation and improves retrieval from a collection of documents. “ It uses text analytics techniques to extract semantic metadata from documents. This metadata can be used to link documents together across the organization, thus providing a rich source of connected content for use by an entire company. Picture 50 thousand documents linked together across a company by enriched metadata that includes people, places, things, facts, or concepts and you can start to visualize what this might look like.

Here is an excerpt of my conversation with Daniel:

FH: So, what is the value of networked content?

DM: Semantic metadata creates a richer index than was previously possible using techniques such as manual tagging. There are five benefits that semantic metadata provides. The first two benefits are that it makes content more findable and easier to explore. You can’t find what you don’t know how to query. In many cases people don’t know how they should be searching. Any advanced search engine with facets is a simple example of how you can leverage metadata to enhance information access by enabling exploration. The third benefit is that networked content can boost insight into a subject of interest by revealing context and placing it into perspective. Context is revealed by showing what else there is around your precise search – for example related documents. Perspective is typically reached through analytics. That is, attaining a high level of insight into what can be found in a large amount of documents, like articles or call center notes. The final two benefits are more future looking. The first of these benefits is something we call “proactive delivery”. Up to now, people mostly access information by using search engines to return documents associated with a certain topic. For example, I might ask, “What are all of the restaurants in Boston?” But by leveraging information about your past behavior, your location, or your profile, I can proactively send you alerts about relevant restaurants you might be interested in. This is done by some advanced portals today, and the same principle can be applied to virtually any forms of content. The last benefit is tight integration with workflow applications. Today, people are used to searching Google or other search engines which require a dedicated interface. If you are writing a report and need to go to the web to look for more information, this interferes with your workflow. But instead, it is possible to pipe content directly to your workflow so that you don’t need to interrupt your work to access it. For example, we can foresee how in the near future, when typing a report in a word processing application such as MS Word, , right in the interface, you will be able to receive bits of information related contextually to what you are typing. As a chemist, , you might receive suggestions of scientific articles based on the metadata extracted from the text you are typing. Likewise, Content management interfaces in the future will be enriched with widgets that provide related documents and analytics.

FH: How is networked content different from other kinds of advanced classification systems provided by content management vendors today?

DM: Networked Content is ultimately a vision for how content can be better managed and distributed by leveraging semantic content enrichment. This vision is underpinned by an entire technical ecosystem, of which the Content Management System is only one element. Our White Paper illustrates how text analytics engines such as the Luxid® Content Enrichment Platform are a key part of this emerging ecosystem.

Making a blanket comparison is difficult, but generally speaking, Networked Content can leverage a level of granularity and domain specificity that the classification systems you are referring to don’t generally support.

FH: Do you need a taxonomy or ontology to make this work?

DM: I’d like to make sure we use caution when we use these terms. A taxonomy or ontology can be helpful, certainly. If a customer wants to improve navigation in content and already has an enterprise taxonomy, it will undoubtedly help by providing guidance and structure. However, in most cases it is not sufficient in and of itself to perform content enrichment. To do this you need to build an actual engine that is able to process text and identify within it some characteristics that will trigger the assignment of metadata (either by extracting concepts from the text itself or by judging the text as a whole) In the news domain, for example, the standard IPTC taxonomy is used to categorize news articles into topic areas such as economy, politics, or sports, and into subcategories like economy/economic policy or economy/macroeconomics, etc… You can think of this as a file cabinet where you ultimately want to file every article. What the IPTC taxonomy does is that it tells you the structure the file cabinet should have. But it doesn’t do the filing for you. For that, you need to build the metadata extraction engine. That’s where we come in. We provide a platform that includes standard extraction engines – that we call Skill Cartridges® as well as the full development environment to customize them, extend their coverage, and develop new ones from the ground up if needed.

FH: I know that TEMIS is heavily into the publishing industry and you cite publishing examples in the Manifesto. What other use cases do you see?

DM: The Life Sciences industry (especially Pharma and Crop Science) has been an early adopter of this technology for applications such as scientific discovery, IP management, knowledge management, pharmacovigilance,. These are typical use cases for all research-intensive sectors. Another group of common use cases for this technology in the private sector is what we call Market Intelligence: understanding your competitors and complementors (Competitive Intelligence), your customers (Voice of the Customer) and/or what is being said about you (Sentiment Analysis) You can think of all of these as departmental applications in the sense that primarily serve the needs of one department: R&D, Marketing, Strategy, etc…

Furthermore, we believe there is an ongoing trend for the Enterprise to adopt Networked Content transversally, beyond departmental applications, as a basic service of its core information system. There, content enrichment can act as the glue between content management, search, and BI, and can bring productivity gains and boost insight throughout the organization. This is what has led us to deploy within EMC Documentum and Microsoft SharePoint 2010 In the future all the departmental applications will become even more ubiquitous thanks to such deployments.

FH: How does Networked Content relate to the Semantic Web?

DM: They are very much related. The Semantic Web has been primarily concerned with how information that is available on the Web should be intelligently structured to facilitate access and manipulation by machines. Networked Content is focused on corporate – or private – content and how it can be connected with other content, either private, or public.

Five vendors committed to content analytics for ECM

In 2007, Hurwitz & Associates fielded one of the first market studies on text analytics. At that time, text analytics was considered to be more of a natural extension to a business intelligence system than a content management system. However, in that study, we asked respondents who were planning to use the software, whether they were planning to deploy it in conjunction with their content management systems. It turns out that a majority of respondents (62%) intended to use text analytics software in this manner. Text analytics, of course, is the natural extension to content management and we have seen the market evolve to the point where several vendors have included text analytics as part of the their offerings to enrich content management solutions.

Over the next few months, I am going to do a deeper dive into solutions that are at the intersection of text analytics and content management; three from content management vendors EMC, IBM, and OpenText as well as solutions from text analytics vendor TEMIS and analytics vendor SAS. Each of these vendors is actively offering solutions that provide insight into content stored in enterprise content management systems. Many of the solutions described below also go beyond providing insight for content stored in enterprise content management systems to include insight over other content both internal and external to an organization. A number of solutions also integrate structured data with unstructured information.

• EMC: EMC refers to its content analytics capability as Content Intelligence Services (CIS). CIS supports entity extraction as well as categorization. It enables advanced search and discovery over a range of platforms including ECM systems such as EMC’s Documentum, Microsoft SharePoint, and others.

• IBM: IBM offers a number of products with text analytics capabilities. Its goal is to provide rapid and deep insight into unstructured data. The IBM Content Analytics solution provides integration into IBM ECM (FileNet) solutions such as IBM Case Manager, its big data solutions (Netezza) and integration technologies (DataStage). It also integrates securely with other ECM solutions such as SharePoint, Livelink, Documentum and others.

• OpenText: OpenText acquired text analytics vendor Nstein in 2010 in order to invest in semantic technology and expand its semantic coverage. Nstein semantic services are now integrated with OpenText’s ECM suite. This includes automated content categorization and classification as well as enhanced search and navigation. The company will soon be releasing additional analytics capabilities to support content discovery. Content Analytics services can also be integrated into other ECM systems.

• SAS: SAS Institute provides a number of products for unstructured information access and discovery as part of its vision for the semantically integrated enterprise. These include SAS Enterprise Content Categorization, SAS Ontology Management (both for improving document relevance) and SAS Sentiment Analysis and SAS Text Miner for knowledge discovery. The products integrate with structured information; with Microsoft SharePoint, FAST ESP, Endeca, EMC Documentum; as well as with both Teradata and Greenplum.

• TEMIS: TEMIS recently released its Networked Content Manifesto, which describes its vision of a network of semantic links connecting documents to enable new forms of navigation and retrieval from a collection of documents. It uses text analytics techniques to extract semantic metadata from documents that can then link documents together. Content Management systems form one part of this linked ecosystem. TEMIS integrates into ECM systems including EMC Documentum and Centerstage, Microsoft SharePoint 2010 and MarkLogic.

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 basic questions to ask before leaping into low cost social media monitoring

I just finished testing two low cost (<$50.00/mo) social media monitoring tools. They were both easy to use with clean user interfaces. Both had some nice features, especially around reaching back out to people making comments in social media. However, running these two services side by side brought home some issues with these kinds of offerings – specifically in the area of coverage, sentiment analysis, and analytics. Note that I am not naming names because I believe the issues I ran into are not unique to these specific tools, but to the low cost social media monitoring market, in general. Some of these issues will also apply to higher priced offerings!

What I did:

I ran an analysis using the term “advanced analytics” including the term itself as well as companies (as additional topics) in the space. I made sure to be as specific and clear as I could be, since I knew topics were keyword based. I let the services run side by side for several weeks, interacting with the tools on a regular basis.

What I noticed:

1. Topic specification. Tools will vary in how the end user can input what he or she is looking for. Some will let you try to refine your keyword search (and these are keyword based, they won’t let you discover topics per se), other won’t. Some will only allow search across all languages, others will allow the user to specify the language. Find out how you can be sure that you are getting what you are searching for. For example, does the tool allow you be very specific about words that should and should not be included (i.e. SAS is the name of an analytics company and also an airline)? Since these low cost tools often don’t provide a way to build a thesaurus, you need to be careful.
2. Completeness of Coverage. The coverage varied tremendously between the services. Nor was the coverage the same for the same day for the same company name that I was tracking (and I was pretty specific, although see #1 above). In fact it seemed to vary by at least an order of magnitude. I even compared this manually in twitter streams. When I asked, I was told by one company that if they weren’t picking up everything, it must be a bug and it should be reported (!?). The other company told me all of my content came to me in one big fire hose, because there had been a problem with it, before (!?). In both cases, there still seemed to be a problem with the completeness of content. The amount of content just didn’t add up between the two services. In fact, one company told me that since I was on a trial, I wasn’t getting all of the content – yet even with the firehose effect, the numbers didn’t make sense. Oh. And don’t forget to ask if the service can pull in message boards, and which message boards (i.e. public vs. private). For an analysis, all of these content issues might mean that completeness of buzz might be misrepresented which can lead to problems.
3. Duplicate Handling. What about the amount of buzz? I thought that part of my content counting discrepancy might be due to how the company was dealing with duplicates. So beware. Some companies count duplicates (such as retweets) as buzz and some do not. However, be sure to ask when duplicate content is considered duplicate and when it is not. One company told me that retweets are not counted in buzz, but are included in the tag cloud (!?).
4. Sentiment analysis. The reality is that most of the low cost tools are not that good at analyzing sentiment. Even though the company will tell you they are 75% accurate the reality is more like 50%. Case in point: on one offering, one job listing was rated positive and another job posting listed as negative. In looking at the two postings, it wasn’t clear why (shouldn’t a job post be neutral anyway) Note, however, that many of these tools provide a means to change the sentiment from +/-/neutral (if they don’t then don’t buy it). So, if sentiment is a big deal to you then be prepared to wade through the content and change sentiment, if need be. Also ask how the company does sentiment analysis and find out at what level it does the analysis (article, sentence, phrase)
5. Analysis. Be prepared to ask a lot of questions about how the company is doing its analysis. For example, sometimes I could not map the total buzz to other analytics numbers (was it duplicates handling or something else). Additionally, some social media monitoring tools will break down buzz by gender. How is this determined? Some companies determine gender based on a name algorithm, while others use profile information from facebook or twitter (obviously not a complete view of buzz by gender, since not all information sources are twitter and facebook like). Additionally, some of the tools will only show a percentage (a no-no in this case), while others may show the number and the percent. Ditto with geolocation information. If the data is incomplete (and isn’t representative of the whole) then there could be a problem with using it for analytical purposes.

What this means

Certainly, the lure of low cost social media platforms is strong, especially for small and medium businesses. However, I would caution people to do their homework and ask the right questions, before purchasing even a low cost product. I would also suggest testing a few products (running them side by side for the same time period, even if you have to pay for it for a month or so) to compare the tools in terms of coverage, sentiment, and analysis.

The reality is that you can end up with an analysis that is completely wrong if you don’t ask the right questions of the service provider. The amount of buzz might not be what you think it is, how your company compares to another company might be wrong based on how you specified the company name, the sentiment might be entirely wrong if you don’t check it, and the analysis may be misleading unless you understand how it was put together.