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What do you mean by BI?

There are many flavors to this term and it gets thrown around very casually. Like many other terms in the data warehousing arena, it helps to first get everyone to agree on what each other means.

Rob Armstrong, employee of Teradata, a sponsor of The Smart Data Collective, likes to look at BI backwards. “I like to read an acronym backwards. BI is not about business intelligence, it is about having enough intelligence regarding your business that you can make, and take, relevant, timely, and profitable actions,” states Rob.

BI is all things related to information: reporting tools, “interactive analytic” tools, data discovery, dashboards, data governance, data mining. I see the term encompassing all these areas. However, each of these purposes has some particular service level expectation and responsibility from the user community.

 
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Posted by on May 23, 2011 in Uncategorized

 

Where have all the Visionaries gone?

Gartner’s magic quadrant as it stands today still shows the traditional enterprise BI Platforms (IBM, Oracle, Microsoft, Microstrategy etc.) in the Leaders quadrant while the Data Discovery Platforms with the exception of Qliktech are in the Challengers (Tableau, Tibco) or in the Niche Players category (Jaspersoft et al).

However, that will be in a state of flux as Traditional BI vendors  add Data Discovery to their portfolio (Microsoft with PowerPivot, SAP with SAP BusinessObjects Explorer, IBM with IBM Cognos Express and Information Builders with WebFocus Visual Discovery ) as they see BI spend dollars slipping to Data Discovery vendors with their ability to model, navigate and visualize data.

Meanwhile, data discovery tool vendors (like Qliktech) are implementing capabilities to improve their enterprise readiness.

Surprisingly there is no player in the “Visionary” category. So it seems that we are stuck with these two alternate approaches (Traditional Platform and Data Discovery) and their hybrids for the near term horizon. The time is now ripe for a Google/Amazon/Facebook mash up kind of approach to BI.

Maybe IBM may have something up its sleeve with “Watson”.  However, natural query language has been around for awhile as a technology looking for a marketing campaign (just ask “Easy Ask”).

I am looking for the true visionary… are you out there? Let’s change some paradigms….

 
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Posted by on March 26, 2011 in BI Vendors

 

Delivery of Electronic Health Records via IPAD

My fellow travelers of the delivery of data and analytics within healthcare….

I can’t help shake the nagging question:  What will be the impact of iPAD / Droid on EHR technology?  Will it be a paradigm changer or a fad?  While, I don’t have the answers, I found a few articles and opinions regarding EHR’s and IPAD/I Phone Apps while casually strolling the internet Friday night…

In general, it is agreed that it will take some time to adapt, however, a few EHR vendors are already in the market with systems built from the ground up for the iPad:

  • Nimble – Released by ClearPractice in October, 2010.
  • Dr. Chrono – Founded in 2009 with their first release in 2010.

Additionally, there are well over 10,000 medical apps available in the Apple App Store. These apps range from basic ICD-9 lookup tools to more advanced apps to track patient SOAP notes. A recent article listed interesting top EHR tools:

Lightweight EHRs
iMediNotes – iMediNotes lets physicians create and track basic SOAP notes. It offers very limited templates.
Mediforms EMR – The free version of this EMR was released in early 2010 and is geared towards gynecologists. The paid version will be coming in 2011 and will be more full-featured, including templates for other specialties.
SurgiChart – Released just last week, SurgiChart allows surgeons to track and share their patient case summaries. It currently does not allow the ability to create or edit them.
Scutsheet – Scutsheet provides basic functionality for creating, editing, and tracking patient progress notes and lab test results.

Other Medical Apps
MediMobile – MediMobile is primarily a charge capture application. It also offers the ability to track patient information and PQRI requirements. It also integrates with existing billing systems. This core functionality provides a lot of the core EMR functionality and could pave the way towards a more complete EMR system.
Epocrates – One of the most popular medical apps on the App Store, Epocrates is a mobile drug information resource for physicians. It doesn’t offer ability to track patient records, but tracking drug interactions is a key component of EMRs. If they were able to build a mobile EMR, they’d be able to capture market share quickly through their large user base.
Medscape – While this app is comparable to Epocrates as a drug reference tool, the vendor WebMD is a likely iPad EMR candidate. Despite the WebMD/Emdeon split in 2006, WebMD could realize synergies with their past medical billing systems and leverage a large network of users.

 
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Posted by on March 26, 2011 in Healthcare BI

 

Gartner top data warehousing trends

Data architecture is the trend, however the traditional tiered data layer approach is still the norm… news from Gartner Report on what’s hot…

The Resurgence of Data Marts
A data mart is defined as an application-specific analytic repository of any size, normally with a specific, smaller group of users than a data warehouse. Data marts can be used to optimize the data warehouse by offloading part of the workload to the data mart, returning greater performance to the warehousing environment.

Column-Store DBMSs
Column-store DBMSs generally exhibit faster query response than traditional, row-based systems and can serve as excellent data mart platforms, and even as a main data warehouse platform. Gartner foresees several vendors changing the pricing model for the software from a more traditional per-user or per-core model to a price based on the volume of data loaded into the database.

In-Memory DBMSs
In-memory DMBS (IMDBMS) technologies exhibit extremely fast query response and data commit times and introduce a higher probability that analytics and transactional systems can share the same database. Analytic data models, master data approaches and data services within a middle tier will begin to emerge as the dominant approach, forcing more traditional row-based vendors to adapt to column approaches and in-memory simultaneously. BI solutions will emerge sooner rather than later, and these will leverage IMDBMSs with superior-performing products and will quickly become acquisition targets for megavendors.

Data Warehouse as a Service and Cloud
In 2011, data warehouse as a service comes in two “flavors” — software as a service (SaaS) and outsourced data warehouses. Data warehouse in the cloud is primarily an infrastructure design option as a data model must still be developed, an integration strategy must be deployed and BI user access must be enabled and managed. Private clouds are an emerging infrastructure design choice for some organizations in supporting their data warehouse and analytics.

Additional information is available in the Gartner report “Data Warehousing Trends for the CIO, 2011-2012.” The report is available on Gartner’s website at http://www.gartner.com/resId=1530314.

 
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Posted by on February 22, 2011 in Data Warehouse Architecture

 

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Dilbert Data

 
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Posted by on February 14, 2011 in Uncategorized

 

Can data be beautiful?

Information visualization / data presentation can be beautiful, elegant and descriptive. There is a variety of conventional ways to visualize data – tables, histograms, pie charts and bar graphs are being used every day, in every project and on every possible occasion.  However, at the top of the list is is a legendary talk of the Swedish professor Hans Rosling (Hans Rosling TED Talk), in which he explains a new way of presenting statistical data. His Trendalyzer software turns complex global trends into lively animations, making decades of data pop. Asian countries, as colorful bubbles, float across the grid — toward better national health and wealth. Animated bell curves representing national income distribution squish and flatten. In Rosling’s hands, global trends — life expectancy, child mortality, poverty rates – become clear, intuitive and even playful.

If you love time series data visualization, you owe to yourself to visit GapMinder (http://www.gapminder.org/), who is the development team for TrendAnalyzer.  This software unveils the beauty of statistical time series by converting boring numbers into enjoyable, animated and interactive graphics.  (note: In March 2007, Google acquired Trendalyzer from the Gapminder Foundation and the team of developers who formerly worked for Gapminder joined Google).

Using TrendAnalyzer you can view over 600 indicators on economic, environment, health, labor, technology and more.

GapMinder is going to be my favorite place for awhile.

 
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Posted by on February 6, 2011 in Data Visualization

 

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Data Mining vs Data Sorting

Data mining is like the novel Moby Dick.  Everybody has heard of it, but few have read it.

Vendors like to toss about the term “data mining” to get our attention.  However, when it really comes down to it, there are very few practitioners using data mining techniques to derive value.  Some are just writing queries and sorting on the data to “visually” mine the results.

Data Mining versus Data Sorting.  What is the difference?

At the basic level, both mining and sorting are simply the search for valuable information (the hidden gold).  The quest to extract useful information and patterns from data. Data sorting is the process of querying data and sorting for common characteristics – but that is as far as it goes.  Through visualization techniques, the reader of the data is left to derive their own conclusions.

Data mining is using the computational power of systems to develop pattern-discovering algorithms with minimal intervention from the user. The most important feature that separates data mining from data sorting is the ability to “predict” future behaviors using patterns found in the data.

Data mining techniques can be divided into two  main categories: Discovery techniques and Predictive techniques.  Discovery techniques are used to find patterns that preexist in the data, but with no prior knowledge that the patterns exist.  One can think of these patterns as serendipitously discovered.  The most popular techniques of discovery are: (1) clustering, (2) association and (3) sequential.

Predictive Mining is the process of using the patterns found during discovery, applying regression analysis to predict a categorical or numerical value.

 
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Posted by on February 1, 2011 in Data Analysis