What are the best ways to identify BI problems in your organization?

Accessing BI maturity involves going beyond simply looking at the features of the BI product itself. A crucial factor is user adoption - how many employees are actively using the BI tools and, more importantly, how well they can translate the data into actionable insights. BI is most powerful when it empowers informed decision-making across the organization. By gauging user adoption and the impact of data insights on business choices, companies can determine how well BI is acting as the pulse of the organization, guiding it towards strategic goals.

BI tools can be used to solve complex business problems by providing data-driven solutions and recommendations that support decision making and action taking in a business domain. They can help users evaluate and compare alternatives, monitor and measure outcomes, and learn and improve over time.

Taking a linear approach doesn’t solve the issue, instead think ‘disruption led adoption’ ChatGPT - by Jan ‘24 just 2 months since launch, 100 million monthly active users!! Instagram took over 2.5 years!! So it’s not a user adoption issue. Look at innovation driven adoption within enterprises. Create products which solves real world challenges within the organisation. These maturity assessments and maturity levels are fluff. Keep innovating and disrupting your business.

Assessing your organization's BI maturity level is a foundational step in identifying and addressing BI problems. By understanding where you are, where you want to be, and the obstacles in between, you can chart a path toward enhanced data-driven decision-making and business performance.

Defining clear and realistic Business Intelligence (BI) objectives and scope is essential for ensuring the success of your BI initiatives. This step provides a roadmap for achieving your desired outcomes while setting boundaries to manage expectations and mitigate potential challenges. Let's set clear goals for our BI project. These goals should match what we want to achieve in our business and why. Also, we need to define what our BI project will cover – like where we'll get data from, what tools we'll use, who will use it, and what we expect to get out of it. By doing this, we make sure everyone knows what we're aiming for and avoid any confusion or changes that could cause problems later on.

Conduction a data audit and quality check are pivotal to ensuring that your BI project is built on reliable, accurate, and relevant data. This involves reviewing data sources, processes, and governance to identify any issues that could impact data quality. By validating, cleaning, and standardizing your data, you ensure it meets the requirements of your BI project. Addressing data quality problems early on helps prevent errors and misleading insights, laying a solid function for effective decision-making.

In my opinion, identifying BI problems in an organization involves: - Conducting a comprehensive data audit to assess data quality, accuracy, and completeness. - Performing regular data quality checks to identify inconsistencies, anomalies, or errors in the data. - Analyzing data usage patterns and user feedback to pinpoint areas of inefficiency or dissatisfaction. - Reviewing BI reports and dashboards for relevance, timeliness, and accuracy. - Seeking input from stakeholders across different departments to gather insights into their BI needs and challenges. - Leveraging analytics tools to identify trends, outliers, and potential areas for improvement in BI processes.

One thing I find helpful is engaging stakeholders and users is not just about keeping them informed, it is about actively involving them in the BI process. This means soliciting their feedback, listening to their concerns, and incorporating their insights into the development and refinement of BI output. This involvement ensures that the BI project aligns closely with their requirement, increasing the likelihood of successful adoption and utilization.

Testing and validating your BI outputs are essential steps to guarantee the integrity and usability of your insights. By investing time and effort in this final phase, you can maximize the value and impact of your BI initiatives, driving better decision-making and business outcomes.

In my opinion, testing and validating BI outputs is essential for identifying BI problems in an organization: - Conduct thorough testing of BI reports, dashboards, and analytics to ensure accuracy and reliability. - Validate BI outputs against known benchmarks, KPIs, or industry standards to assess their validity. - Utilize user acceptance testing to gather feedback from stakeholders and identify any discrepancies or issues. - Implement data reconciliation processes to verify consistency between different data sources and BI outputs. - Regularly review and audit BI outputs to identify and rectify any discrepancies or anomalies promptly.

The main area I’d focus on is the reality of business events to what our Business intelligence tools tell us: How do we identify this? Business intelligence revolves around the quality of the data. This quality is known mainly to the key business folks who impact the day to day data consumption. Ex: There is an acquisition event where the organization added x number of employees from the new acquired entity. The reason for the acquisition was listed as ‘New Hire’ instead of ‘Acquisition’. When we look at the BI dashboard, we’ll just see the spike in hiring but with no meaningful reason. So BI teams have to work closely with the business users to validate the events & define proper data definitions for accurate reporting.