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101 – Data Mining and Predictive Analytics  

In today’s world mining of text, Web and media (unstructured data) plus structured data mining, the term information mining is a more appropriate label. Mining a combination of these, companies are able to make the best use of structured data, unstructured text and social media. Static and stagnant predictive models of the past don’t work well in the world we live in today. Predictive analytics should be agile to adapt and monetize on quickly changing customer behaviors in our world, which are often identified online and through social networks.

Better integration of data mining software with the source data at one end and with the information consumption software at the other end has led to improvement in the integration of predictive analytics with day-to-day business. Even though there haven’t been significant advancements in predictive algorithms, the ability to apply large data sets to models and the ability to enable better interaction with business has led to improvements in the overall outcome of the exercise.

There is a great introduction to the world of data mining and predictive analytics here.

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January 24th, 2012 at 7:31 am

In Interview – Consider CloudHosting Your Business Intelligence  

// Jaspersoft’s experience with more than 100 successful cloud BI deployments has made us realize that a partnership, best-of-breed approach to cloud BI is the best way to go. BI as a service through on-demand SaaS (News – Alert) deployments are generally singular offerings that are overstretched, offer limited flexibility, and generally need to be built from the ground-up, resulting in costly down-time and high implementation costs. One of the best practices that we’ve established from our multiple launches is that customers need to have a cloud hosting-enhanced BI solution with a lean framework. Jaspersoft’s lean architecture based on web-based open standards coupled with experts in cloud management and BI consulting results in a proven solution than can meet a myriad of business needs. ..

More from an interview with Karl Van den Bergh, vice president of product strategy at Jaspersoft.

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November 3rd, 2010 at 11:40 pm

Next Generation Healthcare Analytics  

Over at The Health Care Blog, Deb Bradley, Vice President, Client Solutions at Verisk Health in Waltham, Massachusetts writes about some examples of the next generation healthcare analytics. Most of us who do analytics engineering as apart of our day jobs would agree, healthcare is on area where analytics should grow vastly. There is a lot of data that can be intelligently massaged to answer some of the most challeging health related questions.

Medical claims, pharmacy claims, lab values, HRAs, genetic markers, biometrics – the abundance of data is having an immediate impact on how analytics shape healthcare. Next generation analytics are bringing attention to health and wellness rather than disease-specific guidelines, and generating novel approaches to value-based medicine and care management.

Traditionally, analytics, such as predictive modeling, have been used to identify individuals for chronic care management and to set rates. New predictive models, however, include financial and clinical algorithms, which allow healthcare organizations to implement advanced ways to identify, manage and measure risk across and within a population.

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April 15th, 2009 at 10:25 pm

Data Mining Moves to HR  

For most of its eight-year history, Cataphora has focused on digital sleuthing. The company hunts for statistical signs of fraud. But in the past few years, Cataphora has been dispatching its data miners into a new market: statistical studies of employee performance.

The trend, though early, is unmistakable, and it extends far beyond Redwood City. Number crunching, a staple for decades in the quantifiable domains of engineering and finance, has spread in recent years into marketing and sales. Companies can now model and optimize operations, and can calculate the return on investment on everything from corporate jets to Super Bowl ads. These successes have led to the next math project: the worker. “You have to bring the same rigor you bring to operations and finance to the analysis of people,” says Rupert Bader, director of workforce planning at Microsoft (MSFT).

Such a mission might have been laughable a decade ago. But as the role of computers in the workplace expands, employees leave digital trails detailing their behavior, their schedule, their interests, and expertise. For executives to calculate the return on investment of each worker, their human resources departments are starting to open their doors to the quants.

From Business Week, an insightful article on how value of each employee is determined by HR using Data Mining/Analytics.

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March 22nd, 2009 at 7:44 am

Who’s Hot and Who’s Not in BI, Analytics?  

A blogpost on BI Vendors by Doug Henschen on Intelligent Enterprise -

In the “Advanced Analytics” camp, IDC’s stats show that Microsoft, SPSS and SAS were the fastest-growing vendors among the top five with 20.0%, 17.8% and 15.2% sales increases, respectively (see chart at left). Visual Numerics grew 9.3% and Teradata treaded water with 3.0% growth. Mind you, SAS and SPSS are in a league of their own with $440 million and $205 million in revenue, respectively, while all the others were in the Single-A, sub-$50 million ballpark.

SAS has plenty to crow about in IDC’s stats, so it has once again purchased rights to distribute an excerpt of the report as a free download. Unfortunately, this year’s excerpt only covers the top-five vendors in each category (last year’s excerpt covered 21 BI tools vendors and 13 analytics vendors). If you’re prepared to pay $3,500, you can purchase the complete “Worldwide Business Intelligence Tools 2007 Vendor Shares” report, which details sales among the top-15 vendors in BI in 2007 — a list that adds MicroStrategy, Information Builders, Actuate, QlikTech, Panorama Software, IBM (without Cognos), and TIBCO to the companies mentioned above.

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July 9th, 2008 at 7:11 am

Breakthroughs in Analytics – Part 3  

Here’s a follow-up to the previously posted series, Breakthroughs in Analytics, from Tech News World.

In the latest update, Ned Madden talks about the various tools and vendors in the Analytics domain. Read more.

Today’s software packages are much more focused on the actual application of analytical approaches to specific types of decision problems and specific types of industries, according to Anthony Milano, GMI’s VP of professional services.

“In the old days, tools were typically very general purpose in nature,” Milano told TechNewsWorld. “Now, oftentimes, tools are much more focused on helping users solve specific types of problems in business verticals and industries.”

Vendors are even adapting their core analytical engines to specific needs by creating a packaged solution that includes a version of the engine, analytic models, processes, methodologies, add-ons and extensions that allow the product to solve a very specific need, Milano said.

“Importantly, this type of solution minimizes the amount of time required to solve the problem and makes it easier for the client to get the job done without requiring a deep topic expert,” he added. “In effect, these packaged solutions embed the expertise in the solution.”

Milano stressed the importance of analytics solutions that are provided under the rental model from an application service provider (ASP) as Software as a Service (SaaS).

“These software delivery models have proved to be very cost-effective and efficient for customers, particularly when all the costs of owning and installing your own software are understood,” he said.

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June 2nd, 2008 at 7:31 am

Improving data quality without breaking the bank  

Interesting article on Data Quality from B To B Mag-

Today, most corporations are tightening the screws on their corporate cash drawers. In attempts to conserve funds yet still reach stated revenue goals, businesses are turning to their existing customer bases to generate incremental revenues. This is a logical step as the costs to cross-sell additional products and/or services to existing customers are far less than those required to attract new customers. Obviously, this puts tremendous pressure on the accuracy of databases.

As one-to-one relationship marketing becomes more the norm, databases are expanding at exponential rates. In fact, corporate databases double in size roughly every six to nine months. These two factors alone should be sufficient stimulus for businesses to get their databases in order in a cost-effective manner. The average business database contains a staggering amount—15% to 40%—of bad data. That means roughly one in four pieces of marketing material mailed is worthless.

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May 29th, 2008 at 8:12 pm

Breakthroughs in Analytics  

CRM Buyer has a series on breakthrughs in analytics world. A good read. Part 1 and Part 2.

While BI is about quickly obtaining enterprise information, Web analytics encompasses the collection, analysis and reporting of information about user activity on company Web sites. This process of analyzing the behavior of Internet visitors involves the study of the impact of a Web site on its users.

Distinctions between the two should be disappearing fairly quickly, according to Gary Angel, president and CTO of Semphonic, a tool-independent Web and search engine marketing analytics consultancy.

“I think it’s clear that there’s an increasing merger between traditional BI and Web analytics,” wrote Angel on his SemAngel blog. “This is true both in terms of data integration and tools. That’s certainly going to accelerate, and I see no reason why, in three years, the two disciplines will be separate in any meaningful sense. In addition, I think we’ll start to see much more ‘data-driven’ analysis within Web analytics.”

In 2006, IDC estimated the size of the 2005 Web analytics market at US$318 million and projected it to more than double in the ensuing five years. JupiterResearch put the size of the Web analytics market at $463 million in 2006. Today, the market is above $500 million.

This growth has occurred because the Web has become a part of the marketing mix model and is proving its value, according to Jim Sterne, president of the Web Analytics Association (WAA).

“Today, Web analytics tools can do their magic from afar,” Sterne told TechNewsWorld. “Ten years ago, we weren’t thinking about selling Software as a Service. Today’s tools are also much more capable of capturing the growing quantity of data and segmenting visitors to ensure the best possible response to a click. Further, 21st century tools are becoming more integrated with other marketing systems like e-mail, direct mail, telemarketing and in-store sales.”

E-commerce companies often use Web analytics software to measure such concrete details as how many people visited their site, how many of those visitors were unique visitors, how they came to the site (i.e., if they followed a link to get to the site or came there directly), what keywords they searched with on the site’s search engine, how long they stayed on a given page or on the entire site, what links they clicked on and when they left the site. Web analytic software can also be used to monitor whether or not a site’s pages are working properly.

With this information, Web site administrators can determine which areas of the site are popular and which areas of the site do not get traffic. They can then use this data to streamline a site to create a better user experience.

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May 26th, 2008 at 10:18 pm

Posted in Analytics,Technology

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A browser for analytics  

Read more from the Computer World article -

Based on the open-source Gecko browser engine from Mozilla, Strata is designed for people who need to create ad hoc reports from myriad data sources. It has its own scripting language so you can write a script that, say, monitors a Web site for changes to your data. Kirix also produces packaged scripts, or extensions. For example, by September, it will offer one that encrypts data going in and out of Strata.

Currently available for Windows and Linux users (a Mac version is ready but not yet shipping), Strata sells for $249 per seat.

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May 19th, 2008 at 5:52 am

From Text Analytics to Data Warehousing  

I liked the recent article of Seth Grimes which talks about Text Analytics Accuracy. His article, today, on Intelligent Enterprise, pointed me to the IBM article on IBM® OmniFind™ Analytics Edition which talks in detail about extracting unstructured data from e-mail, Web pages, news and blog articles and building a data warehouse out of them to unlock the huge potential which was previously untapped.

In recent months/weeks, the focus on unstructured data is becoming more and more as businesses and vendors are starting to understand the power of this unstructured data and how it can text mined and used to the benefit of the exterprises. And its a good this.

A must read. Highly Recommended.

IBM OmniFind Analytics Edition

Text analytics enables you to extract more business value from unstructured data such as emails, customer relationship management (CRM) records, office documents, or any text-based data. IBM® OmniFind™ Analytics Edition provides rich text analysis capabilities and interactive visualization to enable you to find patterns and trends hidden in large quantities of unstructured information. The text analysis results from OmniFind Analytics Edition are in XML-format and can also be stored, indexed, and queried in a DB2 database. This allows you to incorporate your text analysis results into existing business applications and reporting tools by using regular SQL or SQL/XML queries. This article provides an overview of text analytics with OmniFind Analytics Edition and describes several ways of bringing its analysis results into DB2, in relational or pureXML™ format.
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OmniFind Analytics Edition provides the ability to interactively explore and mine the results of text analysis, as well as structured data that is typically associated with unstructured text. For those of you familiar with business intelligence applications, you can think of it as content-centric business intelligence, in that it aggregates the results of text analysis to detect frequencies, correlations, and trends. Typical use cases include:

Analysis of customer contact information (e-mails, chats, problem tickets, contact center notes) for insight into quality or satisfaction issues
Analysis of blogs and wikis for reputation monitoring
Analysis of internal e-mail for compliance violations or for expertise location

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May 18th, 2008 at 6:29 pm