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Chuck 10 Steps to Successful Business Intelligence


A Recommended Course of Action

Consider the following strategic approach to achieve a predictable outcome.

  1. Verify Pressure For Change
    Successful BI deployments are driven by a pressure to change. Only when the opportunity associated with better decisions or the pain of poor decisions is clearly recognized and exceeds the collective effort of cash outlay, dedicating resources and business interruption will enough pressure exist to go the distance with a BI solution. Attempting to implement a BI solution, however legitimate the intention, when decision makers don't recognize a decision making dilemma is an uphill fight that will be met with change management resistance such as waning sponsorship, IT reluctance and user adoption challenges. If you find yourself in this position, begin with an education agenda and see if you gain momentum.

  2. Quantify Stakeholder Objectives
    In determining stakeholder objectives it's necessary to first define your stakeholders. As with all enterprise software deployments, visible and vocal executive sponsorship is a must, so soliciting executive team expectations early is paramount. Beyond that, if you're beginning with a departmental or line of business project, your stakeholders will likely include those business unit directors or managerial staff as well as line managers and support staff who are held to performance standards which materially affect the departments measurable objectives. Each of these roles holds relevant first hand information, is key to a successful deployment and must be afforded the opportunity to identify their objectives. Not all objectives may make project scope, but all should be surfaced, heard and considered.

  3. Dedicate Resources
    While executives and decision makers seek business intelligence solutions which can be configured, tailored and exercised without much or any IT involvement, it's a mistake to select or attempt to implement a BI product without IT participation. Even with Software as a Service (SaaS) BI solutions, which are easily provisioned on-demand and more quickly deployed, BI requires data cleansing, data staging and transfer, and integrating and consolidating data from a number of usually disparate information systems and technologies. These tasks will certainly benefit from technical talent on the IT team.

    Similarly, BI solutions must align with business objectives, focus on business requirements and deliver business and operational insight. Therefore, BI deployments should not be IT driven. Business intelligence is best accomplished by leveraging the highest and best skills from both IT and business staff, and ultimately result in a symbiotic relationship as each side is dependent upon the other for success.

    BI tools are not just for top executives or a privileged few. The goal is to connect as many operational decision makers with operational data as possible. Wider participation leads to better decisions at more levels in the organization, increased operational alignment with the company's top strategic goals and a culture of learning. Consider naming a cross-functional team or committee to define the information strategy and decision support roadmap.

  4. Determine The Most Salient Metrics
    Identifying the key performance indicators (KPIs) which are most influential in advancing business objectives is a pre-requisite to a successful BI program. KPIs must be aligned with the business imperatives that they are benchmarking. It's critical to make sure you measure KPIs that really drive performance, and not measures which are easily retrievable or traditionally reported.

    Identifying the right performance metrics is not a onetime event. As business plans, budgets and management directives change so too will the performance metrics which align to those objectives. Too stay abreast with changing business conditions, new opportunities and competitive threats, business leaders must implement a process of continuous review to reaffirm, adjust or replace metrics and see to it that the metrics measured are at all times optimal for achieving the missions for which they are aligned. It's during this review process that many managers identify new business drivers for the first time.

    Performance metrics are unique and highly dependent upon individual business goals, however, several performance indicators are common across industries. Marketing metrics may include campaign performance indicators such as response rates, conversion rates and ROI while sales may align performance with pipeline quality, forecast accuracy and win rates. Customer service is sure to measure strategic metrics such as customer satisfaction and retention as well as several operational metrics such as first call resolution (FCR) rates and up-sell conversions. It's key is begin with fewer, more relevant metrics as opposed to initially measuring everything that can be measured.

  5. Identify Data Sources
    After identifying what data is needed, create an inventory of data sources. For CRM analytics, the bulk of data is likely to reside in the Customer Relationship Management system. However, additional data stores are likely, and may include a marketing automation system, email and groupware systems, a Content Management System (CMS), an accounting software system, an Enterprise Resource Planning (ERP) application, shadow systems or even Excel spreadsheets.

    While internal transaction-based applications hold a wealth of structured data, it's the unstructured data residing in external social media and other repositories that presents both opportunity and challenge. Unstructured social content residing on social networks, blogs, rating and review sites, and online communities, to name a few, possess valuable contributory data that can be appended to existing information to add further perspective and insight for decision makers.

    Fortunately, best of breed CRM analytics and traditional BI platform suppliers have stepped up to address this opportunity—although the methods and objectives vary significantly by vendor.

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Research firm IDC predicts the global market for analytics software will grow from $25.5 billion in 2010 to $34 billion by the end of 2104.



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