Introduction

During 1990s many organizations implemented Enterprise Resource Planning (ERP) systems in order to automate back office operations. These systems become backbone of the organization’s data infrastructure. However, changes in the business environment forced organizations to leverage their decisions on entirely informational potential. In order to exceed limitations of transactional based systems to provide valid data foundation for decision making process, organizations start to apply business intelligence solutions in their data environment.

In general, there are two major approaches in business intelligence strategy:

• STRATEGICALLY: Enterprise data warehouse – As a first step you need to build single, enterprise data warehouse as a unique information repository in the organization containing full informational potential from operational sources. As a second step – you need to build  business intelligence solutions for particular business domains on top of the enterprise data warehouse
• TACTICALY: Silo based solution – targeting particular business domains by implementing  particular business intelligence solutions

This article is not focused on any of these approaches particularly (e.g. to review which of these approaches is better), but on capacity of organizations to adopt business intelligence solution in every day operations (no matter of which type of business intelligence approach they are using).

So, why user adoption is so much critical for success and existence of business intelligence systems?

The reasons are simple:

1. Business intelligence systems are primary intended to be support in decision making process. That means that users are not forced to use this system as they have to use transactional systems (the exception is when regulatory reporting leverage on business intelligence systems).
2. Considering point one and fact that business intelligence systems can be expensive (especially in case of enterprise data warehouse) if users don’t use it, eventually solution will be terminated.

Let’s find which factors are important for business intelligence solution user adoption…

User Participation during solution implementation

In general there are two types of users in the organization:

1. Data owners (business users, that owns data and using solution in the everyday work) and
2. Data stewards (technical users, system administrators  that responsible for proper solution work, configuration and parameterization of solution and so on)

Participation of users during implementation process is very crucial for solution adoption. That is good opportunity for them to understand solution from inside and to make effort to closely adjust solution with their requirements. That is especially important in case that solution is implemented from the vendor outside of the organization. As much as they learn during implementation about the solution, they will get more confidence after start using it.

Proportion of participation of both types of users is also very important. The wrong proportion between business and technical users involved in the project could result with  mismatch of business requirements and  project  adoption (too much technical, too little Business) as well as inflexible, silo based solution (too much business, too little IT) 

Data Quality

Data integration (including data cleansing) is most demanding phase in business intelligence solution implementation and it takes time. However, time is limited. After the project roll out, users expect data accuracy. They are focused on job and business issues – data inaccuracy, if happen, is additional, unexpected, disturbing issue that makes them very irritated.

Therefore, the time between technical deployment and production roll out (also known as stabilization phase) is crucial for implementation team in order to achieve required data accuracy level.  Moreover, the “package” should include the data governance policy and procedures order to preserve data accuracy during regularly data integration process. If you fail in this stage, it is very difficult (read: almost impossible) to compensate that after the production roll out. Data inaccuracy in a longer period has bad influence on user adoption and eventually results with solution termination.

Organization

Strategic deployment of business intelligence is impossible without establishing Business Intelligence Competence Center (also known as Center of Excellence) in the organization. Business Intelligence Competence Center (BICC) is formal or informal organization, which must be permanent, consist of BI team, Mentor, Business Owner and other people within or outside of organization interested in current or further usage of benefits of DW. BICC mission is to establish strategically deployment of DW within the organization.

In terms of user adoption BICC should be homeland of business intelligence in the organization. BICC should spread business intelligence evangelism throughout organization, especially to the business owners. Business owners support in user adoption is crucial.

Common language

A good communication between people requires a common language spoken by all participants. The same is for communication in the organization. Two main groups of people, business and technology professionals should have reference dictionary in order to understand each other (when someone use term “account”, this should have the same meaning for all participants). The same story is for technical solutions in the organization. Data integration requires reference data model that should be mapped to all operational data sources.

The bottom line: adoption of business intelligence solution requires easy and simple communication between participants in the business processes, and that requires common dictionary and reference data model on the organizational level.

Features and convenience

There is a different type of users: executives, analysts, ordinary users and so on. Each of these users requires different features in business intelligence solutions. An executive likes data visualization, dashboards and scorecards with KPI (Key Performance Indicator) – he doesn’t have time to analyze detailed data or to make time consuming data analysis. A Data Analyst likes analytical features such as scenario analysis, what if analysis, simulation, data mining and so on. Moreover, very often he requires feature like export to Excel (or any other spreadsheet). An ordinary user likes predefined reports, fast opening and simple for use.

The bottom line: user adoption of business intelligence solution is related to solution flexibility to deliver information in a format required by particular user (or type of user).

Training

Training is reasonably important factor in a solution adoption. A good interactive training should help users to understand the solution and his features and to give him knowledge about how to solve their business problems by using the solution.

Conclusion

Adoption of one particular business intelligence solution in the organization is definitely related with solution capability to mach user requirements. But this is not enough. There is some other factors (organizational, implementation related) that has significant impact to business intelligence solution adoption. In order to avoid project failure and solution termination, you should be proactive and consider all of them timely and systematically.