View online: tdwi.org/flashpoint
May 2, 2013
ANNOUNCEMENTS
NEW TDWI Best Practices Report:
Integrating Hadoop into Business Intelligence and Data Warehousing
CONTENTS
Enabling an Agile Information Architecture
Delivering Value through Mobile Business Intelligence
Salary by Gender, Age, and Experience
Mistake: Poor Data Preparation
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Enabling an Agile Information Architecture
William McKnight
McKnight Consulting Group
Topics:
Data Management, Data Analysis and Design, Data Warehousing
Long gone are the days when enterprise information architectures were judged by their similarity to a generalized standard. Times were simpler when:
- There was no big data of concern
- Master data meant data warehouse dimensional data
- The idea of a cross-system query was laughable
- We tried to put all data in the data warehouse permanently
- Information management meant data warehousing
- The cloud was nonexistent
- Syndicated data was not useful
- Columnar orientation was only performed by the strange Sybase IQ
- We secretly judged our success on the size of our data warehouse
- Information management was separated from operations by an impenetrable brick wall
Time has seen these views fall by the wayside. Vendors who still bring laminated architecture to the sales table are quickly deemed out of touch. Today, the possibilities for architecture are endless. There is no “one size fits all” in terms of architecture from company to company; nor is there a one-size-fits-all platform for data. It is likely that at least five data structures will store 80 percent of a company’s data, with a total of 15 or more capturing all of a company’s data.
Information architecture is built from the ground up. The key is the separation of workloads into the correct platform, tied together by architecture elements. These elements include data integration, data virtualization, and master data management, as well as the softer forces of data governance and program governance.
Today, these elements need to be in place to enable agility in the architecture. Without them, inefficiencies and suboptimal platform selection will result. The latter is especially harmful because workload performance will be harmed, and good performance translates to a project’s success.
Inefficiencies can also drag down a project’s success and, consequently, the enterprise’s success. Mastering data for an application, for example, is not really a choice. All applications need “good enough” data to pass muster. The question is where they are going to get it--another one-off grow, or via an API from an enterprise-adjudicated source?
Data virtualization helps fill in the cracks of information architecture, allowing for the one-off query for data that was placed across the enterprise. There may also be built-in data virtualization, when the performance price is acceptable, for data that is otherwise best placed in a heterogeneous structure. It’s a judgment call, which is perhaps the best touchstone for the agile information architecture.
Agility is fused into architectures when it leverages what is in place for new requirements. It may extend an existing data store or two with more information for the requirement, or it may flow data from a source into a new data store.
Take, for example, the possibilities for post-operational analytic data stores: relational row-based data warehouses, data warehouse appliances, data appliances such as HANA that also serve as operational stores, columnar databases, and Hadoop. Combinations of these include hybrid relational/columnar DBMSes, appliances that are columnar, and so on.
There are also cubes, relational marts, and others. Making correct platform decisions will comprise half the success. The other half is fitting the decision into the architecture correctly, leveraging what is there, and quite possibly receiving feeds to systems designed to feed quality data. Both platform (or re-platform) selection and architecture fit are necessary.
Any conversation about how information architecture should look, void of requirements, is an academic exercise. In the real world, the new requirement must fit into the existing architecture, reusing and extending it appropriately. No application takes the form of the sacrificial lamb, taking one for the enterprise so that the long-term architecture can be formed. Likewise, I do five-year plans to yield a “true north” for the planners to keep an eye on, but always stress how it will change as the business changes. Architectures must ultimately be agile.
Every enterprise must have, or rent, knowledge of all the relevant information management possibilities in order to fully activate data and the workloads. It must then make great selections and it must support the architecture with cross-enterprise elements. This is the essence of the agile information architectures that succeed today in support of the modern gold that is information.
William McKnight is a consultant, speaker, and author in information management. His company, McKnight Consulting Group, has attracted such Global 2000 clients as Fidelity Investments, Pfizer, and Verizon. William is a popular speaker worldwide and a prolific writer. He provides clients with action plans, architectures, strategies, complete programs, and vendor-neutral tool selection to manage information.
Delivering Value through Mobile Business Intelligence
Hugh Watson, Barbara Wixom, and Bruce Yen
Mobile BI is high on many BI directors’ agendas. Mobile BI offers portability and easy access to BI, which can potentially drive pervasive BI use throughout an organization. Much as the movement from client/server to Web-based BI was transformational, so, too, is the shift to smartphones and tablets. Although mobile BI’s adoption rate has been slower than was initially anticipated, surveys show that it is now taking hold and is widely considered to be very important or even critical to business success.
BI directors have many decisions and choices to make when planning for mobile BI. Which devices should they support? Should they provide users with devices or have them bring their own? Should they deploy Web-based or “native” applications? Which vendor should they use? How should privacy and security issues be handled?
Over the past few years, many companies have moved into mobile BI, including GUESS, a $2.69 billion global company that designs and sells contemporary clothing. In 2011, GUESS won a TDWI Best Practices Award for its GMobile initiative, which delivers BI on iPads. This article describes GUESS’s approach to mobile BI and lessons learned by GUESS and other companies that can help BI directors in their initial or ongoing mobile BI efforts.
Learn more about GUESS’s mobile implementation by downloading this article.
Read the full article and more: Download Business Intelligence Journal, Vol. 18, No. 1
Highlight of key findings from TDWI's wide variety of research
Salary by Gender, Age, and Experience
Women continue to lag markedly behind men in both salary and bonuses. For example, average wages for men rose 5.2 percent between 2010 and 2012, to $111,225, while women’s wages fell a fraction of a percent, to $97,037, over those same two years. For 2012, men earned $14,188 more than women on average, or 14.6 percent.
The outlook is not much brighter on the bonus front. Bonuses for men are 65 percent higher than those for women ($15,770 versus $9,566), and more men than women receive bonuses (67 percent versus 61 percent). As a look at data from the most recent five years illustrates, disparity in overall compensation between the genders is nothing new.
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Read the full report: Download the 2013 TDWI Salary, Roles, and Responsibilities Report
FlashPoint Rx prescribes a "Mistake to Avoid" for business intelligence and data warehousing professionals.
Mistake: Poor Data Preparation
Krish Krishnan
Big data processing requires you to prepare the data prior to and during the processing cycles and to provide additional inputs as needed for taxonomies and metadata. Failure to execute the preparation steps will skew the results of processing big data. For example, processing log files will help you understand the need for data preparation.
Weblogs or machine logs have a fixed format and field layout that can be useful in analyzing the behavior of products, machines, and human/machine interactions; the logs can also be useful in associating the behavior with enterprise analytical platforms for visualization. However, there are a few steps developed by every organization that address how the data needs to be named, enriched, associated with metadata, and parsed. These steps must be followed to ensure that data is ready for processing; the steps must be completed in the preparation stage of processing the data into the analytical systems and the operational data store. Pay special attention to the date/time format, relevance to master data or metadata, ambiguous data, and column values.
If you do not allow adequate time and appropriate processes for data preparation, you may end up with data issues in your program.
Read the full issue: Download Ten Mistakes to Avoid In Your Big Data Implementation (Q1 2013)
