Do you want to take most of your data? Are you struggling with Data-Driven decision-making for your business? Here are some hacks which will help you make data-driven business decisions efficiently.
“Big data” is maybe one of today’s most used terms. But, exactly, what is “big data”? The phrase is used to characterize the size and complexity of data sets.
If a considerable amount of information has been retrieved from a negligible amount of content, it could be labeled “big data.” Examine data-driven decision-making now.
What Does It Mean By “Data-Driven Decision Making?”
This word refers to a decision-making process that entails gathering data, extracting patterns and facts, and using those facts to draw conclusions that influence decision-making.
The practice of making organizational decisions based on facts rather than intuition or observation alone is known as data-driven decision making (or DDDM). Today, every sector aspires to be data-driven.
“Let’s not use the facts; our intuition alone will lead to excellent conclusions,” no corporation, group, or organization declares.
Most professionals recognize that, in the absence of facts, bias and erroneous assumptions (among other concerns) can cloud judgment and contribute to bad decision-making.
Why Is Data-Driven Decision Making Important?
Consistency and continuous growth are the most important aspects of data in decision-making. It enables companies to discover new business opportunities, boost revenue, foresee future trends, optimize current operational efforts, and generate actionable insights. As a result, you’ll be able to expand and improve your empire through time, making your company more versatile.
The digital world is in constant motion, and you must use data to make more informed and influential data-driven business decisions to keep up with the ever-changing landscape around you. Data-driven business decisions make or break companies. As a result, it shows the value of online data visualization in decision-making.
Steps to be followed for easier Data-Driven Decision making
Organizations that approach decision-making cooperatively consider information as a concrete asset and treat it more than companies that take other, more ambiguous approaches.
However, according to a recent survey, 58% of respondents indicated their organizations rely on gut feel or intuition for at least half of their routine business choices.
How can you ensure you make data-driven decisions void of bias and focus on obvious questions that empower your organization? The following hacks can help businesses to make better decisions based on data.
1. Identification of Problem or Opportunity
The first step is to recognize a problem or an opportunity that could be valuable and always ask, “What sort of impact will there be?” Once you’ve identified the problem, you’ll be in a better position to move forward, as having a clear understanding of the situation is the most crucial prerequisite.
A skilled data analyst is well-versed in the industry and has excellent organizing skills. To begin, consider the issues that exist in your particular industry and competitive market. Then, once you’ve identified them, make sure you completely comprehend them. Establishing this core knowledge will enable you to create more accurate inferences using your data in the future.
Begin by determining the business questions you wish to answer to meet your organization’s objectives. You can streamline the data collection process and prevent wasting resources by establishing the precise questions you need to know to inform your strategy.
2. Gather Information
After discovering challenges and possibilities, the next stage is to figure out what data-driven decision-making is relevant and what isn’t. But, more importantly, you must understand the information component to make the best decision possible.
An information element will help you understand the cause and lead you on your path to decide. It could be any data source that you feel can help you decide. Assemble the data sources from which you’ll be pulling information.
You may combine data from various databases, web-based feedback forms, and even social media. While it may appear that coordinating your multiple sources is simple, discovering common factors across each dataset can be a big challenge.
It’s all too easy to get caught up in the immediate aim of using the data solely for your current purpose. Still, it’s a good idea to see if this information could be helpful for other initiatives in the future. If that’s the case, you should work on a strategy for presenting the data in a useful way in other contexts.
3. Analyze the situation
Now comes an essential portion of the procedure. First, you must have the ability to examine data sets or information pieces. Second, when analyzing a scenario, it’s important to keep in mind that one should constantly try to comprehend the many outcomes of the analysis.
You’ll develop models at this point to test your data and respond to the business questions you identified previously. Different models, such as linear regressions, decision trees, and random forest modeling can be tested to see which one best fits your data collection.
4. Develop Options
After analyzing the trend or pattern (of data), based on the analysis, try to figure out various options which could be a probable solution. Now, creating opportunities could be easily possible when you tend to –
- Be creative and upbeat.
- Ask “what if” questions.
- How would you like your situation to be?
Now, once you have the options, it’s time to evaluate all of them or let’s say in assessing quickly, you can ask the below-mentioned questions –
- What criteria should you use to evaluate?
- Which alternative will best achieve your objectives?
5. Select a preferred alternative
All the preceding processes lead to a position in which you must select between several options. All the other alternatives are data-driven, and each one has something to offer. As a result, when deciding amongst the numerous options, you must be pragmatic.
- Examine the provisional chosen alternative for any potential negative repercussions in the future. And figure out how to respond to the following questions: What issues could it cause?
- What are the dangers of taking this step?
Your findings will ultimately assist your organization in making more informed decisions and driving strategies. However, it is important to realize that these findings might be rendered essentially meaningless if they are not presented properly.
6. Act on the Decision
So, out of all the options available, you’ve decided on one. Now is the moment to trust your data analysis and take action based on your conclusion. Keep in mind that you must have complete faith and trust in yourself and your data analytics capabilities as you move through the action.
Case Study – Google
Google, the search giant, is one of the most well-known examples of data-driven decision-making. So Google was curious whether having managers mattered in a startup, which is known for dismantling hierarchies.
Data scientists at Google looked at performance reviews and employee surveys from the managers’ subordinates (qualitative data) to answer the question. The analysts plotted the information on a graph and determined that managers were perceived as sound.
They took it a step further and divided the data into top and bottom quartiles, after which they ran regressions. These tests revealed significant disparities in team productivity, employee contentment, and employee turnover between the best and worst managers.
Good managers make Google more money and create happier employees, but what makes an excellent manager at Google? Again, the analysts reviewed data from the “Great Manager Award” scores, in which employees could nominate managers who did an exceptional job.
Employees were required to give specific examples of what made the manager so special. To balance out the data set, we also questioned managers from the top and worst quartiles.
Google’s analysis found the top 8 behaviors that make a skilled manager at Google and 3 that don’t. As a result, they revised their management training, incorporated the new findings, continued the Great Manager Award, and implemented a twice-yearly feedback survey.
Conclusion
Want to know more about how you can make easier, faster, and better data-driven decisions? Drop us an email at hi@thinklayer.com
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