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I D C A N A L Y S T C O N N E C T I O N

Dan Vesset

Program Vice President, Business Analytics

Best Prac t ices for a B I and Ana ly t ics St ra tegy

February 2012

At a time of great economic volatility, there has been a significant decrease in the ability of decisionmakers at all levels to rely effectively on only experience or intuition to make decisions. The demandto respond faster, with greater insight into ongoing internal and external events based on facts, is

increasing. As a result, a growing number of organizations are moving toward having more pervasivebusiness intelligence (BI) by turning to evidence-based decision making supported by a range of BIand analytics technology and processes that enable decision makers to have the best possibleintelligence about customers, finances, operations, suppliers, and the market.

Although IDC is expecting worldwide spending on business analytics software to be $33 billion in2012, technology is only part of the story. Higher BI and analytics competency and pervasiveness areachieved when organizational culture, business processes, and technologies are designed andimplemented with the goal of improving or automating all strategic, operational, and tactical decision-making capabilities of all stakeholders.

That is a lofty goal, but one that leading organizations are striving to achieve. There is growingevidence that higher BI and analytics competency and pervasiveness have a direct impact oncompetitiveness. However, justifying large capital outlays for software will be challenging unlessshort-term benefits can be directly correlated to the investment. As more incremental projects are

undertaken, it will be important to execute these projects within the long-term strategic plan oforganizationwide decision management.

The following questions were posed by SAP to Dan Vesset, program vice president of IDC's Business

Analytics research, on behalf of SAP's customers.

Q. What is the value of a BI and analytics strategy to a process composed of many

incremental BI projects?

A. BI and analytics technology (and projects) is never an end in itself. The technology, people, and

processes exist to provide support to decision makers or to automate certain decision-making

processes. These in turn must lead to actions based on insight and augmented with experience.

Therefore, it is imperative to have a strategy that guides the organization toward effective and

pervasive use of BI and analytics resources. Such a strategy needs to take into account the broadspectrum of decision makers and decision-making processes that exist in any organization.

For example, at one end of the decision-making spectrum is a relatively small number of

strategic decisions with broad scope and a high level of uncertainty made by executives in a

collaborative environment. At the other end of this spectrum are thousands or tens of

thousands of tactical decisions made by front-line employees or, in some cases, systems.

Any one of these decisions has limited scope and impact, and most are made "in the field"

without much collaboration. In the middle are ongoing operational decisions made by

managers, business analysts, and quantitative analysts or data scientists.

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Each of these broadly defined groups has a different set of data and technology

requirements. Yet, a recent IDC study of over 4,000 organizations revealed that only 25%

have a BI and analytics strategy. The lack of a strategy leads to execution challenges.

Q. What are the factors to consider when creating a BI and analytics strategy?

A. IDC encourages organizations to develop an organizationwide BI and analytics strategy that

articulates responses to the following questions:

What are our organization's business objectives, challenges, and goals, and how do we

measure progress toward these goals?

Are our BI objectives aligned with our business objectives? What metrics or key

performance indicators (KPIs) exist to ensure that measurement of progress toward

organizational goals is made possible?

What are the types of strategic, operational, and tactical decisions being made at

different levels in our organization?

Who are the different user groups and user types making these decisions?

What data sources are required to support the decisions being made at our organization?

What BI and analytics technology functionality is required to support the types of

decisions and decision-making processes of various end-user groups, including

executives, managers, business analysts, quantitative analysts, operational staff,

customer-facing staff, and external stakeholders?

What staffing needs and organizational structure are required to ensure that individuals

or teams exist to address tasks such as data integration, data quality, data management,

master data management, report or dashboard development, data analysis, and

information access?

What technology components exist or are needed to ensure that the decision support orautomation needs of all decision makers are addressed?

Responses to these questions will assist you in defining a long-term BI and analytics

strategy. However, it's also important to conduct a periodic assessment of responses to these

questions and a review of the level of BI and analytics competency and pervasiveness in

your organization.

Q. What are the benefits of a BI and analytics solution?

A. Today, not only access to information but also the ability to analyze and act upon that

information creates competitive advantage in commercial transactions, enables sustainable

management of communities, and promotes appropriate distribution of social, healthcare,

and educational services.

The value of better BI and analytics is often expressed in such intangible terms as the ability

to make better decisions, where "better" is usually undefined. In practice, however, there is

growing quantifiable evidence of the value of BI and analytics.

Our studies have shown that indicators that define BI and analytics competency and

pervasiveness have a direct correlation to the competitiveness of organizations within their

respective industries. This positive impact on an organization's competitiveness is achieved

by affecting business process improvements, productivity, and cost containment.

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Other IDC studies have identified the median ROI of BI and analytics projects to be just over

100%, while the benefits accrued by organizations deploying BI and analytics technology and

processes split as follows:

54% from business process improvements

42% from productivity gains

4% from technology cost savings

In other words, 96% of the benefits are in the productivity and business process

enhancement categories. Both types of benefits are important. Yet there is a limit to the

efficiency gains due to productivity in bringing information together. The data-gathering

process can be streamlined only so far before diminishing returns set in.

This is not the case with business process enhancement, including product development and

service delivery innovations. The cycle of feedback and correction can be continually

improved. Models can be made more accurate in predicting the impact of policy changes.

Employees can improve their judgment as they learn to incorporate relevant feedback in

making better decisions.

Q. What are examples of best practices that lead to pervasive and highly impactful BI and

analytics use?

A. Today's BI, analytics, and data management challenges (and opportunities) are likely to

overwhelm organizations that are unprepared for the emerging changes. IDC defines these

organizations as "fumblers." By comparison, "fact finders" have the following characteristics:

A high reliance on BI and analytics and consider it critical to their competitiveness

BI and analytics solutions' output has a high level of influence on all employees' actions

BI and analytics are deployed to support individual, intragroup, and intergroup decision

making

Our research demonstrates that, as a group, fact finders are more competitive than fumblers.

80% of the most competitive organizations are fact finders almost 30% more than among the

least competitive organizations. IDC's worldwide BI and analytics analyst team has spent years

evaluating best practices at thousands of commercial, nonprofit, and public sector

organizations. Common themes and specific practices unite the organizations we define as fact

finders. In addition to having a stated BI and analytics strategy, these organizations focus on:

Trainingformal, informal, and embedded into technology on the meaning of data,

the use of BI and analytics tools, and the use of analytics to improve decision making.

They highlight how to improve existing process and expose what's possible today to drive

innovation.

BI and analytics system design quality to ensure the ability to rapidly respond to changing

business conditions and end-user requirements. For example, they measure the extent to

which end users' expectations about the speed of adding various BI and analytics

solution components (e.g., new dashboard, new data source, and new KPI) are met by

the IT group.

Data governance by placing a high level of importance (including providing funding) on a

data governance group and associated data governance policies.

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Nonexecutive management involvement in promoting and encouraging the design and

use of the BI and analytics solution. While executives are important to initiating and

funding projects, research shows that the next layer of management is instrumental in

spreading the use of BI and analytics technology and processes throughout the

organization.

Performance management methodology as one of the key means to align the

organization with a set of defined metrics and KPIs that influence user behavior.

A B O U T T H I S A N A L Y S T

Dan Vesset is program vice president of IDC's Business Analytics research. Mr. Vesset's research is currently focused on

the business intelligence and analytic applications markets, which encompass multidimensional analysis, end-user query

and reporting, data mining, and other related business intelligence tools as well as supply chain and operational analytic

applications.

A B O U T T H I S P U B L I C A T I O N

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