Business Intelligence for Midsize Businesses

Business intelligence is one of those phrases that everyone agrees is something all organizations, regardless of their size, should be doing.  After all, no organization would admit to doing business stupidity.  Nor, I presume, would any vendor admit to selling it.

But not everyone seems to agree on what the phrase means.  Personally, I have always defined business intelligence as the data analytics performed in support of making informed business decisions (i.e., for me, business intelligence = decision support).

Oftentimes, this analytics is performed on data integrated, cleansed, and consolidated into a repository (e.g., a data warehouse).  Other times, it’s performed on a single data set (e.g., a customer information file).  Either way, business decision makers interact with the analytical results via static reports, data visualizations, dynamic dashboards, and ad hoc querying and reporting tools.

But robust business intelligence and analytics solutions used to be perceived as something only implemented by big businesses, as evinced in the big price tags usually associated with them.  However, free and open source software, cloud computing, mobile, social, and a variety of as-a-service technologies drove the consumerization of IT, driving down the costs of solutions, enabling small and midsize businesses to afford them.  Additionally, the open data movement lead to a wealth of free public data sets that can be incorporated into business intelligence and analytics solutions (examples can be found at kdnuggets.com/datasets).

Lyndsay Wise, author of the insightful book Using Open Source Platforms for Business Intelligence (to listen to a podcast about the book, click here: OSBI on OCDQ Radio), recently blogged about business intelligence for small and midsize businesses.

Wise advised that “recent market changes have shifted the market in favor of small and midsize businesses.  Before this, most were limited by requirements for large infrastructures, high-cost licensing, and limited solution availability.  With this newly added flexibility and access to lower price points, business intelligence and analytics solutions are no longer out of reach.”

This post was written as part of the IBM for Midsize Business program, which provides midsize businesses with the tools, expertise and solutions they need to become engines of a smarter planet. I’ve been compensated to contribute to this program, but the opinions expressed in this post are my own and don’t necessarily represent IBM’s positions, strategies, or opinions.

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Open Source Business Intelligence

OCDQ Radio is a vendor-neutral podcast about data quality and its related disciplines, produced and hosted by Jim Harris.

During this episode, I discuss open source business intelligence (OSBI) with Lyndsay Wise, author of the insightful new book Using Open Source Platforms for Business Intelligence: Avoid Pitfalls and Maximize ROI.

Lyndsay Wise is the President and Founder of WiseAnalytics, an independent analyst firm and consultancy specializing in business intelligence for small and mid-sized organizations.  For more than ten years, Lyndsay Wise has assisted clients in business systems analysis, software selection, and implementation of enterprise applications.

Lyndsay Wise conducts regular research studies, consults, writes articles, and speaks about how to implement a successful business intelligence approach and improving the value of business intelligence within organizations.

Related OCDQ Radio Episodes

Clicking on the link will take you to the episode’s blog post:

• Decision Management Systems — Guest James Taylor discusses data-driven decision making and analytical concepts from his book Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics.

• Data Driven — Guest Tom Redman (aka the “Data Doc”) discusses concepts from one of my favorite data quality books, which is his most recent book Data Driven: Profiting from Your Most Important Business Asset.

• Making EIM Work for Business — Guest John Ladley discusses his book Making EIM Work for Business, exploring what makes information management, not just useful, but valuable to the enterprise.

• The Data Governance Imperative — Guest Steve Sarsfield discusses his book The Data Governance Imperative, exploring the business value of data quality, the characteristics of a data champion, and creating effective data quality scorecards.

• Master Data Management in Practice — Guests Dalton Cervo and Mark Allen discuss their book MDM in Practice, demystifying the theories surrounding MDM, and recommending how to properly prepare for a new MDM program.

• Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.

• Good-Enough Data for Fast-Enough Decisions — Guest Julie Hunt discusses the intersection of data quality and business intelligence, especially good-enough data for fast-enough decisions, a necessity in the constantly changing business world.

• Big Data and Big Analytics — Special Guests Jill Dyché and Dan Soceanu, following the 2011 Pacific Northwest BI Summit, discuss big trends in business intelligence, including cloud computing, collaboration, and big data analytics.

Alternatives to Enterprise Data Quality Tools

The recent analysis by Andy Bitterer of Gartner Research (and ANALYSTerical) about the acquisition of open source data quality tool DataCleaner by the enterprise data quality vendor Human Inference, prompted the following Twitter conversation:

Since enterprise data quality tools can be cost-prohibitive, more prospective customers are exploring free and/or open source alternatives, such as the Talend Open Profiler, licensed under the open source General Public License, or non-open source, but entirely free alternatives, such as the Ataccama DQ Analyzer.  And, as Andy noted in his analysis, both of these tools offer an easy transition to the vendors’ full-fledged commercial data quality tools, offering more than just data profiling functionality.

As Henrik Liliendahl Sørensen explained, in his blog post Data Quality Tools Revealed, data profiling is the technically easiest part of data quality, which explains the tool diversity, and early adoption of free and/or open source alternatives.

And there are also other non-open source alternatives that are more affordable than enterprise data quality tools, such as Datamartist, which combines data profiling and data migration capabilities into an easy-to-use desktop application.

My point is neither to discourage the purchase of enterprise data quality tools, nor promote their alternatives—and this blog post is certainly not an endorsement—paid or otherwise—of the alternative data quality tools I have mentioned simply as examples.

My point is that many new technology innovations originate from small entrepreneurial ventures, which tend to be specialists with a narrow focus that can provide a great source of rapid innovation.  This is in contrast to the data management industry trend of innovation via acquisition and consolidation, embedding data quality technology within data management platforms, which also provide data integration and master data management (MDM) functionality as well, allowing the mega-vendors to offer end-to-end solutions and the convenience of one-vendor information technology shopping.

However, most software licenses for these enterprise data management platforms start in the six figures.  On top of the licensing, you have to add the annual maintenance fees, which are usually in the five figures.  Add to the total cost of the solution, the professional services that are needed for training and consulting for installation, configuration, application development, testing, and production implementation—and you have another six figure annual investment.

Debates about free and/or open source software usually focus on the robustness of functionality and the intellectual property of source code.  However, from my perspective, I think that the real reason more prospective customers are exploring these alternatives to enterprise data quality tools is because of the free aspect—but not because of the open source aspect.

In other words—and once again I am only using it as an example—I might download Talend Open Profiler because I wanted data profiling functionality at an affordable price—but not because I wanted the opportunity to customize its source code.

I believe the “try it before you buy it” aspect of free and/or open source software is what’s important to prospective customers.

Therefore, enterprise data quality vendors, instead of acquiring an open source tool as Human Inference did with DataCleaner, how about offering a free (with limited functionality) or trial version of your enterprise data quality tool as an alternative option?

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