Decision Intelligence has been receiving much buzz lately. McKinsey predicts that 70% of businesses will use decision intelligence in some form or the other by 2030. Gartner predicts Decision Intelligence as one of the top technology trends of 2022. Towards the end of this blog, you will understand why.
As big data and cloud computing adoption continue to skyrocket; organizations are more likely to be data-driven. With new challenges and staying competitive at the epicenter, organizations that once wanted to be a data-first entities are now looking for something beyond being just data-driven.
Business intelligence laid the foundation for enterprises to extract meaningful insights from the pooled organizational data wealth. The popular BI tools among business communities, Power BI, Tableau, etc., need no introduction.
The advent of low code and no code apps has further simplified the data gathering process internally, resulting in more effective and impactful decisions. With a gold mine of data, churning mere insights by decision facilitators isn’t adequate to stay relevant in business. The answer to all these lies in Decision Intelligence (DI), a new data and analytics trend creating quite an upstir.
Realizing the benefits of business intelligence on both the business and technology front, it’s time to understand why organizations need decision intelligence? How decision intelligence differs from business intelligence? What does it have to offer?
Here are the answers…
What and Why Decision Intelligence
While business intelligence allows organizations to analyze, visualize, and extract insights using BI tools, Decision intelligence helps transform those insights into consequential business decisions.
One of BI’s significant capabilities is to sieve the current and historical data to extract insights. Decision intelligence goes a step further. It creates an impactful convergence of technical excellence and business minds to create game-changing decisions. Find more info on what DI is and how it changes your business in our latest read.
While BI has many benefits, including organizational data-driven culture, it lacks the ability to identify the decision-making variables and risks involved, often driven manually by stakeholders or decision-makers. An intense human process is prone to errors. There are chances where stakeholders might not understand the takeaways of the insights or know what to do with the insights, leading to poor decision-making.
BI model
The current analytics model focuses on individual questions with a static model. Stakeholders break down key business challenges into static questions and formulate decisions by combining different pieces of the puzzle.
Upon analyzing the relevant data, decision facilitators provide critical insights to every minor question of a larger business challenge thrown at them and share it with decision-makers for a final decision.
While the overall process serves the need, it is time-consuming, missing real-time info, lacks decision computing capabilities, less effective, and might lead to missed opportunities due to the slow process.
The entire experience can be improved if the decision and execution steps can be supported, augmented, or automated with little human supervision. This is where organizations need an intelligent decision-making system to make the process easier and improve decisions’ success rate.
DI model = BI Model + Decision Science
Decision Intelligence is where data science meets business intelligence. It brings in the last mile advantage on business intelligence capabilities. It incorporates various decision-making techniques and algorithms to formulate a best-fit decision for a business challenge.
The AI and ML infusion in the Data intelligence platform helps eliminate human errors and allows them to make faster, high-quality decisions with less human intervention. After all, who hates having virtual assistance in analyzing and eliminating risks in their decision-making process.
However, the decision intelligence model works slightly differently from the BI model. Instead of the stakeholders breaking down the business questions into tiny pieces of static questions and mapping them back to particular BI tools or data sets, DI instantly provides the required actionable insights into your data using Machine Learning (ML) and Natural language search.
DI uses a modern architecture that does parallel processing considering all the available data and provides deep insights. In comparison, the BI process has a shortage. The DI tools directly give the insights instead of loading the system with multiple dashboards. DI tools help users get answers to the business questions directly from the insights and avoid breaking them down to static questions.
As a result, data dashboards, insights, and business analytics become more comprehensive that can connect AI and human decision-making to make high-quality, quick decisions.
Automate your Decisions with DI
There are totally three levels in which Decision intelligence can be an asset to an organization. The first level is decision assistance, which supports the decision-making process with analytics and data exploration.
The second level is decision augmentation which provides the predictions and all the possible outcomes along with the predictions.
The third level is decision automation. With AI and process automation in place, it can perform both the decision and execution step with the human over vision. Not all decisions can be automated. Important and sensitive decisions like designing a business strategy can be supported and augmented but not automated.
More Differences between Business Intelligence (BI) Vs. Decision Intelligence (DI)
| Business Intelligence | Decision Intelligence | |
| Purpose | BI systems were designed to process the historical data for the predefined queries with a traditional analytic model. | Decision intelligence is a combination of dashboard overload and advanced AI to predict the future and provide suggestions. |
| Insights | Provides statis insights | Delivers dynamic predictions and recommendations |
| Time | With BI, the stakeholder or the decision-maker should further analyze and manipulate multiple dashboards, to arrive at a business decision, which is often time-consuming. | Decision intelligence provides a plan of action based on real-time insights, leading to faster decisions. |
Takeaway
Extracting insights from traditional BI platforms and self-service BI tools has become more complex and irrelevant for many day-to-day decisions with an increasingly complex data landscape. Business teams require analyzing massive amounts of real-time data across channels to take the daily, on-demand micro-decisions that impact an organization’s long-term success. Static reports and surface-level dashboards will not suffice in outperforming the competition. To fully capitalize on new opportunities, the time-to-insight and time-to-decision must be near real-time. Organizations must use all the available and relevant data while making business decisions. Analyzing the vast amounts of data along with risk modeling and decision-impacting variables is the need of the hour, and only decision intelligence holds the answers for these.
Business Intelligence (BI) Vs. Decision Intelligence (DI), deciding which technology to implement in the decision-making process is crucial for organizations. It is BI, along with the capabilities of DI, that organizations should consider.
How to get started with DI?
Decision intelligence systems are consumption platforms. They absorb current data and apply your AI and machine learning models to provide contextual suggestions. Today, most firms use AI or machine learning models to serve specific analytic aims. So getting started with decision intelligence can be done with few pre-requisites that include
- A proper data fabric
- AI/ML models
- Equipped IT team
- A focused use case
- A technology partner
If you aspire to go beyond being data-driven and make high-quality decisions faster, then you are at the right place. Get in touch with us now.