5 Reasons Why Data Analytics is Important in Problem Solving

Data analytics is important in problem solving and it is a key sub-branch of data science. Even though there are endless data analytics applications in a business, one of the most crucial roles it plays is problem-solving. 

Using data analytics not only boosts your problem-solving skills, but it also makes them a whole lot faster and efficient, automating a majority of the long and repetitive processes.

Whether you’re fresh out of university graduate or a professional who works for an organization, having top-notch problem-solving skills is a necessity and always comes in handy. 

Everybody keeps facing new kinds of complex problems every day, and a lot of time is invested in overcoming these obstacles. Moreover, much valuable time is lost while trying to find solutions to unexpected problems, and your plans also get disrupted often.

This is where data analytics comes in. It lets you find and analyze the relevant data without too much of human-support. It’s a real time-saver and has become a necessity in problem-solving nowadays. So if you don’t already use data analytics in solving these problems, you’re probably missing out on a lot!

As the saying goes from the chief analytics officer of TIBCO, 

“Think analytically, rigorously, and systematically about a business problem and come up with a solution that leverages the available data.” 

– Michael O’Connell.

The 4 Types of Analytics Explained x
The 4 Types of Analytics Explained

In this article, I will explain the importance of data analytics in problem-solving and go through the top 5 reasons why it cannot be ignored. So, let’s dive into it right away.

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What is Data Analytics?

Whenever you perform any operation on any data, intending to explore that data and find different trends or conclusions in it, you’re analyzing the data, which’s exactly what we call data analytics.

Data analytics is the art of automating processes using algorithms to collect raw data from multiple sources and transform it. This results in achieving the data that’s ready to be studied and used for analytical purposes, such as finding the trends, patterns, and so forth.

Businesses are using data analytics in a variety of ways. From predicting their customer behaviors to making more informed business decisions, analytics is everywhere. A recent survey shows that around 94% of enterprises believe that data and analytics are important to their growth.

Why is Data Analytics Important in Problem Solving?

Problem-solving and data analytics often proceed hand in hand. When a particular problem is faced, everybody’s first instinct is to look for supporting data. Data analytics plays a pivotal role in finding this data and analyzing it to be used for tackling that specific problem.

Although the analytical part sometimes adds further complexities, since it’s a whole different process that might get challenging sometimes, it eventually helps you get a better hold of the situation. 

Also, you come up with a more informed solution, not leaving anything out of the equation.

Having strong analytical skills help you dig deeper into the problem and get all the insights you need. Once you have extracted enough relevant knowledge, you can proceed with solving the problem. 

However, you need to make sure you’re using the right, and complete data, or using data analytics may even backfire for you. Misleading data can make you believe things that don’t exist, and that’s bound to take you off the track, making the problem appear more complex or simpler than it is.

Let’s see a very straightforward daily life example to examine the importance of data analytics in problem-solving; what would you do if a question appears on your exam, but it doesn’t have enough data provided for you to solve the question? 

Obviously, you won’t be able to solve that problem. You need a certain level of facts and figures about the situation first, or you’ll be wandering in the dark.

However, once you get the information you need, you can analyze the situation and quickly develop a solution. Moreover, getting more and more knowledge of the situation will further ease your ability to solve the given problem. This is precisely how data analytics assists you. It eases the process of collecting information and processing it to solve real-life problems.

Data analytics is important in problem-solving

5 Reasons Why Data Analytics Is Important in Problem Solving

Now that we’ve established a general idea of how strongly connected analytical skills and problem-solving are, let’s dig deeper into the top 5 reasons why data analytics is important in problem-solving.

1. Uncover Hidden Details

Data analytics is great at putting the minor details out in the spotlight. Sometimes, even the most qualified data scientists might not be able to spot tiny details existing in the data used to solve a certain problem. However, computers don’t miss. This enhances your ability to solve problems, and you might be able to come up with solutions a lot quicker.

Data analytics tools have a wide variety of features that let you study the given data very thoroughly and catch any hidden or recurring trends using built-in features without needing any effort. These tools are entirely automated and require very little programming support to work. They’re great at excavating the depths of data, going back way into the past.

2. Automated Models

Automation is the future. Businesses don’t have enough time nor the budget to let manual workforces go through tons of data to solve business problems. 

Instead, what they do is hire a data analyst who automates problem-solving processes, and once that’s done, problem-solving becomes completely independent of any human intervention.

The tools can collect, combine, clean, and transform the relevant data all by themselves and finally using it to predict the solutions. Pretty impressive, right? 

However, there might be some complex problems appearing now and then, which cannot be handled by algorithms since they’re completely new and nothing similar has come up before. But a lot of the work is still done using the algorithms, and it’s only once in a blue moon that they face something that rare.

However, there’s one thing to note here; the process of automation by designing complex analytical and ML algorithms might initially be a bit challenging. Many factors need to be kept in mind, and a lot of different scenarios may occur. But once it goes up and running, you’ll be saving a significant amount of manpower as well as resources.

3. Explore Similar Problems

If you’re using a data analytics approach for solving your problems, you will have a lot of data available at your disposal. Most of the data would indirectly help you in the form of similar problems, and you only have to figure out how these problems are related. 

Once you’re there, the process gets a lot smoother because you get references to how such problems were tackled in the past.

Such data is available all over the internet and is automatically extracted by the data analytics tools according to the current problems. People run into difficulties all over the world, and there’s no harm if you follow the guidelines of someone who has gone through a similar situation before.

Even though exploring similar problems is also possible without the help of data analytics, we’re generating a lot of data nowadays, and searching through tons of this data isn’t as easy as you might think. So, using analytical tools is the smart choice since they’re quite fast and will save a lot of your time.

4. Predict Future Problems

While we have already gone through the fact that data analytics tools let you analyze the data available from the past and use it to predict the solutions to the problems you’re facing in the present, it also goes the other way around.

Whenever you use data analytics to solve any present problem, the tools you’re using store the data related to the problem and saves it in the form of variables forever. This way, similar problems faced in the future don’t need to be analyzed again. Instead, you can reuse the previous solutions you have, or the algorithms can predict the solutions for you even if the problems have evolved a bit.

This way, you’re not wasting any time on the problems that are recurring in nature. You jump directly onto the solution whenever you face a situation, and this makes the job quite simple.

5. Faster Data Extraction

New data analytics tools are coming out every day, with each of them getting better and better. When businesses approach solving a problem, most of their time is spent on data acquisition and getting the data ready to be used in solving the problem.

However, with the latest tools, the data extraction is greatly reduced, and everything is done automatically with no human intervention whatsoever. 

Moreover, once the appropriate data is mined and cleaned, there are not many hurdles that remain, and the rest of the processes are done without a lot of delays.

When businesses come across a problem, around 70%-80% is their time is consumed while gathering the relevant data and transforming it into usable forms. So, you can estimate how quick the process could get if the data analytics tools automate all this process.

Even though many of the tools are open-source, if you’re a bigger organization that can spend a bit on paid tools, problem-solving could get even better. The paid tools are literal workhorses, and in addition to generating the data, they could also develop the models to your solutions, unless it’s a very complex one, without needing any support of data analysts.

What problems can data analytics solve? 3 Real-World Examples

Employee Performance Problems 

Imagine a Call Center with over 100 agents

By Analyzing data sets of employee attendance, productivity, and issues that tend to delay in resolution. Through that, preparing refresher training plans, and mentorship plans according to key weak areas identified.

Sales Efficiency Problems 

Imagine a Business that is spread out across multiple cities or regions

By analyzing the number of sales per area, the size of the sales reps’ team, the overall income and disposable income of potential customers, you can come up with interesting insights as to why some areas sell more or less than the others. Through that, prepping a recruitment and training plan or area expansion in order to boost sales could be a good move.

Business Investment Decisions Problems

Imagine an Investor with a portfolio of apps/software)

By analyzing the number of subscribers, sales, the trends in usage, the demographics, you can decide which peace of software has a better Return on Investment over the long term.

Takeaway

Data analytics is a sub-field of data science and plays a major role in problem-solving. It makes it easy for us to extract and gather information from various sources and combine it to use it in our solutions effectively. More the information we can gather, the easier tackling the problem gets for us.

Throughout the article, we’ve seen various reasons why data analytics is very important for problem-solving. 

Many different problems that may seem very complex in the start are made seamless using data analytics, and there are hundreds of analytical tools that can help us solve problems in our everyday lives.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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