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The Power of Decision Intelligence in Data- Driven World

The value of data in decision-making depends on its reliability and development and also it allows businesses to find new business possibilities, increase revenue, forecast future trends, improve existing operational efforts, and generate actionable insights.

The Power of Decision Intelligence in Data- Driven World

Data is the new currency, and rightfully so. It is feasible to make better-informed decisions that will lead to commercial progress, development, and an enhanced bottom line by harnessing the abundance of digital ‘big data’ accessible at your fingertips. And by embracing the potential of business intelligence.

In a data-driven environment, it is crucial that we understand the power of decision intelligence or Data-Driven Decision Making (DDDM). It is a practice of gathering information based on quantifiable objectives or KPIs, evaluating patterns and facts from these findings, and implementing strategies and actions that benefit the company in a variety of ways.

Now, why exactly is DDMM important for you to exercise?

We’ll tell you.

The value of data in decision-making depends on its reliability and development. It allows businesses to find new business possibilities, increase revenue, forecast future trends, improve existing operational efforts, and generate actionable insights. As a result, you’ll be able to develop and modify your business over time, making your company more flexible.

This blog talks about the vitality that lies in data-driven intelligence, how to ensure it, and the mistakes you should avoid!

DDMM: Qualitative Or Quantitative?

Essentially, DDMM entails achieving important business objectives by relying on verified, studied data rather than winging it. Data scientists rely on two forms of DDMM – qualitative and quantitative.

Interviews, films, and anecdotes are examples of qualitative analysis that aren’t characterized by statistics or metrics. Observation rather than measurement is used in qualitative data analysis. It’s critical to code the data, in this case, to guarantee that things are grouped together in a methodical and logical manner.

On the other hand, the focus of quantitative data analysis is numbers and statistics. The median, standard deviation, and other descriptive statistics are important in this situation. Rather than being seen, this sort of study is measured.

To create better data-driven business decisions, both qualitative and quantitative data should be examined.

How To Establish An Improved Data-Driven Decision Making Process?

Knowing the importance of DDMM is not enough. You must also know how to effectively ensure it. We have curated three takeaways that you should keep in mind while establishing an enhanced Data-Driven Decision-Making strategy. Keep reading below!

1. Gather Data ASAP

This should be the priority one. Early data collecting should be a top task for small enterprises and start-ups. Also, implementing a business dashboard culture in your organization is critical to effectively managing the tremendous amounts of data you’ll be collecting.

2. Make Data Presentation Logical

Present the information in a relevant manner. Researching for information and gaining insights is as important as communicating your message effectively. By creating and configuring a powerful web dashboard that will convey your data narrative, your team and management can make the best data-driven business choices.

3. Create Quantifiable Goals

After you have your data, and your insights, set quantifiable objectives to ensure you’re on the correct track. Your goals should match your process – and that can help to establish an effective DDMM strategy. .

Google’s Success Story Of DDMM

Startups are known for dismantling hierarchies, and Google wanted to see if having managers made a difference.

Google data scientists examined performance reports and employee questionnaires from the managers’ subordinates in order to answer the question (qualitative data). In terms of team productivity, employee contentment, and employee turnover, these studies revealed significant disparities between the best and worst managers.

The analysts looked at data from the “Great Manager Award” ratings, which allowed workers to nominate outstanding supervisors. Employees were required to give specific examples of what made the boss so special.

To complete the data set, managers from the top and bottom quartiles were questioned. According to Google’s research, there are eight characteristics that make a great manager and three that don’t. They updated their management training to reflect the new results, kept the Great Manager Award, and instituted a bi-annual feedback survey.

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