Top Benefits of Data-Driven Decision Making

In this new information age, Data is the most important word. Data and information is what helps people make their decision no matter how trivial they seem, whether it be identifying the customer’s needs, target audience, or minor decisions like which preschool to send your kids to. Whereas when it comes to corporate and working sectors, Data helps people make better decisions. 

What Is Data Driven Decision Making?

Data-driven decision making or DDDM is the process of collecting, analyzing, and making decisions based on the insights gained from the information gathered. 

The procedure is more objective, and it can be rapidly assessed based on the data’s impact on metrics.

Every industry requires decision making based on data. It aids management in planning to see what can be done to speed up production and save time. Data based decision making also aids in the utilization of historical data to forecast future events. Large enterprises can utilize the method for big data analysis, diagnostic modelling, and processing to improve their results. 

Why is data analysis important? Here are the top benefits of effective decision making that is data-driven:

Accountability & accuracy

One of the most significant advantages of a decision making in management is that it improves openness and accountability for all organizations. Most firms utilize objective data to gather data, retain records, and comply with regulations. As a result, the company is held accountable for appropriately managing its data.

Effective Feedback

It is the best way for market research. Data-driven businesses amass massive amounts of data over time. Much of this information is effective feedback that reveals what customers like and don’t like. This information is useful for fast wins, but it may also be used to make long-term changes. With so much data, it’s much easier to see patterns and uncover holes in your products or services as they emerge. Based on the data you’ve gathered, create new goods or transform existing offerings to drive growth.

It is cost-effective

You can use the information gathered to discover potential cost-cutting strategies across the board. It’s possible that an excessive amount of money is being spent on a particularly unsuccessful marketing plan. Alternatively, the data could suggest that a certain product is returned at a higher rate than others. With this data, you can evaluate the goods, discover and handle customer complaints, and cut down on superfluous administration and shipping costs. The more data is used efficiently in decision-making, the more nimble your company becomes. Data-driven businesses may outperform their competitors and increase income with this agility. The best forward-thinking big data in businesses will utilize this money to support new products and expand into new areas. Big Data requires a lot of interpretation and to understand this data is no challenge. But with the help of cost effective KPI Dashboards which show you effective insights on how to understand your Business Intelligence Metrics businesses can function much more effectively.

Helps optimize decision making

The enemy of progress is indecision. While you’re pondering and making decisions, your competitors have already taken actions to get an advantage. Projects can proceed slowly if you don’t have good evidence to back up your judgments, especially if there’s a disagreement or opposing viewpoint. Data-driven approach in business can move considerably more quickly and with better confidence in their decisions. They have cold, hard evidence to back up their claims, thus a protracted debate is unnecessary.

Saves a lot of time

Companies who have differentiated themselves through good data visual analytics and insights have already realized what those that are falling behind are only now realizing. Data informed decision making has several advantages, one of which is that they may be done fast and precisely. They neither have to predict what users want or have to spend hundreds of thousands of dollars to alter courses when they’re incorrect. At the start of the race, they’ve already stepped over the starting line, while competitors are still tying their shoes during iterative phases.

Examples of Data Driven Decision making Companies.

Corporations have now understood how data is the prime factor in any kind of decision making whether it be business or internal workflow. Below are some examples of companies who make the best of data and influence it in effective way. 

Google & Google Analytics:

Google is a search engine that is working day and night to understand better how people and their search query works. It is even trying to serve their users a much better way to provide them the correct information, while coming up with new algorithms that enforces website owners to make their content much more informative. They also have Google analytics that helps website owners how to serve their users with better information. Google Analytics is a great example of a Data Driven Decision Making tool that helps businesses.

Netflix:

Netflix is working hard to come up with new content on their streaming platform but also they work on understanding their user needs. Netflix developers use google analytics to track people’s searches on which content should be on their streaming platform and if the content rights are too expensive they try to go for related content. For eg. Christopher Nolan’s movie Tenet’s rights are too expensive. So they try related content which is similar in this case i.e., the Director’s other movies that are least expensive and can be streamed on their platform. Netflix is again using google search evidence data to make decisions and serve their viewers better.

Takeaway:

Corporations are harnessing the power of data and using prediction analysis and business intelligence to make better financial and business decisions to serve users. Ubiq is a Business Intelligence Software that will help you make the best and informed evidence based decision making for your business.

Bonus Read: Data Analytics vs Data Science