Mac News World has an interesitng article about Business Intelligence that is a must read. Not only is there increasing demand because ot the sheer velocity of data these days, but economic recovery and success (or failure) hang in the balance for those who either do or don’t jump on the BI bandwagon.

Here is the article:

Business intelligence is all about speed — it gives corporate decision makers the modeling tools they need to make complex and important decisions quickly. Just as important as offering a forward view, however, BI allows executives to look back and find out what happened when something went wrong.

WiseAnalytics recently conducted a survey targeted at midmarket organizations using or considering business intelligence (BI). The goal of the survey was to identify adoption rates, general awareness and satisfaction with BI in general and targeted midmarket solutions specifically.

DM Review participated in survey distribution through Web site and newsletter promotions. The information in the report represents a subset of the overall responses and is based solely on the answers provided by DM Review participants (87 in total).

Please click here to read the entire article.

If you have kept yourself informed about the Business Intelligence consolidation in the ERP space, then you are probably aware that such aquisitions can have conversation changing implications in the software world. The big ones so far are Oracle and Hyperion/Siebal, SAP & Business Objects, and IBM/Cognos. If we survey the landscape closely, it becomes clear that customers and vendors are telling us something important with these moves. Here are three ways that the conversation is changing.

1. Getting Data Out of an Organization is Just as Important as Getting Data In.

Initially, as the ERP market developed, some would argue that the paramount conversation was about standardizing on business process with a common platform. Leveraging the data that ERP could capture and offer to the business was more of an afterthought. Execution on business process was the main focus. However, with the consolidations we have seen, customers, and in turn, vendors, are telling us that leveraging that data to execute on strategy (BI) is just as important. In one sense, the coming together of ERP and BI is akin to bringing two critical sides of an important business coin together. Getting data in is as important as getting data out. Customers know it (mostly), and the industry is responding. This offers existing ERP vendors the ability to have a new and more valuable conversation with their prospects and customers.

2. Beginning with the End in Mind

It is becoming clear that companies who venture down the ERP road should really begin with the end in mind. What does that mean? What if the starting conversation revolved around a company’s primary strategic objectives across the board and how visualizing the relevant data could help them reach those goals? It makes sense to talk about ERP and executing strategy (BI) at the same time because the resulting information of streamlined business processes needs to be leveraged to drive competitive differentiation, operational efficiency, and improved profitability. At the end of the day, these are an organization’s highest concerns and Business Intelligence is the way to deliver the type of visibility that will drive performance improvements. The crux is to deliver ERP and BI in tandem to help fulfill the end vision and encourage user adoption. So maybe the best way to start an ERP conversation is with a consolidated dashboard that visualizes an organizations key metrics?

3. Easing the Implementation Burden

It is no secret that ERP prospects cringe less at the cost of software than the services. Implementation costs are high. But the conversation is changing in this area as well. Conventionally, discussions about a customer’s legacy data would happen at the system integrator level and the services scope would include a component of data cleansing work that would make the data ERP ready. Unfortunately, most customers fail to adequately understand the services cost to this. Often this is a manual process that takes much time and therefore dollars. Here is where SAP has a new and valuable advantage. The Business Objects Data Quality tools (formerly First Logic) acquired by SAP can help a customer establish a DQ process to quickly de-duplicate, cleanse, standardize, and set business rules around their critical data before it goes into SAP. This is the first step to realizing a data governance vision and it will greatly reduce the time it takes to make data ERP ready. The sad alternative, for companies who fail to acknowledge and complete the data quality task, is to spend too much on services, or put their ERP implementation at risk with bad data. Garbage in, garbage out, as the old adage goes.

Here is the first part of an excellent two part article by Maureen Clarry at the b-eye network. In it she discusses the roles and responsibilities of BI teams at the program and project level. It is true that many BI projects fail because of improper planning and this article is a helpful guide to companies wishing to embark on BI projects successfully. What is important to note (and refreshing) is that the roles Maureen outlines include those for data responsibility. This is important in that approaching BI requires careful consideration of how information will flow through a supportive architecture from source to report. Failing to consider this often leaves organizations with a short sighted perspective of BI and a hodgepodge of tools without ability to provide any form of governance. Enjoy the article!

Jill Dyche, from the b-eye network, has written a great article on implementing data governance through the challenges of organizational change. In one sense, what contributes to data governance failure is the ever existing chasm between IT and business. A key take-away for success is to not stop at the vision of data governance, but to ensure you are driving the change down to the personal level. This means that attention be given to gathering feedback and employee level input on the change in process. This is a must read for anyone serious about data governance.

Here is a list of links that you may find helpful….

Data Warehousing: Is a Consolidation Craze Ahead?

Behind the Business Objects-Oco SaaS Deal

Green BI?

The “Edge” Business Objects has in the Mid Market. Here is the press release.

How to market your BI Program

Top 10 Reasons why people are making SOA fail

The Non-Traditional Data Conundrum

How to Manage Your Data As an Asset

Michael Corcoran, VP at IBI, has written a good article about trends in the BI space that one shouldn’t ignore. The gist of it suggests that more people are demanding information and delivering it to users in a way that is simple and mobile. Have a read for yourself.

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Business intelligence (BI) technology has had a tremendous impact on decision-making activities at most companies. In the last several years, it has become one of the most commonly deployed software technologies in the business world, helping executives, managers, and professional analysts stay in touch with current activities.

From an end-user standpoint, however, BI technology is only beginning to reach its potential. In this article, I identify six ways to boost BI adoption rates and drive greater return on your BI investments.

1: Serve a Wider Audience

For years we have been hearing about how BI is moving out to the masses. Vendors are making their BI software more accessible to casual users, and customers are creating BI applications that reach a broad spectrum of the user population. The more consumers of information you have, the greater the value you will obtain from your BI efforts.

To truly deliver BI to everyone in your organization, you have to be able to provide users with a high degree of sophistication via a very simple interface that requires no training. When it comes to reporting, developers should create universal, parameter-driven reports that offer thousands of potential outputs — without requiring users to understand how to use a BI “tool” at all. Successful BI practitioners follow this “guided ad hoc” approach to make business intelligence easy to digest, guiding users to the answers they need. Also, each menu or form should include a subscription or scheduling option, so users can request regular updates via e-mail.

Once the right information is easily available, everybody in your organization becomes a potential decision maker, whether they work in customer service, shipping, manufacturing, finance, or just about any other area. Whether you call it “consumer BI,” “pervasive BI,” or “BI for the masses,” the focus has shifted from BI professionals, who need complex tools to create and analyze information, to everyday workers who simply want to access content in meaningful ways.

Click Here to continue reading

It’s been on my mind how to help companies start thinking about their data as a strategic asset (I wrote about it here in a previous post). The IBM article below is good in that it can help CXO’s achieve a paradigm shift about their data. Specifically, point 2 is one to note. These predicted trends are putting more pressure on companies to consider a BI platform that includes capabilities to address Data quality problems in addition to just reporting, not to mention the incredible value that comes from a closed loop execution/strategy (data in/data out) vision the new SAP/Business Objects can now bring to the table.

IBM Council Predicts Data Will Become an Asset on the Balance Sheet and Data Governance a Statutory Requirement for Companies Over Next Four Years
Monday July 7, 4:56 pm ET

ARMONK, NY–(MARKET WIRE)–Jul 7, 2008 — The IBM (NYSE:IBM – News) Data Governance Council today predicted five key information challenges that will reshape corporate behavior and the market’s ability to trust information during the next four years.

The global Council recently met to discuss how businesses will handle the enormous amount and complexity of information generated by organizations and financial markets. Their findings included the prediction of five imminent, information-related issues:

 
--  Data governance will become a regulatory requirement in an increasing
    number of countries and organizations. In some countries, organizations
    will have to demonstrate data governance practices to regulators as part of
    regular audits. This will likely affect the banking and financial services
    industries first, and will emerge as a growing trend worldwide.

--  The value of data will be treated as an asset on the balance sheet and
    reported by the Chief Financial Officer while the quality of data will
    become a technical reporting metric and key IT performance indicator. New
    accounting and reporting practices will emerge for measuring and assessing
    the value of data to help organizations demonstrate how data quality fuels
    business performance.

--  Calculating risk will be used more pervasively across enterprises for
    small and large decision-making and will be increasingly automated by
    information technology. Today in most organizations, risk calculation is
    done by a select group of individuals using complicated processes. In the
    future, risk calculation will be automated providing greater transparency
    to more easily examine past exposure, forecast direct and indirect risk,
    and set aside capital to self-insure and cover risk.

--  The role of the Chief Information Officer (CIO) will change, making
    this corporate officer responsible for reporting on data quality and risk
    to the Board of Directors. The CIO will have the mandate to govern the use
    of information and report on the quality of the information provided to
    shareholders.

--  Individual employees will be required to take more responsibility for
    recognizing problems and participating in the governance process to
    facilitate greater operational transparency and the identification of risk.
    They will be aided by new categories of operational software that will
    demonstrate common data governance problems and allow employees to self-
    govern; sponsor and vote on new policies; provide feedback on existing ones
    and participate in dynamic data governance.

Data has become the new currency in today’s information economy, but many businesses make the wrong decisions by not using the right information. “There is no one-size-fits-all approach to data governance,” said Richard Livesley, head of information governance and quality, BMO Financial Group. “Every company must configure their own data governance program based on their individual needs. Likewise, there are different levels of data governance maturity and different ways of attaining it. In just a few years, data governance will become a key benchmark as boards of directors recognize their fiduciary responsibility to enhance and protect data, and markets measure business performance by looking at data value and risk on the balance sheet.”

The IBM Data Governance Council is a group of 50 global companies, including Abbott Labs, American Express, Bank of America, Bank of Tokyo-Mitsubishi UFJ, Ltd, Bank of Montreal, Bell Canada, BMO Financial Group, Citibank, Deutsche Bank, Discover Financial, Kasikornbank, MasterCard, Nordea Bank, Wachovia, Washington Mutual and the World Bank, among others, that have pioneered best practices around risk assessment and data governance to help the business world take a more disciplined approach to how companies handle data.

According to the Council, investors and consumers alike will benefit as data governance emerges as a required discipline for organizations, giving rise to greater trust, better transparency and reduced risk. Data governance today is at a crossroads, creating the opportunity for a marked change over the next four years as data quality evolves into a key performance indicator for businesses worldwide.

To meet growing market needs, the Council plans to build an Information Governance Framework based on their existing Data Governance Maturity Model that will define standards and best practices. This new framework will provide guidance for organizations to execute data governance at a granular level with defined roles, tasks, activities and a broad set of data governance procedures to make data governance a part of business operations.

“Organizations today understand that data governance is a strategic imperative but they need help making it work,” said Steve Adler, chairman of the Council. “Upcoming work by the Council will help organizations better understand governance, how it will benefit the bottom line and be reported to the markets, as well as the desired end result for each business. This is an important contribution the Data Governance Council can make in the marketplace and we invite others to participate as we build standards that define the next level of maturity for this important discipline.”

Data governance helps organizations govern appropriate use of and access to critical information such as customer information, financial details and unstructured content, measuring and reporting information quality and risk to enhance value and mitigate exposures. IBM’s work in this area supports and furthers the company’s Information on Demand strategy, announced two years ago, that has delivered results through consistent earnings growth, hundreds of new customer wins, strategic acquisitions and industry-first software offerings.

For more information on the Data Governance Council, go to: http://www-306.ibm.com/software/tivoli/governance/servicemanagement/data-governance.html

]]> http://towardintelligence.wordpress.com/2008/07/09/data-as-asset-on-your-balance-sheet/feed/ 2 jsantic http://towardintelligence.wordpress.com/2008/06/20/what-if-your-data-had-a-quota-data-as-strategic-asset/ http://towardintelligence.wordpress.com/2008/06/20/what-if-your-data-had-a-quota-data-as-strategic-asset/#comments Fri, 20 Jun 2008 17:11:45 +0000 jsantic http://towardintelligence.wordpress.com/?p=86 ]]>

There is a paradigm shift happening, but it’s not happening fast enough. The shift is in the way that businesses view the importance of their data for business intelligence. For most, strategic assets are the people and equipment that make up the steady stream of revenue companies enjoy. These “assets” require steady investment and enhancement in order to preserve the lifeblood and value they represent. But many today believe that a company’s data requires the same “strategic asset” label.

The issue that prevents a company from realizing the value of their data is their belief that “IT stuff” is cost overhead and therefore needs strict budget management. To add to this, business lines that are overhead are most susceptible to cost cuts and budgetary constraints. Some forward thinking visionaries will suggest this perspective is akin to shooting oneself in the foot and does little to encourage the strategic income value of data.

What if your data had a quota? what if you employed data quality tools to automate the governance process and measured the value in revenue on a yearly basis. Estimations would suggest that adding revenue as a result of trusted data is not a far stretch and many are realizing this. Could you save $500,000/year on internal resources alone? Sure you can. This is reachable by reducing the manual cleansing effort required for, let’s say, deploying ERP or any other application you have slated as a project for the year. If you have a lot of data, this figure represents about 8 FTE’s trying to spread-mart their way to success.

Additional questions to consider are:

• Can I get vendor rebates with a clear picture of how much I am spending?
• Will I sell more to my customers if I know exactly what they bought?
• Can I moderate my inventory to improve cash flow and reduce back orders?
• How can quicker access to acquisition data make me money?
• In what ways can sales benefit from quality data?

These questions are just a start and can lead to some significant value that you could assign a quota to. Why not?

For your reading pleasure, please have a browse through the following articles to help you further understand data as a strategic asset.

Data Quality: Relatively Critical & Critically Relative

Gartner Says Organizations Must Establish Data Stewardship Roles to Improve Data Quality

Survey: Executives Still Don’t Take Data Quality Seriously Enough

CIO.com has a good article that explores Forrester’s research into the spiking MDM interest of late. The article outlines some common problems and mistakes made by companies wanting to deploy MDM. The net of it is that it can and will be expensive; however, the value of good data and governance is worth the price. One thing the article omitted was how investing in Data Quality can be a first step toward MDM that can help neutralize some of the costs to implementing MDM down the road. Please have a read, the article in entirety is below.

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By Thomas Wailgum

May 28, 2008 — CIO — In the pursuit to achieve “one version of the truth” from their growing volumes of corporate and customer data, enterprises are struggling to implement master data management (MDM) initiatives today.

MDM is one way to achieve data truth, but it’s not easy. At a high level, MDM is a set of processes and technologies that help enterprises better manage their data flow, its integrity and synchronization. At the core is a governance mechanism by which data policies and definitions can be enforced on an enterprise scale. (For an inside look at an MDM success story, see How Master Data Management Unified Financial Reporting at Nationwide Insurance.)

The reasons for organization’s difficulties are many, including people, process, governance and cost complexities, according to a May 2008 Forrester Research report, “Trends 2008: Master Data Management,” by Ray Wang and Rob Karel. The analysts based their findings on nearly 150 MDM inquiries and interviews with end users and vendors.

From the interviews with Forrester clients, the analysts claim that executive-speak about the importance of their organizations’ data and how it must be nurtured, analyzed and protected is at an all-time high.

“Unfortunately,” Wang and Karel write, “MDM requires much more than rhetoric to survive its adoption barriers.”

Here are the five most common problems and mistakes cited by MDM early adopters and those who have had successful projects.

1. Approaching MDM as purely a technology initiative.
While IT departments and staffers will drive and sponsor many MDM initiatives, it is the business stakeholders who should ultimately define the value of the MDM efforts that can improve their business processes. They must give more than just minimal participation and sponsorship, Wang and Karel write. (See Master Data Management: Truth Behind the Hype for a look at how Wachovia handled this critical topic.)

For example, data architects often benefit from a cross-enterprise perspective, “allowing them to recognize the business impacts of a data-quality problem often not even visible to the business stakeholders themselves,” the analysts write. “Hence it’s natural for IT to evangelize early MDM efforts.” Risks increase, however, when IT takes ownership of not just the enabling technology solution but the business data definitions and rules that, in fact, must come from their business customers.

2. Assuming dirty data is just an IT problem.
Poor data quality is, obviously, a critical business barrier. “No longer relegated to the IT teams as a technical exercise, business units require accurate and up-to-date information to make key decisions,” Wang and Karel write. “Without accurate information on product inventories, customer locations and relationships, enterprises lack the ability to act on key initiatives such as serving customers efficiently, managing compliance and risk, and optimizing install base value.”

3. Managing the vast complexity of multiple data domains without proper techniques.
Cross-enterprise MDM—which Forrester defines as “transactional, bi-directional synchronization of multiple data domains across your information supply chain including all points of data capture, update and usage”—is extremely complicated, concede the analysts. Many MDM software packages are able to reduce this complexity by providing common data models, integration APIs, and Web-service-enabled features that help coordinate the “information supply chain,” as the analysts call it. (Also see Demystifying Master Data Management.)

“The technology, unfortunately, is the easy part,” according to Wang and Karel. “In fact, the data governance, prioritization, people and process aspects of implementing an MDM solution will likely derail the project before the technology fails.” In other words, change-management problems are more prevalent and dangerous than the IT hurdles.

4. Prioritizing funding and managing costs.
Not only is an MDM project complicated, but it is expensive. The software license costs for an average size implementation of an enterprise MDM solution range anywhere from $500,000 to $2 million, states the report. (If you’re just starting out, see 10 Mistakes to Avoid When Writing an RFP for Master Data Management.)

In addition, enterprises often find it costs $2 in professional and consulting services for every $1 in software licenses just to implement MDM technology, write Wang and Karel.

“When you consider the eventual costs of synchronizing the MDM solution throughout your entire data management infrastructure, some large organizations can approach a 5-to-1 professional service to software cost ratio,” the analysts write, “leading to a potential overall investment exceeding $10 million.”

5. Underestimating the level of executive sponsorship required for success.
For sure, it’s energizing for IT, business managers and users to hear senior executives proclaim that data is their company’s most critical asset and that an MDM transformation is key to the organization’s future, write the analysts.

“However, discouragement soon follows when these same executives fail to provide the necessary resources, funding and prioritization to mitigate the risks associated with bad data,” write Wang and Karel. “These scenarios often reflect an organization’s inability to incorporate a business case for MDM into overall corporate strategy.”