At SAS, big data goes hand in hand with big data analytics.  The company is focused on analyzing big data to make decisions.  SAS defines big data as follows, “When volume, velocity and variety of data exceeds an organization’s storage or compute capacity for accurate and timely decision-making.”   However, SAS also includes another attribute when discussing big data which is relevance in terms of analysis.  In other words, big data analytics is not simply about analyzing large volumes of disparate data types in real time.  It is also about helping companies to analyze relevant data.

SAS can support several different big data analytics scenarios.  It can deal with complete datasets.   It can also deal with situations where it is not technically feasible to utilize an entire big data set or where the entire set is not relevant to the analysis.  In fact, SAS supports what it terms a “stream it, store it, score it” paradigm to deal with big data relevance.   It likens this to an email spam filter that determines what emails are relevant for a person.  Only appropriate emails go to the person to be read.  Likewise, only relevant data for a particular kind of analysis might be analyzed using SAS statistical and data mining technologies.

The specific solutions that support the “stream it, store it, score it” model include:

• Data reduction of very large data volumes using stream processing.  This occurs at the data preparation stage.  SAS Information Management capabilities are leveraged to interface with various data sources that can be streamed into the platform and filtered based on analytical models built from what it terms “organizational knowledge” using products like SAS Enterprise Miner, SAS Text Miner and SAS Social Network Analytics. SAS Information Management (SAS DI Studio, DI Server, which includes DataFlux capabilities) provides the high speed filtering and data enrichment (with additional meta-data that is used to build more indices that makes the downstream analytics process more efficient).  In other words, it utilizes analytics and data management to prioritize, categorize, and normalize data while it is determining relevance.  This means that massive amounts of data does not have to be stored in an appliance or data warehouse.

• SAS High Performance Computing (HPC). SAS HPC includes a combination of grid, in-memory and in-database technologies. It is appliance ready software built on specifically configured hardware from SAS database partners.  In addition to the technology, SAS provides pre-packaged solutions that are using the in-memory architecture approach.
• SAS Business Analytics.  SAS offerings include a combination of reporting, BI, and other advanced analytics functionality (including text analytics, forecasting, operations research, model management and deployment) using some of the same tools (SAS Enterprise Miner, etc) as listed above.  SAS also includes support for mobile devices.

Of course, this same set of products can be used to handle a complete data set.

Additionally, SAS supports a Hadoop implementation to enable its customers to push data into Hadoop and be able to manage it.  SAS analytics software can be used to run against Hadoop for analysis.  The company is working to utilize SAS within Hadoop so that data does not have to be brought out to SAS software.

SAS has utilized its software to help clients solve big data problems in a number of areas including:

• Retail:  Analyzing data in real time at check-out to determine store coupons at big box stores; Markdown optimization at point of sale; Assortment planning
• Finance: Scoring transactional data in real time for credit card fraud prevention and detection; Risk modeling: e.g. moving from looking at loan risk modeling as one single model to  running multiple models against a complete data set that is segmented.
• Customer Intelligence: using social media information and social network analysis

For example, one large U.S. insurance company is scoring over 600,000 records per second on a multi node parallel set of processors.

What is a differentiator about the SAS approach is that since the company has been growing its big data capabilities through time, all of the technologies are delivered or supported based on a common framework or platform.  While newer vendors may try to down play SAS by saying that its technology has been around for thirty years, why is that a bad thing?  This has given the company time to grow its analytics arsenal and to put together a cohesive solution that is architected so that the piece parts can work together.  Some of the newer big data analytics vendors don’t have nearly the analytics capability of SAS.   Experience matters.  Enough said for now.

What does this mean?  According to the announcement:

“SAP Social Media Analytics is a cloud-based solution that is able to process more than 95 million social media posts per day. It uses an advanced natural language processing (NLP) engine to read and categorize each one of these posts according to the opinions, emotions and behaviors that the market is expressing. “

NetBase is a SaaS social media insight and analytics platform that contains one year of social media data.  This data consists of blogs, tweets, newsfeeds, and other Web content.  NetBase combines deep Natural Language Processing (NLP) analytics with a content aggregation service and a reporting capability.  The product provides analysis around likes/dislikes, emotions, reasons why, and behaviors. For example, whereas some social media services might interpret the sentence, “Listerine kills germs because it hurts” as either a negative or neutral statement, the NetBase technology uses a semantic data model to understand not only that this is a positive statement, but also the reason it is positive.

The platform is currently used by hundreds of corporate customers, and was developed in partnership with five of the top 10 consumer packaged goods companies, including Coca-Cola and Kraft.  I have used NetBase for competitive intelligence, most notably when I was putting together the Victory Index for Predictive Analytics.  The platform is quite robust and easy to use.

 The idea is that an end-user could do his or her social media analysis in the NetBase solution and then, using an API provided with the solution, export data into Business Objects to further analyze it.  Here are a few screen shots I pulled from the company’s online demo that illustrate this:

Step 1:  In this simple example, say an end-user is trying to understand the buzz around a specific product (in this case a PC product).  He or she utilizes the NetBase system to understand some of the key opinions, passions, and sentiment regarding this brand.

Step 2:  Once the end user has done some analysis, he or she can export the results of the analysis to SAP Business Objects.  The illustration below shows the kind of data that is exported.  In this case, there is information about attributes and emotions about the product.  These values also have a sentiment indicator associated with them.

This data can then be visually displayed and analyzed in SAP Business Objects.   In the example below, the insights are displayed on an IPad.

In addition to simply displaying information in SAP Business Objects, the plan moving forward is to be able to operationalize this data throughout workflows that are part of an enterprise business process.  I imagine that SAP HANA will enter the picture too at some point.

I am glad to see that SAP is partnering with NetBase on this solution.  It is a good time for SAP to incorporate social media analysis into its products.  As social media analysis becomes more mainstream, SAP customers are, no doubt, asking for a solution that can work with SAP products.  While SAP bought Inxight, a text analytics vendor, a number of years ago, it does not have the social media historical data or the SaaS business around it.  This partnership seems like a good solution in the short term.  I will be interested to learn more about how SAP will incorporate social media analysis into enterprise workflows.   Certainly NetBase will benefit from the huge SAP installed base.  I suspect that SAP customers will be intrigued by this new partnership.

Big Data and Big Data Analytics are hot topics right now. Different vendors of course, come at it from their own point of view. Here’s a look at how four vendors (Attensity, IBM, SAS, and SAP) are positioning around this space, some of their product offerings, and use cases for Big Data Analytics.

In Attensity’s world Big Data is all about high volume customer conversations. Attensity text analytics solutions can be used to analyze both internal and external data sources to better understand the customer experience. For example, it can analyze sources such as call center notes, emails, survey verbatim and other documents to understand customer behavior. With its recent acquisition of Biz360 the company can combine social media from 75 million sources and analyze this content to understand the customer experience. Since industry estimates put the structured/unstructured data ratio at 20%/80%, this kind of data needs to be addressed. While vendors with Big Data appliances have talked about integrating and analyzing unstructured data as part of the Big Data equation, most of what has been done to date has dealt primarily with structured data. This is changing, but it is good to see a text analytics vendor address this issue head on.

Attensity already has a partnership with Teradata so it can marry information extracted from its unstructured data (from internal conversations) together with structured data stored in the Teradata Warehouse. Recently, Attensity extended this partnership to Aster data, which was acquired by Teradata. Aster Data provides a platform for Big Data Analytics. The Aster MapReduce Platform is a massively parallel software solution that embeds MapReduce analytic processing with data stores for big data analytics on what the company terms “multistructured data sources and types.” Attensity can now be embedded as a runtime SQL in the Aster Data library to enable the real time analysis of social media streams. Aster Data will also act as long term archival and analytics platform for the Attensity real-time Command Center platform for social media feeds and iterative exploratory analytics. By mid 2012 the plan is for complete integration to the Attensity Analyze application.

Attensity describes several use cases for the real time analysis of social streams:

1. Voice of the Customer Command Center: the ability to semantically annotate real-time social data streams and combine that with multi-channel customer conversation data in a Command Center view that gives companies a real-time view of what customers are saying about their company, products and brands.
2. Hotspotting: the ability to analyze customer conversations to identify emerging trends. Unlike common keyword based approaches, Hotspot reports identify issues that a company might not already know about, as they emerge, by measuring the “significance” of change in probability for a data value between a historical period and the current period. Attensity then assigns a “temperature” value to mark the degree of difference between the two probabilities. Hot means significantly trending upward in the current period vs. historical. Cold means significantly trending downward in the current period vs. historical.
3. Customer service: the ability to analyze conversations to identify top complaints and issues and prioritize incoming calls, emails or social requests accordingly.

Next Up:SAS

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

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 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.

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.

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.

- 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!

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.