Boost BI User Adoption With Effective Strategies
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BI user adoption refers to the ratio of end-users who actively use BI tools compared to the total number intended to use them. The goal is to proactively monitor, measure, and improve this ratio over time. Common struggles include users losing trust in data due to quality issues, a lack of performance and stability in systems handling large volumes and unplanned queries, and not meeting different user personas' needs such as information consumers versus business analysts.
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Boost BI User Adoption With Effective Strategies
- 1. 8/7/2018 1www.datavail.com BI User Adoption Ripu Jain
- 2. 8/7/2018 2www.datavail.com 1.Why you should care 2.Lets define it 3.Struggles and Challenges
- 3. 8/7/2018 3www.datavail.com BI is only as valuable as the decisions it enables
- 5. 8/7/2018 5www.datavail.com Lets define it: What is User Adoption? • #end-users who actively use BI VS #end-users BI was intended for. • Its more than just “training”. • Goal is to monitor, measure, and improve this ratio proactively, and over time.
- 6. 8/7/2018 6www.datavail.com Struggles & Challenges BUT WILL THEY STAY?
- 7. 8/7/2018 7www.datavail.com Struggles & Challenges Create BI User Personas Information Consumer: Executives, Managers, people strictly consuming information in form of dashboards Business Users: COO/CFO, Sales Managers, people interacting with reports, setting alerts, etc. Business Analysts: Marketing/Finance analysts, data scientists, people requiring self-servicing/mashup capabilities to do analyses Information Officers: BI Managers, IT, Power Users, people designing reports and dashboards for others to consume
- 8. 8/7/2018 8www.datavail.com Struggles & Challenges Lack of trust in data and reports lack of experience with the tool lack of data quality and standardization lack of confidence where the data is coming from and how its transformed lack of common data definition thereby obtaining different results from same DW lack of security and governance
- 9. 8/7/2018 9www.datavail.com Struggles & Challenges Performance & Stability Large data volumes Poorly designed architecture Increasing user base, and a high number of ad- hoc or unplanned queries Outages, connectivity issues, browser incompatibility
Editor's Notes
- For as long as BI has existed, organizations have made significant investments in high-performing platforms, yet the solution is shunned by the people it was intended to help. The return on investment (ROI) is not in how the organization drives the BI program, but how efficiently it enables the organization to make strategic, operational, and mission-critical decisions. Getting the right information, to the right people, at the right time, is the holy grail of BI. It is therefore obvious that all BI managers want 100% adoption of their BI tools, the reality however is far from it.
- Even with modern self-service BI and analytics tools with mobile and embedded capabilities, user adoption has only increased 7 percentage points in 10 years.
- User Adoption is the ratio of #end-users who actively use BI (either to consume information and make decisions, or do exploratory/explanatory analyses) VS #end-users BI was intended for. User Adoption is more than just “training”. Offering a contextual based training can help build user confidence. Learning the features and functionality apart, the users need to be trained how to use the tool within the organization in context of their role and department. This is more likely to encourage the user to use and rely on the system for data-driven decisions and business improvements. Lastly, user adoption doesn’t stop after training the users. It’s an on-going process that needs to be monitored, measured and improved over time.
- Pulling all data into the DW and assuming the analysts and business users will start using BI tools to extract information and make decisions has been proven false over the years.
- Different users have different needs. Taking a one-size-fits-all approach to BI and analytics will alienate portions of your user base and hinder pervasiveness. So it’s important to know who your users are and what requirements they have. Your ability to satisfy the distinct needs of each type of user in your organization will directly impact your adoption rates. Executives and management love their dashboards loaded with KPIs. Your analysts and power business users have the time, skills, and inclination to leverage data discovery and visualization tools. One way to address this is to create different BI User Personas, and group your end users in those personas based on how they will interact with the system. Examples of BI User Personas
- Another reason for lack of BI User adoption could be “lack of trust in reports”. Your users expect your BI and analytics environment to serve as a trusted means of gathering insight for planning and decision-making. This lack of trust can be because of several reasons: Lack of experience with tool – as mentioned previously, this can be addressed by end user training workshops aligned with end user role, subject matter, and the right context. lack of data quality and standardization – Almost every organization has data quality issues. If your users question the accuracy and consistency of the data being delivered to them, they’ll abandon your solution and find other ways to get the information they want. This issue can be addressed by employing DQ tools and MDM programs. The cleaner your data, the more insights your team will be able to draw from your systems. The more insights they draw, the more they’ll find value in your BI solution. lack of confidence where the data is coming from and how its transformed – addressed by hosting workshops for end users and maintaining data lineage documentation lack of common data definition thereby obtaining different results from same DW – addressed by employing Data Catalog tools lack of security and governance – Lack of a security strategy or incomplete security capabilities leads to the potential for inappropriate information distribution or the inability to provide information to the broadest audience. Questioning the data or arguing over whose data or report is the most accurate are common results of a lack of governance. This cam be addressed by adhering governance principles and having a robust DG program in place.
- Some BI and analytics initiatives experience low adoption because of poor performance and stability. Large data volumes, poorly designed data infrastructure, an increasing user base, and a high number of ad hoc or unplanned queries are just some of the factors that can negatively impact response times. Regardless of the underlying cause, users can become impatient and will seek out other ways to get the information they need. “If users have to wait ten seconds or more on a regular basis, they grow impatient and stop using the BI tool.” So while all of these challenges are common occurrences at organizations that hinder BI user adoption, every organization is different and every BI program is different.