How Enterprises Can Drive Value from Big Data with Decision Intelligence

How Enterprises Can Drive Value from Big Data with Decision

Organizations can leverage decision intelligence in their operations to make precise, quick, and intelligent decisions that will help them deliver on the promised return on investment.

According to the Statista report “Amount of Data Created, Consumed, And Stored 2010-2025,” the amount of data created, captured, consumed, and stored globally is expected to grow by more than 50% between now and 2025.

While gathering and viewing this data is one thing, comprehending and modifying it and then presenting it in a way that a layperson can understand is quite another. This knowledge gap and a lack of data culture within companies generate substantial accessibility challenges. Data can get siloed among departments without company-wide visibility and accessibility, limiting a company’s overall view and hindering its agility in responding to industry developments.

So, without the help of a data scientist, how can businesses tap the actual potential of big data efficiently and quickly enough to respond to trends as they occur — and even predict future market behavior? Artificial Intelligence in decision making, otherwise known as decision intelligence, is the answer.

Also Read: Ensuring Data Quality in the Era of Big Data – Three Challenges to Overcome

Why Should Businesses Employ Decision Intelligence?

The digital world is becoming increasingly complicated and unpredictable as it becomes more interconnected. And as the more complex business leads to more challenging business decisions, hence automating part of the decision-making process can help in the following ways:

Efficiency

The amount of data that companies can collect has grown to the point where using traditional ways to make well-informed decisions is inefficient.

Days or weeks could have passed when the content was delivered and considered by decision-makers. This is far too long in today’s dynamic corporate environment, where staying ahead of the competition often involves making critical choices in minutes or even seconds.

Even from the most complete big-data sets, decision intelligence can deliver recommendations quickly. Thanks to this mix of accuracy, speed, and scalability, companies can be agile enough to react to market movements in real-time.

Analytics Democratization

Since data analytics can be complex, it is often inaccessible to less tech-savvy people, but many crucial decisions are made by hands-on managers who are rarely exposed to such complicated technical data.

This is changed by decision intelligence. Unskilled employees will no longer be exposed to such perplexing data if AI is used to crunch the numbers and develop recommendations. They can avoid time-consuming conversations with experts by acting promptly after receiving all predictions and suggestions in an easily understandable way.

Also Read: Four Key Big Data Challenges to Watch Out For in 2022

Data-Driven Decisions

In today’s business world, decision-making models must be capable of analyzing existing data, predicting outcomes, and selecting the optimal options for the company. In decision intelligence, AI-decision-making systems examine the data much more closely, looking for any unusual or previously overlooked patterns. By detecting potential anomalies, AI eliminates the risk of data vulnerabilities and hiccups that could significantly impact the organization.

Eliminating Errors and Biases 

Decision intelligence systems can help minimize cognitive and emotional biases from directly impacting the outcomes of business decisions if the leadership team continues running into them when trying to make decisions. Decision intelligence algorithms can entirely avoid cognitive and emotional biases since they objectively look at the available data and are not sensitive to them.

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Prangya Pandab is an Associate Editor with OnDot Media. She is a seasoned journalist with almost seven years of experience in the business news sector. Before joining ODM, she was a journalist with CNBC-TV18 for four years. She also had a brief stint with an infrastructure finance company working for their communications and branding vertical.