Technologies like AI and IoT have not only made it efficient for businesses to complete various tasks, but also generate and collect data at a scale that the world’s never seen yet.

Experts estimate that over 2.5 quintillion data is created all over the world each day (that’s a million to the 5th power).

With so much data now available, businesses and organizations now struggle in selecting and interpreting the information that’ll help them make well-informed decisions.

This is where big data analysis comes in.

So what exactly is data analysis?

Experts have defined data analysis as a method of gathering, cleaning, interpreting, and converting raw data into useful and easy-to-understand information.

Why is data analysis important?

In today’s technology-driven world, money isn’t the only thing that makes the world go around. Big data analytics now also plays a significant role.

That’s because the ability of these businesses to efficiently organize and use the massive amounts of available data can mean the difference between success and failure.

Additionally, businesses whose activities and data drive decisions are 23x more successful in customer acquisition and sales compared to those that aren’t.

For that to happen, these businesses and organizations need the help of data analysts to organize, interpret, structure, and present these large data sets for them to use.

Only then can they use the data gathered to guide them in making better business decisions, from product development to improving customer service levels.

4 data analysis methods

Data mining

Also known as text data analysis, this data analysis method involves collecting data from various databases.

The raw data are collected with the help of various Business Intelligence tools.

The analyzed data reveals patterns that can help businesses and organizations predict specific outcomes and make their decisions based on these.

Statistical data analysis

Statistical data analysis involves organizing and interpreting data on past events and scenarios.

The analysis derived using this method helps businesses to answer the “what happened” question.

There are two kinds of statistical data analysis commonly used by data analysts.

The first is what’s called descriptive statistical data analysis. Here, the data analyst would analyze, interpret, and present data based on the entire data set.

On the other hand, inferential statistical data analysis is the method where a data analyst would base their interpretation and data modeling on a sample of the entire data set.

Diagnostic data analysis

This data analysis method goes hand-in-hand with statistical data analysis.

That’s because this method helps decision-makers understand why they achieved the statistical data analysis results by identifying behavioral data patterns.

Decision-makers can then take the analysis using these two methods to identify any gaps in their business processes or activities. In order for this method to be as efficient as possible, you might consider aligning your business data from multiple systems and having all of the important information in one place.

This, in turn, will put them in a better situation to come up with solutions to address these gaps.

Predictive analysis

As you may have guessed, the results derived by the data analysis method provides decision-makers insights on what’s most likely going to happen if everything stays the same.

At the same time, this data analysis method can also be used to give businesses and organizations an estimate of what will happen to the results if a variable is altered.

Through this data analysis method, decision-makers can choose which course of action would be the best option to take.

The data analysis process

Step #1: Define your objectives.

Before you start any business process, you need to be clear why you’re going to be analyzing data in the first place.

This step involves asking questions that are clear and concise so that everyone in the team is on the same page.

At the same time, the questions should be quantifiable and measurable. That way, key decision-makers can accurately assess solutions based on the results.

Step #2: Establish your metrics.

There are two things that you need to determine for this step.

The first is to decide which data to measure. This helps data analysts weed out data that are irrelevant to the analysis that they’ll make.

The second is to determine how the data would be measured. Some of the things that considered here are the age of the data you’ll be collecting and what unit of measurement will be used.

Step #3: Collecting the data.

Once the metrics are in place, it’s time to gather the data you’ll be analyzing.

The way how you collect your data will depend on the objectives you’ve established.

For example, if you’re using the data mining method, you’ll need a data mining tool in place to collect the necessary data from the existing database.

On the other hand, if you’re conducting diagnostic data analysis, you may have to create a program using Python or another computer programming language for this.

Step #4: Clean the collected data.

Ideally, all the data you’ll gather are complete and accurate.

Unfortunately, that’s not always the case. Often, data analysts would come across data that are outdated or incomplete.

Since these data would significantly affect the overall results of the analysis that would be done, data analysts would first need to clean these out to make sure that their interpretation is accurate.

Step #5: Analyze the data.

As you may have guessed, this is the step where data analysts would explore, find relevant patterns, and interpret the data collected.

It’s in this step where they’ll use a series of tools and platforms to complete this entire process. These can either be purchased or created by developers working in the organization or company.

Step #6: Present the data visually.

The last step involves interpreting the data into a manner that managers and key decision-makers in the company or organization can understand. This usually takes the form of a graph or chart.

Starting a career as a data analyst

A data analyst may have a lot on their plate every single day. But that hasn’t deterred people from making a shift in their career to become a data analyst.

For starters, there’s a massive demand for data analysts today. Just take a look at the number of data analyst job openings currently listed on

Even some jobs like this Market Research job opening at Apple require candidates to possess data analysis among the required skills it’s looking for in an ideal candidate.

Then there’s the part of the pay.

Because data plays a significant role in many key business decision-making activities, companies and organizations are willing to pay top-dollar to data analysts.

Most job search sites like Glassdoor provide an updated average annual salary you’ll get as a data analyst, depending on your experience level.

The good news is that you don’t need to complete a 4-year IT course to get the qualifications you’ll need to apply as a data analyst (although that’s an advantage).

Many top companies recognize and honor data analytics certifications that were obtained online.

Apart from being more affordable than attending a university, online data analytics certification courses provide you a balance of theoretical knowledge and hands-on experience.

So when you apply for a data analyst job opening, you’re prepared.

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Final thoughts

Data analysis is now quickly becoming a major influencer in daily decisions businesses and organizations make.

That’s because, by itself, data is nothing more than random numbers and names.

Businesses and organizations need the skills of a data analyst to organize and interpret raw data into something meaningful that can be easily understood.

And that demand continues to increase.

So, if you’re looking for a career where you’ll feel valued, indispensable, and paid a handsome salary, then learning data analytics would be the first step to making this a reality.