The quantity of data produced within organizations is increasing daily as many industries shift to digital operations. To maximize the value of that data, many organizations are turning away from traditional reports in spreadsheets and static PDFs, instead deploying flexible dashboards and visualizations using business intelligence (BI) solutions that enable self-service capabilities. Self-service allows the end user to view and filter data, tailoring visualizations to their needs, getting to insights quicker for faster decision-making.
Most modern BI solutions consist of: a tool to extract, transform and load data; a data warehouse or data lake for storing and preparing data; and a data visualization platform to display insights. Traditionally, reporting focuses on historical data analysis. However, a modern BI platform allows for the storage of more complex data and provides the ability to forecast, apply predictive models, identify patterns and highlight potential actionable decisions.
On the surface, replacing traditional methods with a new BI solution appears to be simply implementing a new software platform. While for many solutions this may be the case, the challenge lies in organizational change and user adoption. For an end user, new technology means a different way of viewing, interacting with and presenting organizational data, ultimately creating what could be a steep learning curve. These changes present a barrier to widespread user adoption — and a barrier to becoming a data-driven organization. However, organizations can increase user adoption of business intelligence in a variety of ways.
Create Trust in the Data
Spreadsheets offer the comfort of seeing cell-by-cell transformations and the raw data. When data is stored in a database and compiled in a report, users might feel a loss in trust in the data presented. This could fuel skepticism of the underlying transformations or calculations because they are not easily accessible for review.
To promote trust and encourage use of these insights, it is beneficial to use a cross-functional team that enables analysts and end users to collaborate on the creation of visualizations and the calculations behind them. To take this further, before releasing new functionality, a small set of stakeholders should test new features, validating statistics presented and providing feedback for potential improvements.
Keep It Simple
Modern BI solutions are packed with new visuals. While those developing reports and dashboards grasp new visuals like ribbon charts and tree maps, business users may find these confusing at first. As a rule of thumb, if a user does not grasp what is being communicated in a visual within five seconds, the user will simply ignore it and move on. It is important to start with the use of visuals familiar to your audience, then incorporate more robust options as familiarity with the solution and new presentation of data increases.
In some cases, it will be beneficial to provide training with the release of a new report or dashboard. Demonstrating how charts and graphs interact to filter and highlight key data points — or that multiple filters can be applied at once — empowers users to find the information they need.
Provide Data Context
Providing clear context around the data points and sources presented in a dashboard helps users understand the full picture and begin to unfold the story behind the data. Simple practices include denoting the measurement time frame of a visual (by days, weeks, months, etc.) or clearly defining what is being measured in key performance indicators and metrics.
This documentation serves as a means of training, as well as a reference for follow-up questions. The documentation should consider the audience. Rather than using technical language, materials provided should be focused on business-facing users in the form of a data dictionary or user guide, using familiar terms.
Iterate Often
Data changes constantly, as do the requirements surrounding the use of it. As users become familiar with a solution, there will be no shortage of feedback on how data could be presented differently or new metrics to be incorporated. The best ideas often surface after stakeholders have a chance to digest the data. Implementing an agile business intelligence strategy allows for quick iteration, delivering stakeholder insights faster.
Part of this strategy should include creating an open feedback loop between analysts and stakeholders to foster collaboration and provide valuable insight into the end-user process. Understanding that process allows analysts to shift from simply presenting data to providing actionable recommendations.
Sorting through data to find actionable insights for your organization can be a complex process. A business intelligence solution can help you overcome your data challenges.
