Why your organization should move from business intelligence to a decision-making system

There has been a lot of buzz around Decision Intelligence (DI) lately. By 2030 70% of firmsin accordance with McKinsey, will use decision intelligence in one way or another.

Organizations are likely to become data-driven as the popularity of big data and cloud computing continues to grow. Organizations that previously wanted to be data-driven organizations are now looking for more than just data management due to new challenges and staying competitive at the cutting edge.

It’s time to understand why businesses need decision intelligence now that we know the benefits of business intelligence on both the technology and commercial fronts.

According to Gartner research, “Dashboards are a reporting engine that
aggregates and displays metrics and key indicators so they can be checked against
the gaze of all possible viewers.”

To move from running a business based on intuition to running it with intelligence, all data-driven enterprises require sophisticated business intelligence solutions. Many people find it difficult to know where to start. Using data to drive business results is a notoriously difficult skill to acquire, as information comes from an almost infinite number of systems and programs.

Data visualization

Visualization is the process of presenting information and dashboards that bring together and show that information comes in different shapes, sizes and for different needs, as shown below. They can be a standalone web dashboard or an embedded report in an app, an infographic, or a mobile dashboard.

With the advent of different types of data (both big and wide), different methods and tools, the implementation of visualization projects is now driven by questions such as:

  • How is this information aggregated?
  • Where is this information consumed?
  • Who is this information intended for?
  • What is this information used for?

We know reports and dashboards have been around for a while, so what’s changed? Interestingly, not much has changed, although the basics have remained the same. Let’s find out:

Decision making intelligence

Dumping data and information into a visualization tool will only make it a busy dashboard rather than a useful one. From here, it is necessary to understand which indicators make sense and set priorities. Focus on what “decisions” dashboard users can make or infer based on the visual. So, move towards Decision Intelligence.

Research ideas

Previously, reports and dashboards were typically used for well-understood business areas for descriptive analysis. Now everything has changed (and forever). Companies now want to look at research data. Data that isn’t very clear when presented and given the ability to slice and dice with the ability to create self-service style visuals will be a great feature for the business. It also makes a more compelling use case for dashboards and visualizations in general. So focus on getting unknown/research ideas.

New data visualization

In line with other technology trends, the ability to integrate advanced analytics into a report (without the need for data science practitioners) or ask a question to be answered by voice is a hygiene factor. text. Ask a question (and customize it) to see information presented dynamically. Ask differently and get more from data.


Different people have different expectations of reports and dashboards. Therefore, the design and metrics must speak to the user and their role. Yes, it may seem that we are moving towards hyper-personalization of visualization, but this is not an exaggeration.

Use the right tool

Like everything else in the technology space, rendering has moved to the cloud (for reasons of scalability and ease of maintenance) and more importantly because new features are released first in the cloud and then on-premises (if for some reason the tool has both) . Learn, adapt and use this new and brilliant tool.

Design over the tool

More and more people are interested in receiving reports where they are – whether it’s embedded reports in a web application or a mobile version of a dashboard, or perhaps an infographic in a magazine. This ease of use provides the tools you need to visualize the right look. Create the right design and then decide on the tool.

This space will become even more crowded and exciting. We may see a lot more augmented reality, augmented reality, and cloud-based platforms to handle visualization needs. So what do we do?

This brings us back to the basics again:

People’s aspect

BI engineers, data analysts, and to a large extent ML engineers need to start thinking of themselves as digital evangelists. How many times have we heard “data-driven enterprises” in the recent past? When you boil it down, it means people who can help tell the story of what the business is or what it “could be” using data. So yes, data analysts, storytellers, digital agents and evangelists.

Process aspect

This aspect is kind of neglected because visualization is somehow between data engineering and machine learning. But each visualization (reports/dashboards) needs its own lifecycle management, requirements gathering templates, well-defined success criteria, possible testing strategy, and much more. Time to focus on this aspect and not see reporting as an afterthought development after the product.

Technological aspect

Not going to deny it, this is where things get awkward! Whatever tool and technology we choose, they will always have pros and cons. So the ideal thing here would be to try to get the design right (pipelines, update rate, metrics calculations, deployment of top-level processes). A tool used for visualization becomes just a tool. However, keeping an eye on all the features and new tools in this space and creating a CoE within the company will be a game changer.


Organizations that previously relied on dashboards and manual data analysis are moving to decision intelligence to help their business and analytics teams make better decisions, faster and more consistently. A data-driven idea is at the heart of creating new business opportunities, improving operational efficiency and strengthening customer relationships. Organizations will eventually rely on DI as a common strategic tool to quickly select the best potential business outcomes.

The path to these outcomes will be dramatically accelerated by DI, enabling faster decision-making while eradicating common BI errors such as multiple versions of the truth, decision lag, and human bias. According to Gartner, by 2023, a third (33%) of large enterprises will have analysts using decision modeling and other forms of intelligent decision making.

By uncovering information that would otherwise take months to discover and suggesting next steps, decision intelligence should complement business intelligence. Despite its potential for automation, decision-making intelligence should only be applied to simple, recurring tasks that are a consequence of automatically surfacing information.

To learn more about decision intelligence and data visualization, visit Cigniti Business Intelligence / Visualization.

  • Cigniti is a world-leading AI and IP-based digital assurance and digital engineering firm with offices in India, US, Canada, UK, UAE, Australia, South Africa, Czech Republic and Singapore. We help companies accelerate their digital transformation at various stages of digital adoption and help them achieve market leadership.

https://www.cigniti.com/blog/decision-intelligence-data-visualization-business-intelligence/ Why your organization should move from business intelligence to a decision-making system

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