According to Gartner, “There remains a 70% likelihood that a BI project will fail to meet expectations.” It doesn’t matter the size of your company, business intelligence (BI) projects take time, expertise, and training. Additionally, your company culture needs to embrace test, trial and failure methods in a documented, scientific method.
Knowing the odds are stacked against your BI project succeeding, here are the three most important mistakes to avoid.
Lack of Leadership
Business Intelligence implementations take months to plan, test, deploy and roll out. Choosing the leadership for an enterprise-wide system such as BI is a strategic, long-term decision. Considerations of competency, dedication and leadership all need to be weighed heavily.
Where most BI projects fail, is from of lack of leadership. Either having a leadership void and not one, clear person in charge, or selecting a BI project manager that is not equipped either technically or interpersonally to get the teams, tools and technology in sync.
Because BI systems interface with every data point within your organization, your BI project manager needs to have a clear understanding of your business objectives and systems, they also need to have the full support of the executive leadership team. The full support of the executive leadership includes having dedicated resources, including IT’s involvement.
Your BI project manager has to have the interpersonal skills required to manage and navigate conflicts and problems when they arise. They need to be the point-person to keep stakeholders informed on timelines, deliverables and possible obstacles or problems. They also need to be able to ‘rally the team’ to work together on one common goal that will impact the business with significant outcomes.
Not Defining Data Quality Standards
According to a Harvard Business Review article, most data fails to meet “data are right” standards. The reasons vary from the creators not understanding the scope, to poorly calibrated measurement tools, and overly complex processes or human error.
Data scientists clean the data before training the predictive model, but this time-consuming work and does not correct all the errors and often, after all this effort, the data still does not meet “the right data” standards.
Data quality is one of the biggest challenges BI solutions face today. What many employees are not aware of is that data quality doesn’t have to do with only deleting supposedly ‘bad’ or ‘poor’ quality or inaccurate data. Retaining good quality data has everything to do with keeping comprehensive data that is consistent, coherent and complete.
Pursue a Phased Approach
Business intelligence, just like other enterprise software deployments, are similar in that well-defined, multiple-phase projects lower risks as compared to one big dramatic roll-out. This approach also allows early lessons learned to shape future rollouts and permits project managers to publicize (even small) victories that facilitate 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.
Is Your Company Ready for BI?
At ASB Resources our Business Intelligence experts will help you gain a competitive advantage by predicting customer behavior and accurately forecast the smartest direction for your business to take. If your company is ready for BI, contact our business intelligence experts today.