13. May 2008 by Rick Abbott.

For the majority of data management initiatives I’ve seen that have failed to meet expectations, most of have resulted from a lack of strategic planning.  I’m not talking about big picture strategic planning akin to a McKinsey or Bain effort.  The strategy effort I’m talking about involves surveying the business to determine if, where, and how to deploy a data solution, then setting the parameters that will guide the technical implementation (assuming the decision is made to go forward).  My sense is that this step is skipped for one of two reasons:

1. Management isn’t aware of this step in the process
2. The perception of a “strategy process”, particularly within IT, is of a costly, long drawn out affair that produces zero value while killing numerous trees

Both of these issues can be overcome via education.  The most powerful message is that a data centric strategy does not need to take months nor cost hundreds of thousands of dollars.  For most small to midsized businesses, and even for business units of larger corporations, the strategic direction can be formulated with one working session between business and IT representatives, with follow ups via e-mail and conference call/web meeting.  The most important decision, go/no-go, should be obvious to all participants at the conclusion of the working session based on the opportunities identified and the readiness of both business and technical groups to take on an initiative of this magnitude.  Assuming the decision is to go forward, the project team should have the data required to formulate a business plan and roadmap, which can be refined and finalized without the need for another time consuming all-hands meeting.

For more details download my white paper (Data Integration Strategy).

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12. May 2008 by Rick Abbott.

So my wife goes to the Bethesda (Maryland) library to return some books.  The library is situated close to the downtown area, so parking is at a premium.  The library used to have parking meters in place, with payment required during library hours. 

For some reason they replaced this conventional system with a “new” system that requires patrons to do the following:

1. Park the car
2. Make note of the parking space number
3. Walk into the library, key the number into a machine which produces a ticket good for two hours (free of charge)

The process for handing out tickets is as follows:

1. The meter enforcer (I don’t think you’re allowed to say “meter maid” anymore) periodically goes to the machine and prints out a list of all parking spots that are reserved.
2. He/she then trudges out to the parking lot, walks around, and writes tickets for any cars that are not on the list

Anyone with half a brain can see the hole in this system - if you park your car and walk into the library after the meter enforcer has printed the list, you’re dead meat.  Plus the fact that anyone can park there free of charge for two hours is just insane.  I would have loved to be in the requirements sessions when they came up with this bright idea.

And no, my wife is not going to pay the fine.

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12. May 2008 by Rick Abbott.

I ended Part 1with the excerpt stating that CEOs and other senior management need to take an active role in defining the analytical and data warehouse strategy for their companies.  I thought this was an important statement in that I’ve never read a business book that has advocated that level of intimacy between the executive ranks and data management initiatives.  But it underscores the importance the authors give to analytics in driving innovation.

In the first post, I talked about the key elements of the “House of Innovation”, including the importance of flexible business processes that connect N=1 and R=G.  The key concept here is that it’s not enough for a company to define their business processes - they need to determine how their processes will change to satisfy the demands of N=1.  And the key driver to this change is real-time analytical capabilities.  According to the authors, it’s not enough to implement the current approach of collecting data periodically in a large repository to be analyzed at a later date.  Analytics need to be embedded within the business processes, providing insight to managers as they make decisions on what and how to produce, and how to align resources.

From a technical perspective, this is equivalent to business activity monitoring (BAM), which has been around for 8+ years (that I know of).  It involves putting sensors and other monitoring devices (both hardware and software) into machines and applications, and feeding that information flow directly into decision support systems used by line managers.  The authors also touch on the need to pull together (in a batch process) disparate data to form a 360 degree customer view.  This forms the framework for integrating and understanding the real-time information flow. 

The problem I have with all of this is that the authors make two large and broad assumptions - that data quality is not a problem, and integrating data into a repository is relatively easy.  One the one hand I understand their not wanting to get into a technical discussion around cleansing and integrating data.  But in the majority of large companies, this is the number 1 technical issue (I’m conveniently lumping these two issues together, even though they’re two separate steps).  So in assuming away these two issues, they’re removing a prime contributor to both effort and risk associated with implementing.

All in all, I enjoyed reading the book, and gained a new perspective on the importance of data management in driving business innovation. Although I have issues with the implementation aspects of their theories, I agree with the business side of their argument.  And of course it was nice to get some props from the business world for the importance of analytics in driving business innovation.

Bottom line - this is a must read book for anyone in the data management arena (e.g., BI, DW, data integration).

Posted in Technical Focus, Business Focus | 1 Comment »

8. May 2008 by Rick Abbott.

I just finished reading the book, and wanted to provide my thoughts in general (in this post) and on the analytics theme that runs throughout the book (in a subsequent posting).  The authors are C.K. Prahalad and M.S. Krishnan, both professors at the Ross School of Business, University of Michigan.

The authors present the “House of Innovation” as the framework to support their views on innovation.  The “house” has the following components:

1. The foundation is the “technical architecture of the firm”
2. The two pillars, “Personalize co-created experiences” (N=1) and “Global access to resouces and talent” (R=G)
   • N=1 states that companies should be co-creating products and services with their customers that reflect the individual tastes of each customer.
   • R=G states that no company has all the in-house resources to provide N=1. Therefore companies need to gain access to a diverse set of resources that can be assembled quickly to meet variable work loads.
3. The roof is the “Social architecture of the firm”
4. The glue that holds everything together, and the real focus of the book, is the “flexible and resillient business processes and focused analytics”

First we’ll discuss the two pillars, then touch on the business processes.  The analytics piece I’ll leave for another post.

The concept of N=1 as presented in this book is not the same as mass customization (e.g., Dell), but refers to true co-creation of a unique product or service in conjunction with each customer.  The authors give several examples in the book, the most compelling (to me anyway) being ICICI Prudential, an Indian insurance company.  ICICI is testing an insurance product where they adjust premiums for diabetic patients on a frequent basis (every 2-4 weeks) based on test results and compliance with doctor recommendations formulated from the test results.  The benefits to the company and patients are obvious: risk is determined at an individual patent level and premiums are set accordingly.  The company gains increased visibility into the risk within their insurance portfolio, and the customers who follow the diet and medication recommendations benefit from not only reduced premiums but from (presumably) better overall health.  The authors also bring up the question “Is this an insurance or health product?”  The answer in my mind is both - moving to N=1 allows the company to leverage existing capabilities to generate new revenue streams.  However, this example highlights the potential roadblock to N=1, namely that it requires significantly more personal information.  Given the current backlash around lost and stolen personal data, this could be as much of a hurdle to N=1 as the organizational, process, and technology challenges, which are significant.

R=G is all about finding the right resources, both internal and external to the company.  The authors make it clear this is not limited to outsourcing, although this should be a piece of the puzzle.  They stress the need to leverage all forms of external resources, including traditional avenues such as outsourcing and partnerships, but also individuals with critical skill sets.  One example is a bank in India organizing groups of village women to manage micro-loans to the local inhabitants.  The key take away here, as stated by the authors, is “the focus is on access to resources, not ownership of resources”.  I think the primary impediment to implementing this model today is getting the corporate management team to come to terms with the associated loss of control.

The concept of N=1 and R=G are not ground breaking in their own right, as there are numerous examples of both in play today.  I think the most novel and potentially benefical ideas in the book are on business processes and analytics.

The most important concept presented in the book is that business processes should be fluid in order to orchestrate adjustments to both N=1 and R=G, with real-time analytics serving as the GPS.  The authors make it clear that this has nothing to do with the ’80s re-engineering that attempted to streamline business processes for efficiency.  They also stress that it’s not enough to determine what your business processes are today.  You need to put in place the capability, via social and technical architectures, to change the business processes on the fly.  Going forward, the adaptable business process (and associated analytics) will be the most important asset for a company.

I’ll leave you with this quote from the book, as a teaser for my next post on analytics:

“CEOs and line managers cannot delegate strategic decisions on the business applications, analytic capabilities, and data warehousing.  It is the business applications and the analytics engine that form the backbone of the business process architecture.” (pg 240)

Posted in Business Focus | 1 Comment »

7. May 2008 by Rick Abbott.

I “launched” a new web site today, 360DegreeIndex, that will provide market sizing information for the business intelligence and data management vendor space.  “Launched” meaning I pressed the button in FrontPage to populate the site.  It’s not the most aesthetically pleasing site, but I’ll work on that in subsequent releases.  My initial focus is on getting the data published.

The initial version lists data for the following areas:

• “BI25″ - index and market capitalization for the largest (by adjusted market cap) publicly listed vendors
• Vendor count by corporate type (e.g., Listed, Venture Backed, Partnership)
• Venture capital sizing including the number of VC firms (currently 218) with investments in this space, the number of vendors with venture backing (currently 135), and the total estimated current amount of venture capital invested in this space (currently $1.4B)

I plan on updating this data on a monthly basis, and will include month-over-month changes to these figures to provide a view into the trends, which is really the most important part of this exercise. I’m also developing some additional metrics from the 360DegreeVendor database which I will add to the site.

Posted in Vendors | No Comments »

5. May 2008 by Rick Abbott.

I’ve listened to several appliance web casts in the past 2 weeks, and have increased my knowledge of the growing data warehouse appliance market.  One thing that is clear to me is that the landscape has changed considerably in the past 2+ years, with the addition of several vendors providing a “full stack” similar to Teradata and Netezza.  There are also a number of vendors that are providing “software appliances” (to borrow a phrase from Philip Russom, TDWI analyst), by customizing database, operating system, and integration software with either off-the-shelf hardware components (Intel, AMD) or partnering with hardware vendors such as Sun.  And while I’ve gotten bits and pieces of information on this topic from a number of sources, I haven’t seen a full side by side comparison of these products. 

So I pulled together a data warehouse appliance spreadsheet that compares vendors along several categories, including applications, software, architecture (basically SMP or MPP) and hardware.  I’ve also attempted to group the vendors, using “full stack”, “database”, and “hardware” as my initial cut.  I intend this to be a work in progress, and will provide updates as I add new vendors and refine information for existing vendors.  Once I get a complete set of data, I will provide a summary of the dataset.

Please feel free to send me an e-mail if you see incorrect or missing information, or think I should add a vendor to the mix.

Posted in Technical Focus, Vendors | No Comments »

1. May 2008 by Rick Abbott.

The data collected in my BI vendor database, 360DegreeVendor, has allowed me to create an index that measures the publicly traded business intelligence, data warehouse, and data integration vendor space.  The index captures the adjusted aggregate stock price and market cap for the top 25 BI publicly traded vendors.  I say adjusted because for each vendor, I have determined a factor that represents the percentage of business derived from BI products, services, and research, and have applied that factor to their overall stock price and market cap.

Index as of COB April 29, 2008:

BI25 Index: $301.07

BI25 Market Cap: $47.3B

The numbers by themselves don’t have much meaning, the trending will be the most telling. I will publish these numbers on a weekly basis going forward, and will provide trending reports once I’ve built up sufficient data to make it meaningful.

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1. May 2008 by Rick Abbott.

I met recently with Arman Eshraghi, CEO and Founder of LogiXML and wanted to provide an overview of their company and offering, Logi 9.  LogiXML is a primarily a privately held company (with a recent $5M investment from Updata Partners), with headquarters in McLean, VA.  They list well over 100 current customers, in 13 industry groups. 

Logi9, released in January of this year, is a Java based enterprise reporting platform.  It includes report designer, report distribution, dashboard, and OLAP capabilities, which are also available as standalone products.  The platform also includes a data source mapping and ETL feature that allows for access to a variety of disparate data sources.  Logi9 was built from the ground up on the .Net platform, and utilizes an XML based architecture.  They support standard interface protocols such as SQL, ODBC and JDBC, so they connect to most data source applications (e.g., SQL Server, Oracle, MySql).  Logi9 is server based and leverages standard web technologies such as AJAX – there is no client side code which makes software upgrades easier since you don’t have to worry about pushing code out.  The combination of the XML foundation and the server based architecture allows LogiXML to distribute upgrades and patches to the platform with little or no impact to client applications.

They offer a free of charge scaled down version of their product, available at FreeReporting.com.  This is a complete reporting tool aimed at developers and power users.  The support is self service, in that you have to diagnose your own problem via the developer network or discussion forums that LogiXML supports.

Their implementation consulting services are primarily focused around installing and integrating the Logi product in the client environment.  They offer both on-site training in McLean and on-line training, with courses in product administration and report building.

LogiXML is following a recent trend by forming partnerships with data warehouse appliance vendors Vertica and Dataupia to integrate with their products.  Their partner program includes OEM, reseller, and alliance partners such Vertica and Dataupia.

While Logi9 is relatively new (released in the past 3 months), the LogiXML product has been around for almost 6 years, which leads me to believe that the product is stable.  The one concern I have is around the server based architecture – performance on the client side may suffer if the network speed is lacking, since there is nothing stored on the client.  However, I think the trade-off in ease of upgrade is well worth the response time concern, particularly since most networks these days are sufficiently fast to handle this additional data.

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