While I believe the author is correct in his thinking about the business intelligence processes (i.e. focusing on diminishing the broken connections from the many islands of information), I think many organizations may erroneously believe that having better, cleaner and more comprehensive customer data will magically elevate them into a customer retention leader.

What’s even more fundamental than having the most advanced customer information tools (including business intelligence) is making customer service a core tenet of the organization.  Often times the big customer service strategy boils down to the little things:

For instance, how many times have you experienced:

• Eternal hold times when trying to get telephone help for nearly any product or service
• Entering your account number multiple times only to be asked for it again by the agent
• Never finding the answer to your not-so-frequently asked question
• Rude or less than enthusiastic treatment by the customer service representative

There are certainly technologies and techniques that can cost-effectively solve many of the so-called customer relationship problems, but the company first  needs to make satisfying the customer a priority.  In addition, companies need to decide to respect each customer’s time and then behave like they really do.

Consider Zappos who’s corporate mantra is “powered by service” who beyond just giving lip service to customer service, have made it their culture.  Incidentally, they have been richly rewarded by an $800 million acquisition by Amazon who also vows to keep them operating autonomously.

Glimmers of hope

Not is all gloom in customer service.  A recent wired magazine article talks about taking on an abundance philosophy and embracing “waste” by not being miserly about offering your resources (many, like disk storage that cost close to zero) as a way to further endear your company to your customers.   Two examples that come to mind include:

• GMAIL offering gobs of storage so I don’t have to bother deleting and archiving messages (why can’t my bank do the same thing with my transactions so I don’t have to manually update quicken if I wait longer than 90 days?)
• Call centers actually taking your number and calling you back when an agent frees up so you can be productive in the mean time (and thus respecting your time while maximizing theirs).

In conclusion, I love solving customer visibility problems with the heads-up preparation and display of customer data, but it certainly is a bummer when those sophisticated capabilities are “wasted” on a Laissez-faire customer service philosophy.  In such cases, it would be better to save the business intelligence budget and allocate more dollars for a better product, richer employee comp plans or what the heck, better office coffee.

If, your organization however, is committed to great customer service on all fronts and will take action on the measurement of those fronts, then by all means bring on the BI!

His main observation was that most of the postings lacked a requirement for strong analytics and communications backgrounds (e.g., math, statistics, quantitative methods, and verbal and writing proficiency) sighting that “the main focus of the job was the delivery of business intelligence solutions to end users to facilitate their making more informed business decisions”.

For years our industry has been focused on the infrastructure (modeling, ETL, dimensional structure, reporting, etc.) of how data is delivered and less on how a business is to use the data once it has been brought all together. Perhaps this is why SAS institute recently launched a marketing campaign to redefine their wares as “business analytics vs. business intelligence.  This of course set off a small firestorm of accusations of marketing hype followed by rebuttals.

Regardless of the marketing hype or semantics involved, I do believe that an acute understanding of how information is really used to make decisions could significantly improve the value of any business intelligence professional.

Doug Henshen’s recent article (Merrill Lynch Tech Executive Touts BI Shift to Analysis) was yet another illustration of the business world’s appetite to get relevant insight more quickly and with less dollars and fanfare than our industry has been able to deliver to date.  I think the sentiments in this and other articles could be summed up in a few bullets:

• Reduce the time (money) to analyze and rationalize source data
• Minimize time (money) to model data for analytic purposes
• Reduce time (money) to acquire and cleanse data
• Reduce learning curve for front end tools and applications
• Minimize IT involvement

If we can get over 30 million users on Twitter in less than a year (the 2009 equivalent of “put a man on the moon”), it seems like we should be able to innovate our way out of the incredible time lags of getting business intelligence and analytics in a usable and actionable form.  Eliminating these barriers will in my opinion earn the rightfully title of “next generation” when it comes to a sound business intelligence strategy.

I would enjoy hearing your ideas on how our industry can improve.  Twitter me @JeffSeifert to share your insights.

Is this good?  Well,what’s not to be excited about getting insightful and useful answers concerning meaningful and relevant business facts .  Answers that when put into action, could help significantly save customers, revenues and profits. And most importantly, getting these things with a smaller impact to the calendar and checkbook.  With the normal admonitions about making sure you understand just what will be delivered in these solutions (how broad, how deep, what’s excluded) and how reliable the data will be, SaaS BI solutions offer an exciting alternative to the relatively long and expensive custom built approaches that have historically been utilized.

Today, Claudia Imhoff and Colin White (2 well respected industry experts) published an exerpt of a soon-to-be-released research paper on SaaS business intelligence entitled “Pay as You Go: SaaS Business Intelligence and Data Management.” This entry highlights a few of the solutions and Saas vendors covered in their broader report (including Blink Logic, Host Analytics, Kognitio, PivotLink and SAP Business Objects).

For those who have visited this blog previously, you are no doubt aware that we enjoy focusing on the “business” of business intelligence.  I particularly enjoyed the articles’ summary highlighting just how many survey respondents (60%) are either already or are planning to utilize SaaS-based BI solutions.  What’s more, their respondents claimed they appreciated the faster implementation cycles and lower cost not to mention the business focus (vs.IT focus) and easier maintenance.

This has not been more aptly expressed than in this humorous blog entry (That’s not BI) by blogger “Datadoodle” recounting a story about a student who taught himself to program (by reading the manual) but couldn’t technically “read” by the school’s typical definition. When asked how he could program if he could not read, the student said “Oh, that’s not reading, reading is the junk they give me in little books. It’s irrelevant, and I get nothing for it.”

I like his take on this type of question to a business owner who may claim that they “do not have time for that business intelligence stuff”, but just want to “analyze the company’s data to help watch progress and decide what to do next” (knowing nothing of data warehousing, MDM, data modeling, etc.).

Without trying to over-simplify the often arduous process of getting credible information collected and stored, I think a cool little dose of reality could go a long way as our industry re-thinks how we deliver business analytics and decision support (wait a minute, that’s not BI).

We got to talking about the economy, the general business climate and emerging trends in various industries (especially those about to get a lift from some huge government spending).  When the topic turned to what we do for businesses (help them optimize operational and financial performance via the intelligent use of analytic information), she boldly asked “shouldn’t everyone be using analytics?”  This struck me as an excellent and revealing question.

When considering the improvement businesses stand to gain from leveraging key facts about their operations (think inventory optimization, customer profitability, intelligent marketing, etc.) it seems a shame that the adoption of business intelligence and data warehousing has largely been confined to larger organizations.  I say a shame, although not completely surprising given the time, cost and complexity that has historically been
involved in such undertakings.

What’s more, I believe that there is still somewhat of a value disconnect in the world of SMB’s (universal acronym for “small and medium sized businesses”) who may not perceive the value of information (and information technology in general) to the level of their big business counterparts.  This fact was also validated by our accountant who confirmed that many of her clients (most of them SMB’s) “have no clue” as to the value contributors and detractors in their businesses.

Part of this may stem from a lack of need for analytics. For example, consider analyzing retail product sales numbers for Target Stores (and their thousands of SKU’s) vs. the mom and pop grocer who knows all their customers by name down to the 1/2 gallon of milk they buy every Tuesday).  Also likely is that the availability and advantage of leveraging business intelligence is simply not understood (nor valued yet) at this level of the market.

Despite this current information demand/supply disconnect, I see a day very soon where the shrinking time and monetary footprint of delivering valuable, straight-forward business analytics will result in much more rapid mainstream adoption.  When this happens, we may actually see a new era where indeed everyone uses analytics (and wouldn’t have it any other way).

Accurate or not, businesses do not enjoy investing in inventory that will not ultimately effect revenue and profit.  While businesses (especially retail and distribution) should attempt to run as lean as possible, over-correction could cost them vital revenue, profit and cash flow.

Here’s where even basic analytics and a business intelligence strategy (historical demand trends, idle inventory analysis, etc.) can pay the large dividends of having the right items on hand while maintaining a lean working capital footprint.

While the tools and technologies of the trade may be cheaper to acquire (think open source products and entry-level offerings), a comprehensive information delivery solution still requires intelligent knowledge workers who can analyze, model, build and deploy an application that will satisfy the unique needs of the business.  These skill sets do not come cheaply.

More is needed in the way of pre-built assets and applications that can be acquired and customized rapidly and cost-effectively.

Ab Initio is a unique and powerful general purpose computing platform that is most commonly applied to complex and large volume data integration projects typically associated with data warehousing and large-scale analytic data stores.  While its tempting to think of Ab Initio as just another technology that any engineer worth his or her salt should be able to just “pick up”, consider the extensive background requirements that have been associated with the timely and successful implementations of this powerful platform.

While there are numerous factors to consider, below are several guidelines that will help direct you in your search for an Ab Initio Consulting partner.

Experience in complex situations: Ab initio’s impressive graphical development environment (GDE) makes it very easy to get started and begin using, but due to the complex environments in which it is typically found, it is difficult to master and deploy in an optimal manner (thus necessitating expensive re-design work later if not done well).  Most ETL development is pattern-oriented and fairly routine.  To ensure you are working with an expert, seek a partner who has demonstrated experience in doing something out of the ordinary.  This could be any innovative application of the technology (data mining, conversion utillities, parsing engines, etc.) that sets them apart from the “let’s just bill hours” crowd.  Some keys to more advanced skills include Meta-programming or molding application meta data at run time (a particular strength of Ab Initio), re-usability and user-defined components (to name a few).

Mastery of Ab Initio’s Standard Environment: Ab Initio’s standard environment establishes common methods and deployment practices as well standard naming conventions to ensure a uniform delivery approach and ease of maintenance throughout an organization.  When seeking an implementation partner, ensure that they truly understand the standard environment (most developers use the standard environment, but the majority don’t truly understand it) and are prepared to enforce its developmental rigor throughout the project team.  Failure in this point could possibly lead to dramatic leaps in maintenance time and money (e.g. having to reconcile 12 different ways of implementing Ab Initio within the same organization).

EME (Enterprise Meta Environment) utilization and administration: While not all organizations license the EME, if yours does (or is planning to), ensure that you are working with an Ab Initio consulting partner who has an intimate understanding of its function, structure, configuration standards and preferably, administration.  The more comprehensive their knowledge, the more opportunity to leverage the resulting meta data (e.g. change control, where used, impact analysis, etc.) down the road.

Mastery of parallel processing, high performance computing and ETL job performance: Ab Initio is not a kitty car, and as such, you don’t want a toddler heading up your deployment.  You’ve most likely invested in Ab Initio (and the surrounding database, network and server stack) because you have critical, high-volume data requirements that mean millions of dollars to you.  Seek a partner well grounded in the deployment of parallel processing and high performance technologies and applications with the maturity to know what’s at stake. Regarding Ab Initio applications themselves, check references to know that design with performance as a key consideration (e.g. proper partitioning, number of components, etc.) is a prominent attribute. I recall several situations where a senior engineers improved Ab Initio ETL job performance 10, 20, even 100X by leveraging proper design techniques.  Think of the impact to batch windows, data availability, service level agreement compliance and user satisfaction.

Deep understanding of associated environments and technologies: Ab Initio does not run in a vacuum (or the cloud yet for that matter).  In fact, Ab Initio is typically found with a cadre of other high performance technologies (e.g. database, network, servers, etc.) that enable the highest possible processing performance.  Ensure that your Ab Initio consulting partner has relevant experience in the technologies and techniques associated with the related technologies including databases (e.g. Teradata, Oracle, DB2 UDB, Netezza, etc.), Unix/Linux (including Korn Shell Scripting).

Mastery of data warehouse and data integration fundamentals: A smart engineer once explained to me that “a fool with a tool is still a fool”.   He meant that its not enough to just understand the technology, but its application within the environmental and business context is equally if not more important.   Key considerations should include their knowledge of data architecture (logical and physical data modeling), ETL architecture (best practices for data acquisition, staging, loading and updating the data warehouse), master data management (reference data), meta data, general application development and ability to support downstream analytic uses (reporting, business intelligence, other analytics).

Excellent fundamental consulting skills and partnership style: All of the tools, techniques and talents listed above certainly provide a fundamental starting point for considering an Ab Initio consulting partner.  Equally important is the general “personality” of the firm you are considering for the project.  Are they easy to do business with? Do they have integrity (i.e. do their actions match their words)?  Are they out to help you solve your problem or just to bill hours?  How is their communication? Their commitment to your success? Do they provide you direction and a road map?  Methods? (with a Small “m” as we want to avoid methodology “dictators”), and do they provide clear documentation helping you articulate your own organization-specific methods of deployment?  Be sure to be just as comfortable with their business approach as you are with their technology prowess.

Explain to vendor management that price is not the only consideration: Yes the economy is slow and budgets are tight, but you have most probably invested millions in your high performance data warehouse environment and are no doubt anticipating reaping the maximum return on investment.  Implementing Ab Initio in a sub-optimal way, will not only hinder the benefit of speed, data availability and nimble business decisions, but could literally add millions more to your investment in human costs (re-work and maintenance) and hardware (e.g. servers, processors, memory, etc.).  While tempting to seek body-shop and staff augmentation firm pricing, consider data warehouse and BI specialty firms who while are bit pricier, can help ensure an optimal and sustainable information delivery capability.

Selecting the right Ab Initio consulting partner can help you avoid mountains of re-work and ensure that your maintenance burden is as light, nimble, flexible and inexpensive as possible. More importantly, proper design and deployment of this powerful technology will help prepare your organization to leverage timely analytics to optimize its key business operations.

Best wishes in your partner selection endeavors.

… Coming soon:  A similar analysis for IBM’s DataStage (InfoSphere).

With signs of trouble continuing to loom on the horizon, it is no doubt tempting to pull the traditional “levers” of business adjustment. Consider the following possible actions a retail operation could take and the associated upside and downside effects:

What if we could improve sales and labor performance without having to revert to the draconian measures mentioned above? Well, thanks to more capable and affordable business intelligence services, it is possible to leverage powerful analytics that when properly derived from your company’s data, can highlight problem areas that lead to near-term operational improvement.

This approach has been utilized by numerous retailers to:

Improve sales performance through benchmarking by …

* Determining the best sales people and getting the worst to sell more
* Discovering what successful stores are selling and how to get others to do the same
* Understanding how profitable sales are, and how to sell more of what makes money
* Uncovering the key sales issues in underperforming stores, and determining how to bring them up to plan

Improve labor performance through analysis and control by …

* Determining how labor (as a percent of sales) compares across stores and against industry benchmarks
* Discovering how to sell more with less labor expense
* Understanding whether customers are being served well
* Determining wheter the right people are available at the right time
* Finding out how much money is wasted on overtime
* Understand how many customers walk due to insufficient staff

With the above representing just a small sampling of the potential discoveries made possible by leveraging analytics, many areas of optimization can be made visible by this proven (and now more accessible) area of business information science.