Business intelligence is not just about gathering data; it’s about turning data into actionable insights that drive informed decision-making

Organizations need to understand data and must know how to extract, market, and construct their strategy around it. Clearly, one cannot emphasize enough on the importance of data in this information age

• The significance would increase in the post-pandemic world where changes are expected across various sectors. For example: In supply chain, the procurement of consumable goods might be based on the health report of supplier
• Product pricing might be based on location
• Purchase interaction between sales agents & consumers might become completely online

1.Be flexible in choosing the delivery method

In the initial days of development, the team is busy acquainting itself with new tools, to understand the existing technical landscape. Therefore, predicting the delivery of a working software becomes a challenge. Here is how you can make your choice, based on what suits you best:

Scrum:Scrum is the most widely-adopted framework across organizations, with 54% of agile projects using it. Sprint is the heart of scrum; sprints are iterative and incremental; they are protected, time-boxed, and the commitment to complete ‘X’ number of story points is made based on the team’s capacity.

Now, many agile enthusiasts would think that working software can be delivered sprint-by-sprint, but when BI-based products are delivered, stakeholders generally like to see a trend. They want to see a flow of information from top-to-bottom and then decide on the right product.

When can Scrum not be your best bet?

Scrum, as a framework, might not work while implementing BI for certain situations:

a) Chart/Graph Type is not clear

• Group: A smaller stakeholder group
• Situation: The stakeholders look at the data for the first time and decide what type of trend they would eventually like to view. For example, should we look at data of only last year or should we see a trend in the previous 10 years first and then have a drill-down for yearly comparison?
• Effect: Frequent changes in design and previous effort goes down the drain.

b) Each department has a different perspective of looking at the dashboard

• Group: A bigger stakeholder group comprising members from various departments (finance, marketing, sales, operations, etc.)
• Situation: All departments are consuming the same dashboard. Each department has its own interest area and wants to view metrics that measure performance for their business unit. In such situations, it becomes a challenge to find consensus as a big group can have different goals and expectations.
• Effect: A new KPI might get discovered in the middle or perhaps during the project’s end-phase. Then, the entire dashboard will have to undergo an architectural re-design.

Create a culture for self-service analytics

Leadership Support:Obtain buy-in and support from organizational leaders who believe in the value of self-service analytics. Encourage leaders to actively promote and participate in self-service initiatives to set an example for others.

Training and Education: Provide comprehensive training programs and resources to develop employees’ data literacy skills. Offer training on data analysis, visualization tools, and data interpretation techniques, enabling employees to leverage data effectively.

Data Accessibility and Security: Ensure that relevant and reliable data is easily accessible to employees through secure data sources. Implement appropriate data governance and security measures to protect sensitive information while promoting data accessibility.

Community and Collaboration: Foster a collaborative environment where employees can share their knowledge, insights, and best practices related to self-service analytics. Encourage the formation of data-focused communities, forums, or internal user groups to facilitate learning and knowledge exchange.

Encourage Experimentation: Create a safe space for employees to experiment and explore data. Encourage them to take risks, try different approaches, and learn from their mistakes. Promote a culture that values learning and continuous improvement.

Recognize and Reward Data-Driven Efforts: Acknowledge and celebrate employees who actively embrace self-service analytics and achieve meaningful insights or business outcomes through their efforts. Recognize their contributions and provide incentives to encourage wider adoption.

Storytelling through visualization

Once upon a time in a bustling city, there was a team of data analysts working diligently to uncover insights hidden within vast amounts of data. They realized that their findings were often buried in spreadsheets and reports, making it difficult for decision-makers to grasp the significance of the data.

Determined to bridge this gap, the team embarked on a journey to tell captivating stories through data visualization. They understood that by transforming data into visual narratives, they could convey complex information in a compelling and easily understandable manner.

Their first step was to select the right visualization techniques. They experimented with various types of charts, graphs, and infographics to find the most effective way to represent their data. They discovered that bar charts and line graphs were excellent for showing trends over time, while scatter plots helped to reveal relationships between variables. Maps and heatmaps allowed them to highlight geographical patterns, and interactive dashboards provided users with the flexibility to explore data on their own.

Next, the team focused on crafting meaningful narratives. They realized that storytelling through visualization required structure and purpose. They carefully selected the most relevant data points and designed their visualizations to guide viewers through a logical flow of information. They sought to evoke emotions and spark curiosity, captivating their audience from the very beginning.

One of their most impactful visualizations told the story of customer satisfaction. Using a combination of colorful charts and emotive images, they illustrated how customer sentiment evolved over time. They highlighted the challenges faced by the business, the actions taken to address customer concerns, and the positive outcomes achieved as a result. By visualizing the data, they made it easy for executives to grasp the impact of their decisions on customer satisfaction, leading to more informed strategies and improved customer experiences.

The team understood the importance of simplicity and clarity. They kept their visualizations clean and uncluttered, removing unnecessary distractions and focusing on the key messages they wanted to convey. They used colors strategically to emphasize important data points and created intuitive legends and labels to provide context and guide interpretation.

As their visualizations gained popularity, the team realized the power of interactivity. They developed interactive data dashboards that allowed users to dive deeper into the insights, manipulate variables, and explore different scenarios. Users could zoom in on specific regions, filter data based on demographics, or drill down to individual data points. This interactivity enabled decision-makers to engage with the data on a deeper level, empowering them to make informed choices based on real-time insights.

Conclusion

Relevant data is indispensable for informed decision-making. To present the relevant data, one needs to build an engine — an engine that consisting of many parts such as understanding the type of analytics to be performed and the business KPI’s, deciding the tools to use based on the size of the data and number of legacy systems in your organization.