Challenges presented by Business Analytics  

When it comes to business analytics, success often depends on whether or not all parties of an organization fully support adoption and execution. Successful BA examples—and subsequent deployment of new predictive-based initiatives—include:

  • Executive Distrust

    Getting everyone in upper management to sign off on BA implementation can be difficult. Although most organizations have embraced some form of BI and are likely handling data warehousing effectively, analytics is still an area viewed with distrust by many top-level executives, and trust must be built to effectively leverage data analysis. Presenting business analytics as supportive to existing company strategies and outlining clear, measurable goals can help convince slow adopters to approve a trial project.

  • Poor Collaboration

    Failure to achieve teamwork among a cross-section of departments can cripple the evaluation and implementation of analytics-driven initiatives. Business and IT personnel must be in-sync for an analytics strategy to succeed. Poor collaboration creates the risk that analytics won’t provide the information promised, leading to further distrust and potential abandonment of beneficial technology innovation. A cross-functional analytics team that includes major players in technology, business, operations, legal, and HR can help full-scale adoption of analytics in every department.

  • Lack of Commitment

    While many analytics software packages are presented as a prefabricated solution that is easy to implement, cost can be discouraging and return on investments are often not immediate. Although analytical models develop over time and predictions will improve, dedication is required during the initial months of an analytics initiative. Businesses that fail to make it through this crucial investment period may see executives losing trust in the solution and refusing to believe the models, eventually abandoning the concept. Process and goal owners must establish a productive analytics environment and set realistic timelines for results.

  • Slow Information Maturity

    BA implementations often fail due to lack or low quality of available data. A maturity assessment should always be performed on the company’s information architecture and data sources based on analytical requirements. Transactional, aggregated, and operational information should be scored for quality, and the existing integration infrastructure’s ability to support new sources and data feed should be evaluated. The time required to acquire, clean, and analyze new data must be built into the adjustment period.