Affiliated with:

Examining User Adoption Issues in Analytics Projects

image 65

Business Intelligence and analytics solutions often fail if they are not supported by adequate user involvement and adoption.  Addressing the causes of user adoption issues can ensure successful efforts

One significant cause of failure in analytical solution projects is allowing enough time for adequate user adoption of a solution.  Too often, solutions are viewed only as technical projects and the project is recognized as complete when the database is populated or the tool that will provide reports or ad-hoc query access implemented.  While the technical parts of the effort appear to be complete, as the users begin to navigate the data and use the tools with live production data, there will be several necessary enhancements for the customer to see the solution as effective.  Sometimes, these changes result in disgruntled users.

Unfortunately, prevalence of this occurrence with business intelligence and analytics is common.  It is important to examine some of the causes for dissatisfaction and what can be done to mitigate this end-result.

Root Cause of Dissatisfied Users

Often users are upset because they hear IT call a project complete and they do not feel like they have a workable solution.  Ultimately, the project manager did not set the expectation with them that an appropriate adoption period was needed.  They did not understand that the enhancements and fixes that accompany a new application are natural and necessary components for a successful project.  Although it is not easy to establish this understanding with users, it is essential.

Often, the solution delivered was technically sound, data quality was consistent and reliable, and all technology layers were applied appropriately.  The development may have had extremely detailed usage requirements, and solid data requirements to support that usage.  Additionally, the project may have been delivered on time and on budget. 

However, the consultant and team did exactly what was asked for technically, implemented exactly what they agreed the client wanted, and then was no longer engaged.  The consultant would contend that with great requirements, they did exactly what they said they would.  Nevertheless, the client was left with a solution that they did not understand, did not have a chance to examine, and was not confident about the data’s sources.  Ultimately, with some changes to the application, adding some newly generated usage scenarios, some enhanced metadata management, and some user training, the organization had the original solution working and was happy with the result. 

Simply, the root cause of this situation can be traced to the original project plan.  Time was not allocated for the adoption and rollout of the solution, or for adequate time devoted to training and simple user documentation.  Usually, this is a characteristic of “build it and they will come” technically driven solutions.  It can also be a characteristic of user driven demands for project management and releasing the consultant as soon as possible to reduce expenses.  In either case, experienced parties should know enough to require sufficient time for all activities to ensure success.

Plan for Successful User Adoption

When trying to plan for the adoption and rollout of the newly built solution, it is important to consider the business need and complexity of the solution.  This must be a very interactive period for business and IT which includes reviewing data and metrics, while validating calculations and derivatives for the users’ business purposes.  Many experts say that the details will come during the requirements gathering process, but experience shows that most requirements will be validated once the data and solution are in place.  Successful organizations store all the data used to create their metrics so they can adjust metrics as needed once the supporting analysis defines new or revised rules.

So, how much time is enough for this work?  Well, consider that the rollout of a data mining type solution typically takes more interaction than a pure report based solution.  A reports based solution can be validated relatively confidently in the system and integration testing phases, while data mining is only truly accepted once a work result is completed and relevance is defined.  In your planning you need to consider key data aspects (volume, complexity, quality, and available meta data) and usage aspects (type of analytics, front ends being used, level of user proficiency with tools and approaches, volume of types of usage, and numbers of users that will leverage the system).

Once the plan is created and includes time for adoption, there are some critical success factors to leverage during the project:

  • Setting expectations with users early in the project is the best way to mitigate the inevitable challenges. 
  • Developing skills in data governance, data quality, and other areas of enterprise data management
  • Keeping users involved in data definition, validation, certification, and metric/derivative definition will help smooth the adoption process.
  • Have joint meetings to review data/metric issues and have the users actively involved with the technical resources in tracking and fixing said issues.
  • During testing, have developers and users sit together.  This is a great time to build relationships that will allow them to work jointly throughout the rollout.
  • Plan for and conduct joint training sessions with the users.  Throughout the project, identify the power users, including those who will be good candidates for conducting user training.
  • Hold a post implementation review meeting of the project about 1 month after the rollout and training.  The goal of the meeting is not to declare success, but to identify “what could we do better – as a joint team.”  This will also help in building collaborative relationships between IT and the business.


During project planning or while reviewing plans that consultants propose, keep adoption and rollout in mind.  Neglecting this key portion of the project usually has negative consequences.  Remember to set aside time in the plan and secure the business resources that will be needed to enable active adoption of the solution and continue relationship building between the business and IT.


Bruce D. Johnson

Bruce D. Johnson is an experienced IT consultant focused on data / application architecture, and IT management, mostly relating to Data Warehousing. His work spans the industries of healthcare, finance, travel, transportation, and retailing. Bruce has successfully engaged business leadership in understanding the value of enterprise data management and establishing the backing and funding to build enterprise data architecture programs for large organizations. He has taught classes to business and IT resources and speaks at conferences on a variety of data management, data architecture, and data warehousing topics.

© Since 1997 to the present – Enterprise Warehousing Solutions, Inc. (EWSolutions). All Rights Reserved

Subscribe To DMU

Be the first to hear about articles, tips, and opportunities for improving your data management career.