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Chuck 10 Step Approach to CRM Analytics and Business Intelligence (BI)

 

A Recommended Course of Action

Consider the following strategic approach to achieve a predictable BI or CRM analytics outcome.

  1. Select Your Tools
    Software technology enablers are necessary to transform raw data into business insight. A CRM analytics or business intelligence platform generally includes an underlying data warehouse or data marts which store, aggregate and correlate data; integration or middleware tools which extract, transform and load (ETL) data across disparate sources; and data visualization technologies such as dashboards, scorecards and OLAP (online analytical processing) which permit query, reporting and interactive analysis.

    Consolidating data into a central system is an analytics best practice in order to achieve a system of record and avoid inherent problems associated with disparate and redundant data, including potentially conflicting data or multiple versions of the truth. A central data warehouse, or at least a small number of domain specific data marts, will ease data inquiry, extraction, reporting and deliverability as well as system administration.

    Information doesn't need to be delivered in real-time as much as it does in right-time. Real-time may be necessary in order to capitalize on short term opportunities, however, less frequent periodic data refreshes may be fine for monitoring more strategic efforts.

    Business intelligence solutions are available in a variety of software technologies and deployment options.

    Consider Software as a Service (SaaS) BI when … IT resources are at a minimum, budgets are tight, capital expenditures are unrealistic or you seek a proof of concept to validate the solution. SaaS analytics solutions excel where business requirements are straight forward, data sources are relatively contained and management wants to go deep within particular domain areas. SaaS analytics are extremely popular for functions such as website analytics, text mining, speech analytics and pipeline analysis. Sample SaaS CRM analytics vendor solutions include Angoss, Birst, Cloud9, Convergys, Coremetrics, GoodData, Oco, Pegasystems/Chordiant and PivotLink.

    Lead with traditional, on-premise BI software when … You have excess capacity with both IT computing and staffing, there is a cultural preference for on-site computing or you seek to an enterprise-wide solution to integrate with many heterogeneous information systems. Market consolidation has resulted in the largest vendors accounting for the bulk of the market. However, resilient pure play vendors continue to emerge and gain traction by illustrating unique value propositions, accelerated and simplified deployments, superior time to value and reduced costs. A few popular enterprise CRM analytics systems, available in multiple deployment models, include three solutions from IBM – Cognos, SPSS and Unica; two solutions from SAP – Business Objects and Crystal Reports; as well as solutions from arcplan, Board International, Infor Epiphany, Information Builders, Microsoft, MicroStrategy, Oracle, Panorama Software, Portrait Software, QlikTech, SAS, Tableau, Targit and Tibco.

    Evaluate open source BI software when … Your BI needs are likely to require customization and you seek control in modifying the tools. This option can also be attractive to ISVs (independent software vendors) seeking to embed a BI solution in their software products. Open source BI is experiencing its highest adoption in certain industries such as government and certain regions such as Eastern Europe, Southeast Asia and Latin America. Open source CRM analytics solutions are available from vendors such as Actuate, BEE, Cignex, Jaspersoft, Openi, Pentaho, SpagoBI and Talend.

    BI software solutions vary greatly in terms of scope, target market, delivery model and value proposition. A proper software selection project is your best assurance to make sure you acquire the analytics system that best matches to your business requirements and objectives.

  2. Clean Your Data
    We've all heard it – GIGO – Garbage In Garbage Out. Nowhere is this more absolute than with BI projects. Dirty data is the top cause of BI project delays. Few organizations anticipate the data quality issues, and subsequent data cleaning requirements, before they initiate the data population phase. To avoid this repeated mistake, sample each of your data sources early in order to determine data quality and allow for the needed time to scrub the data before its imported to the data warehouse.

    Once you begin seeding the data warehouse with clean data, don't stop there. Look for tools which can automate clean data acquisition and ongoing maintenance. For example, CRM systems can use tools such as account merging, address verification, spell-check and de-duplication. Getting data right at the source speeds data consolidation, reduces data maintenance and lowers costs.

    Once into production, you will need a formal process supplemented by enabling software tools to both clean and enrich the data on a go forward basis.

    Never lose sight that quality data is a pre-requisite for quality decisions; or said another way recognize that BI solutions with faulty data aid users in making poor decisions more quickly and confidently.

  3. Pursue a Phased Approach
    Business intelligence and CRM analytics programs are akin to other enterprise software applications in that well-defined, multiple-phase projects lower risk as compared to big bang or waterfall deployments. This approach also allows early lessons learned to shape future roll-outs, permits project managers to publicize (even small) victories and facilitates user adoption.

    Start with a relatively small department or business unit, possibly one with a never ending backlog of report requests. After achieving success, methodically expand your roll-outs to include more business units and integration with more information systems.

    Also recognize that seldom do performance variables and factors for even departmental objectives reside entirely within a department. Instead, process flows traverse departments and more often than not data resides in often redundant operational silos. Even with a goal to start small and advance in phases, the project should commence with the end in mind. Decision makers in any particular area must understand the enterprise-wide impact of their decisions.

  4. Measure and Refine
    Measurement should begin by actively tracking staff utilization of the BI tools. User adoption will grow over time so tracking who's accessing the tools and the volume of users over time will provide an early indicator toward ROI.

    Information analysis reveals learning and insight, however, it also raises more questions than answers, thereby requiring extending the data models for new interrogation of the data, inserting new measures or dimensions to discover new relationships, displaying the data differently for various roles and contextualizing the data to make it more actionable.

    Business strategies, business unit operational goals and functional targets are fluid so the metrics and BI solutions which support them must continually advance to remain relevant.

    Top business achievers maintain a deep understanding and direct link between their business objectives and the operational performance required to achieve those goals. Businesses advance their growth objectives when the metrics that drive those objectives forward are measured and improved as needed. Many companies recognize this connection, however, the process of aligning, religiously measuring and quickly implementing corrective actions remains elusive. For sustained success companies should implement a formal process whereby performance metrics are faithfully measured and learned from—and modified or adapted based on that learning or as the business shifts.

    Measurement also includes calculating the BI project return on investment (ROI). No enterprise software project is complete without periodic evaluation, determining whether slated goals have been accomplished and calculating payback. To this end, information management costs and benefits need to have been calculated ahead of the BI project in order to have a baseline comparison point. BI ROI should be determined periodically as additional phases are completed, information grows and adoption rates increase.

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Aberdeen Group reports that best-in-class BI adopters are 2.3-times more likely than other companies to use SaaS BI tools. Similarly, IDC forecasts that the business analytics software as a service market will grow more than three times as fast as the total business analytics software market through 2013.

 

 

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Market research firm IDC forecasts that through 2013, the business analytics software-as-a-service market will grow more than three times as fast as the total business analytics software market. "The business analytics SaaS market is poised for rapid growth as more organizations turn to cloud-based computing and alternative deployment options," said Brian McDonough, research manager for IDC's Business Analytics Solutions research service, in a statement.

 

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