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Chuck A 10 Step Strategic Approach to Successful BI Deployment

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 By Chuck Schaeffer

A Business Case For Business Intelligence with a Focus on CRM Analytics

Table of Contents

  1. Executive Summary
  2. The Business Problem
  3. The Business Solution
  4. A Recommended Course of Action
    1. Verify Pressure For Change
    2. Quantify Stakeholder Objectives
    3. Dedicate Resources
    4. Determine The Most Salient Metrics
    5. Identify Data Sources
    6. Select Your Tools
    7. Clean Your Data
    8. Pursue a Phased Approach
    9. Measure and Refine
    10. Raise The Bar
  5. Conclusion


Executive Summary

Business Intelligence, often referred to as 'decision support' or 'CRM analytics' when aligned with CRM software systems and customer strategies, empowers decision makers to better understand, analyze, forecast and impact business performance. Business intelligence software tools help transform raw data from multiple sources into useful information and distribute this insight to all who can use it, when they need it, in order to improve decision making timeliness and accuracy.

As with other enterprise software applications, implementing business intelligence (BI), decision support or CRM analytics software tools without accompanying strategy, business process support and IT alignment will risk implementation, challenge adoption, and likely not achieve objectives or return on investment (ROI). This advisory proposes a 10 step strategic approach to successfully plan, justify and deploy a BI or CRM analytics solution.

The Business Problem

Management's ability to consistently make timely and accurate business decisions—at both strategy and operational levels—is extremely influential in determining whether the company surpasses, or gets surpassed by, competitors. Yet for too many business executives, decision making is an art that comes with experience. And experience is what you get when you don't get what you expected. These decision makers find themselves trying to make decisions based on incomplete, inaccurate, irrelevant or stale information.

Decision making from intuition or just gut feel lacks replication, predictability and scale of successful outcomes—and decision makers seek methods and processes to leverage data-driven, fact-based decision making.

Excel normally emerges as a stop gap measure on the road to business intelligence. However, the inherent flexibility of spreadsheets is a double edged sword—enabling data massaging at will—but also leading to lack of data integrity with a simple transposition error or when a sum formula misses a row, or eventually multiple versions of the truth when team members working on a common project argue over who's data is 'more accurate'. Questionable reliability ultimately leads users to distrust the data and seek alternate solutions. Spreadsheets are powerful tools which should compliment decision making solutions, but put decision making at risk when pretending to be the system of record.

Decision makers recognize their need to graduate from spreadsheets and leverage a more strategic technology tool in order to access more data, more timely and with greater integrity. Executives, managers and other decision makers also seek automation solutions which allow them to spend less time retrieving and compiling historical information and more time analyzing information that supports their most pressing business initiatives, allows them to better plan for the future, quickly identifies areas that need attention and systemically delivers the insight to contribute to improved decisions.

The Business Solution

Organizations normally possess plenty of valuable data, albeit in different repositories that resemble silos. Prospect data in the sales force automation (SFA) system, lead data in the lead management system, service history in the call center application, customer data in the customer relationship management (CRM) system, product and sales data in the enterprise resource planning (ERP) system, sales performance data in the incentive compensation system, business plan and budget data in spreadsheets and so on. Only when the data is consolidated can relationships, patterns and otherwise difficult to identify insight be discovered.

To better execute business strategies and outperform competitors, business leaders are pursuing a combination of systemic processes and decision support software tools which synergize to better source, aggregate, contextualize and deliver business insight to knowledge workers, operational managers and decision makers throughout the enterprise.

Next - The Recommended Course of Action >>

FirstPriorBusiness IntelligenceBI ApproachBI SoftwareBI EvolutionNextLast



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According to Gartner, business intelligence is an interactive process for exploring and analyzing structured and domain-specific information to discern trends or patterns, thereby deriving insights and drawing conclusions. The business intelligence process includes communicating findings and effecting change.


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Analytics lacks a consensus definition, however, the term often describes a BI capability or technique (such as predictive analytics) or BI applied to a particular subject area or business domain (such as CRM analytics).


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