What Do You Mean By “Unconstrained Analytics”? – Part 2

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The starting place for unconstrained analytics is the ability to run any analytics, any time. Beyond that, an analytic platform should be able to support any analyst, anywhere. The truth is that most analysts spend most of their time looking for data or waiting for queries to run. What is the impact of wait time? It’s a significant hit when you consider that most of these analysts are highly paid individuals and the work that they are doing is strategic in nature and vital to the success of the company.

Wait time may be costly, but refusal is even worse. Companies are taking a bigger hit when their highly paid analysts come to them with projects that would significantly impact the bottom line of the business and they are told they can’t do it. Unconstrained analytics is all about building a platform that allows companies to support any analyst, anywhere.

The analytic-driven enterprise makes analytics available to anyone making critical decisions. That includes analytic consumers, business analysts, and analytic modelers:

Analytic consumers.  Users who use traditional reporting products to mine information, but also need transparent access to data and advanced analytic capabilities They need quick, timely responses to business questions.

Business analysts.  Users who spend the bulk of their time doing analytics for the business and need freedom to explore large sets of data They need access to large, complex data often stored in many different places.

Analytic modelers.  Users who build complex analytic models for applications like affinity, business optimization, or predictive analysis. They need access to large complex data sets and hefty computing power.

Data scientists.  Highly educated and decorated users who understand, create, and use advanced analytic functions and data mining algorithms to make discoveries beyond human capability. They need access to large complex data sets, a library of advanced functions, and massive compute power.

My next post will be on analyzing any way.

What Do You Mean By “Unconstrained Analytics”? – Part 1

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I’ve been doing work for ParAccel for the last five months. During that time, I’ve heard numerous people talk about the idea of “unconstrained analytics.” I decided to get to the bottom of what that means. Is it just a marketing term? Or is it a reality? My conclusion: Unconstrained analytics is possible based on the ParAccel Analytic Platform, but I’m not sure companies realize the extent of what they can do.

So consider this: What would happen if you could remove all constraints from your analytics program? Is it possible that you can’t answer that question, because you’ve never experienced the ability to do whatever you want to do in the world of analytics. There has always been constraints? When ParAccel talks about the idea of “unconstrained analytics,” this is what they mean. Imagine a world where you could do any analytics, anytime.

From analytic reporting to on-the-fly discovery to complex analytic processing, the ParAccel Analytic Platform does it all with amazing performance.

Simple Analytics: Workloads with simple sources, basic reporting and simple analysis usually well-defined, tied to a business process and repeated over time

Static Analytics: Predefined workloads tied to decisions being made on a regular, repeated basis and monitored at any point in time, may include complex analysis

Dynamic Analytics: Unplanned workloads with potential complexity across diverse data sets, possible need for extremely detailed data, and variety of analytic algorithms for iterative processes

Complex Analytics: Multisource workloads using multiple analytic techniques and a mix of stored, real-time, and on demand integration of data

In my next post, I’ll talk about providing analytic power to any analyst, anywhere.

Wisdom Spreads Its Wings

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Back in November I wrote about a cool new application from MicroStrategy called Wisdom. It enables you to track/monitor the news and feeds from your network in a way that is very easy to digest. Back in November the big news was the introduction of the Wisdom Network option, which enabled you to analyze and compare the Wisdom Network as a whole (in aggregate, with no personally identifiable information), as well as giving you the ability to compare your broader Facebook network versus the “averages” across all or sub-segments of the users of Wisdom.

One of the possible questions that you, the discerning reader, may have had is something along the lines of “yes, but how many people am I really comparing against on the Wisdom Network”? In other words, is the Wisdom Network really an accurate reflection of the population as a whole, or even of Facebook? The truth is that the statistical gap between the Wisdom Network and Facebook (as a whole) will continue to shrink as Wisdom grows. And the day when that gap becomes statistically insignificant may not even be very far away.

The growth of Wisdom (in my layman’s opinion) has been nothing short of amazing. The good folks at MicroStrategy keep a weekly tracker of the number of users, and the chart below shows you just how quickly Wisdom is spreading its wings…

…and here’s where I’ll go out on a limb. I don’t think that the above graph is the “hockey stick” when it comes to growth. The phenomenal growth you see above is, in my opinion, hurtling towards the moment of critical mass. That’s when Wisdom will really go viral and a lot more folks on Facebook are going to get a whole lot smarter – not only about their own network but also about the world around them.

Don’t believe me? Check out this great article from the Washington Post. To quote verbatim from the article itself, using Wisdom enables you to actually see, and quantitatively identify the answers to questions such as “So what is the personality of the group that “likes” Mitt Romney on Facebook? What do they read? What music do Ron Paul devotees listen to? What sports excite Newt Gingrich fans?”. This posting on YouTube goes into a little more detail about how you could use Wisdom to learn more about the fans of each of the major candidates in the presidential primaries.

So go forth and explore! You’ll be amazed by the amount of time you’re going to spend in MicroStrategy Wisdom’s analytic playground.

 

Analytics: The Next Computing Revolution

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Analytics present a significant opportunity for companies who want to make both strategic and tactical decisions based on the latest and broadest set of information. But volume, velocity, and variety of data (3 v’s used with a shout out to Doug Laney) along with the complexity of analytics, make this an almost insurmountable task. The only way to solve the analytic challenge is to think differently about how to meet the needs of a changing world. It takes innovation to help companies achieve a truly unconstrained analytics program.

The first major breakthrough in computing occurred when smart people turned a calculator into a computing platform that could run different applications. Instead of having everything hard wired into the machine, the compute power was used to run an operating system and soft applications. Software changed the world of computing. The hardware became the platform for unlimited applications.

The second major breakthrough in computing occurred when we realized that much of the data being computed could be broken down into different types and categorized. Instead of everything streaming through the processors unchecked, they created a piece of software that served as a platform for data, a data “base.” The database became the platform for previously unimagined business applications and business intelligence.

The third major breakthrough in computing occurred when Tim Berners Lee, and so many others, pushed the idea that we could separate the application layer from the presentation layer. He pioneered an overly simple presentation layer with graphic freedom never before experienced. That human interface became the platform for widespread access, unlimited content, and unprecedented collaboration.

The next major breakthrough in computing is the analytic revolution. Innovation dictates that by separating analytic workloads companies can analyze massive amounts of detailed data using advanced analytic functions of all kinds. The analytic platform becomes the stage for unconstrained analytics, a place where no analytic application is too difficult and the speed of creating new, intelligent applications quickens remarkably.

 

The Cost of Analytic Mediocrity

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In my previous blog on “Four Faltering Approaches to Analytics,” I talked about some of the pitfalls of inadequate technology approaches some organization settle for in the world of analytics. The quick fix tends to come at a price. Whenever technology is lacking there is a hit on the business side, as well. The cost of analytic mediocrity includes constrained analytics, missed opportunities, risky business, and unnecessary expense.

Constrained analytics.  Most organizations remain in a world of constrained analytics. Their perspective is that it costs too much and takes too long to do the things analysts really want to do. It’s nearly impossible to add new users or bring in more data. The thought of applying analytics to new territory stays buried. It’s simply not a priority because the perception is that analytics take too much effort.

Missed opportunities.  Companies are missing opportunities on two levels. First, business leaders understand what they could do with new analytic applications, they want the applications, but current technology and resource constraints keep them from doing anything about it. Second, analysts are forced to work with incomplete data; and as a result, they misread trends and miss important trends and points of intelligence altogether.

Risky business.  Landmines that could potentially be discovered with a full-on analytics program remain hidden. The world used to function on known indicators. It was a predictable place. New patterns now emerge all around us and must be detected in near real time in order to help us correct course. This requires the integration and monitoring of never before tracked indicators. Because most systems can’t handle this kind of influx of data and information in motion, the landmines won’t be detected until you step on them and hear the click. It’s too late.

Unnecessary expense.  Do the math. When you overspend on hardware to make old technology do what it was never designed to do, you will also increase spending on maintenance and support. You create the monster that whittles away at your budget until there is nothing left to spend on innovation. Sure, you’ll fund the next analytics project, but you’ll never drive towards enterprise-wide analytics.

The bottom line is simple: mediocre analytics approaches yield less than mediocre results. It’s time to consider a new approach. Stay tuned….