What Is Data Analytics and Why It Matters?

There were times when people walked for three days to exchange wolf skins for cheese. Today, you order toilet paper on the loo.

Our ancestors spent their evenings watching the fire. Now we watch streaming services like Netflix. 

So much in our lives has been affected by technology. Still, most people don’t think about the massive amounts of data they generate each time they grab their phones.

Тhe importance of that information is not lost on companies. Here’s where the field of data analytics comes in - we’ll focus on what that is in a bit.

Meanwhile, consider this:

• We’ve generated more data in the past two years than in the entire human history.
• Naturally, this has affected the market. Since 2012, the need to manage it created 14 million jobs worldwide! 
• Extracting meaning from collected data could lead to an amazing future. It will help create safe self-driving cars, effective medicines, and improved agriculture.

Data analytics may not have the same ring to it as big data. However, it’s the key to extracting meaning from all the information we gather. More and more industries acknowledge this is the way forward - business leaders need it to decipher the collected data and make informed decisions.

You get it - it’s really important. But how does it work? Let’s take a dive and explore the secrets of data analytics.

What Is Data Analytics

By definition, data analytics is the science of discovering and interpreting meaning in data, as well as putting the gained insights to use. It’s the link between big data and decision making.

Let’s take Bob’s company as an example. He founded his small business - “Bob’s Socks” and opened a nice tiny shop. However, he soon noticed how people were getting frustrated, as it was difficult for them to find what they needed.

Being resourceful, Bob took his business online. He created an online store. To place an order, people were required to create a profile.

So, Bob had already found a way to capture his users’ data. However, being only human, sifting through the hundreds of profiles and orders isn’t an option.

That’s where Bob would need to make use of data analytics. He hires Steve - a data analyst.

By applying computer programming, operations research, data visualization, and statistics, Steve manages to translate the data into plain English. So now Bob knows that, for example, baby-sized cartoon socks are very popular among women in their late twenties.

With the right tools, he can optimize his website. This group will see a variety of colorful baby socks on the front page, helping Bob’s business grow.

While the technology behind analytics is quite complicated, the concept is easy to grasp. The methods of collecting data and processing it vary in different fields. However, the positive results are consistent. So much so, that:

• If Fortune 1000 companies utilize the methods of data analysis, their operation margins will increase with up to 60%!
• Even if they improve data processing only by 10%, they could still earn $65 million in additional income yearly.

Knowing this, it’s easy to imagine that people like Steve are highly sought-after. In fact:

• According to IBM, the demand for data scientists increased by 28% in 2020.
• Back then, the number of analytics professionals doubled, reaching a million people worldwide!
• Their average yearly salary was $115,000.

Not bad at all! However, before you go on Google looking for crash courses on data analytics, let’s go over some additional details.

Data Analytics vs Data Analysis

It’s a common misconception that analytics and analysis are synonyms and can be used interchangeably. Well, they’re not - if you need to know, they’re actually paronyms. 

Steve knows that. In fact, his tough and thorough work has helped the company spread through another niche - though a somewhat different one from what Bob would have thought. In any case, the stocks of “Bob’s Socks and Stockings” are off the charts!

Ba dum tss. 

Now that this is out of the way - what is the difference between data analysis and analytics?

Analysis is used to understand the past. It deals with questions like “How did people react to my latest marketing campaign?” or “ Is my new product successful?”.

Analytics focuses on the reason behind the results. It’s also used to predict how things will turn out in the future.

In other words, companies observe and learn through analysis and develop their strategies using analytics. This is why the former needs more computing power - data analysts are skilled not only in statistics, but also in programming and mathematics. With these tools, they can create comprehensive predictive models.

Data Analytics vs Data Science

This isn’t as tricky as the last one. Now that you know what data analytics is, let’s focus on data science.

Data science is an umbrella term for a group of fields that handle data cleansing, preparation, and analysis. Its main goal is to find a way to capture information and extract meaning from it. 

The main difference between data science and data analytics is the scope. You can think of analytics as to the “narrower” version of data science, as it deals with specific problems that can be solved immediately. Data science looks for insights, but doesn’t bother to explain the reasons behind them. To put it in other words:

Data analytics answers the questions that data science asks. 

And as any of you that have read “The Hitchhiker's Guide to the Galaxy” know, asking the proper questions is as important as knowing the answer. It's 42.

Data Science vs Big Data

This is probably the most intuitive of all the comparisons.

Data sets that are too large to be processed with traditional tools are considered big data. They consist of massive amounts of structured, semi-structured, and unstructured information.

On its own, big data is useless - it’s chaotic and impossible for a human to decipher.

That’s where data science comes in. Using a variety of tools, it mines big data, categorizes and analyses it. It finds patterns within the seemingly-meaningless information and provides companies with invaluable insights.

So if big data is dough, data science is the cook who prepares the pizza for you to feast on.

The Dark Side of Data Analytics

As anything else, data analytics has a negative side. 

It’s quite useful to get a tailored experience, based on your interests online. However, it also means companies will know a lot about you. 

First of all, it’s not impossible for this information to be leaked. And we’d all like to protect our Google search history. 

However, a bigger risk lies in statistical discrimination. For example, data about previous purchases will allow businesses to predict how much you’ll be willing to pay for a service or a product. 

There’s also the matter of intellectual property. If, for example, you use an app to write down your ideas, in theory, the developers may be able to profit from them. Although this isn’t likely to happen, the law is unclear about who owns this content.

Other examples of data analytics risks include government espionage and creating thorough profiles of citizens. 

In any case, the progress of data analytics isn’t slowing down, so we might as well make the best of it!

Applications

While data analytics is pretty much a necessity for companies and businesses, it wasn’t always as efficient as it is today. Technological advances have allowed us to make better predictions than ever before.

As our ability to draw conclusions from data grows, information is becoming more and more valuable. However, the concept isn’t really new, as you’ll soon find out!

History of Data Analytics

When did humankind discover the benefits of data? While naturally, the discipline was far from what we see today, its roots are more ancient than you may think! 

• 5000 BC -  farmers in ancient Mesopotamia began keeping records about herds and crops.
• 1663 - John Graunt aimed to understand the bubonic plague and create a warning system by recording deaths in London. 
• 1887 - Herman Hollerith built a machine that organized census data by reading punch cards.
• 1937 - After the Social Security Act was passed, IBM came up with a system to manage the information of 29 million people.
• 1943 - The British created the first data-processing machine, Colossus, to find patterns and decipher Nazi codes.
• 1965 - The US government built the first data center to store citizen records.
• 1989 - Tim Berners-Lee invented the World Wide Web. 
• 1995 - The world’s first supercomputer was built.
• 1997 - Deep Blue, a chess-playing computer developed by IBM, defeated the world champion Garry Kasparov.
• 1998 - AT&T Bell Laboratories designed a digit recognition system to detect handwritten postcodes for the US Postal Service.
• 2005 - Roger Mougalas came up with the term Big Data. This is also the year when Hadoop was created. To this day, it remains one of the most popular tools in data analytics and big data management.
• 2009 - The Indian government built the biggest biometric database in the world - it stores the iris scans, fingerprints and photographs of 1.2 billion people. 

We’ve come a long way in our quest to conquer data, haven’t we! So how exactly does this help us today?

What exactly is data analytics’ role in business?

Modern Applications

Now that we know what data analytics is and how it originated, let’s focus on how it’s used today.

Effective Digital Marketing

Marketing crochet hooks to 10-year olds isn’t really lucrative (unless you’re dealing with kids like this genius). Spending your precious ad budget on people that are not interested in your products at all, is a waste.

This is why companies rely on data analytics to create and develop a relationship with their clients. Tools like customer relationship managers (CRM) facilitate that process.

Effectively using information can also help businesses make crucial decisions - set marketing budgets, create a successful campaign, and build their overall strategy.

Human Resources Management

Although by definition, data analytics is more oriented towards technology rather than people, it has its application in HR.

Long gone are the times when employers had to rely on intuition to find the perfect people for a job. Now, they can decide which people to hire, promote or reward by consulting with the information they’ve collected or simply by running a background check. 

HR Analytics focuses on analyzing behavioral data to select the employee who would do best at certain position. It’s becoming an increasingly important tool in the industry.

You can take advantage of different human capital management (HCM) solutions including HR & payroll software.

Portfolio Evaluations

Banks and lending companies also invest in data analytics and develop systems to determine if they should give out a loan or not.

By creating a portfolio for each client, they can make sure to get the best value with minimum risk. Naturally, the ideal candidate would be a wealthy person. However, it’s mostly poor people that need a loan. This is where data analytics can come in handy to find the balance and determine the interest rate.

Digital Analytics

Data analytics is a big part of online business. It allows companies to create reports, optimize processes and research the market.

Examining the way potential customers interact with your website can help you understand what it lacks. In fact, data analytics is a big part of what makes SEO efficient. By monitoring keywords, the frequency and context they’re used in, companies can learn how to adapt their content.

Security

Collecting information about security events is crucial for managing them. It helps businesses identify the biggest threats and find effective ways of resolving them. Data analytics allows companies to use data from a variety of sources. Specially designed software sifts through the information, finding correlations between the events so that they can be prevented.

We’ve gone through some of the most popular applications, so now let’s focus on the industries!

Big Industries and Data Analytics

Healthcare

It’s not a secret that the healthcare system has room for improvement. Recent studies indicate that the key to this may lie in, you guessed it - data analytics!  

If the processes get more effective by just 1%, this could save more than $63 billion globally. That’s an impressive amount, all right. But how exactly could the analytics technology achieve this?

If hospitals make use of all the information they have, we can expect to see shorter queues, better management of resources, and even more effective treatments.

However, 56% of hospitals have yet to develop a strategy to utilize the data analytics technology. Of course, there’s a reason why healthcare is falling behind other industries in this regard.

The biggest barrier is the nature of the data. It’s difficult to convert it and it’s usually very sensitive. Decisions also have higher payoff (if any at all) if they’re made in a timely matter. 

Another problem is that entering so much information about every patient is very time-consuming. Physicians have reacted negatively to attempts to introduce data analytics in this field, as it’s a burden to manage. 

However, with so much to gain from it, data analytics tools are evolving to fit in this industry.

For example, Stanford Children's Health and Lucile Packard Children's Hospital Stanford have already started three innovative programs:

• Prevention of AKI (acute kidney injury) in children. The system identifies kids that need to take big doses of nephrotoxic medications. This way physicians can closely monitor them to prevent kidney damage. So far, there has been a 39% decrease in nephrotoxin exposure rates.
• Medication administration system. A barcode system was developed to improve the distribution of medicine. There’s been a 21% reduction of missed doses and a 66% decrease in wasted medication.  
• Better care for patients with congenital heart disease. By collecting information in electronic health records, clinicians can compare similar cases. This allows them to standardize and improve the treatment. It has led to a 34% reduction of hospital stay, which amounts to almost 300 days per year.

Travel

Data analytics can be very beneficial for the travel industry. Predicting what a specific customer would like and generating offers based on that can result in a higher conversion rate and revenue. 

That’s why more and more companies create loyalty programs. This way they can gather valuable information about their customers and tailor an experience specifically for them. Of course, there often are discounts for loyal customers. However, in the long run, customer retention is well worth it for the company.

Sentiment analysis is another data method that’s growing in importance. Most travelers rely on reviews when choosing their lodgings. That’s why knowing how people feel about a specific hostel or hotel is important. Travel agencies often choose the companies they work with based on the feedback from their customers.Recommendation engines are also rising in popularity. By analyzing the criteria entered by the customer, they can come up with various suggestions. These include alternative travel dates, new destinations or attractions and faster or more scenic routes. Not only does this guarantee higher revenue for the company, but also a more enjoyable experience for the travelers.Naturally, all of this is made possible by analytics systems. So we can expect to see more and more data tools, designed to make our vacations better.

Gaming

Considering the area of competence of game developers, it comes as no surprise that the use of data analytics quickly became evident in this industry.

For the last year alone, gaming companies generated a total of $135 billion - a number steadily growing. The introduction of mobile games helped companies reach an even bigger audience. 

This growth has caused a flood of data. By managing it, gaming companies can not only increase their advertising revenue, but also improve the user experience. Now developers can better understand what gamers like and need. Naturally, this draws more players, causing even greater data flow and additional optimizations.

The gaming industry is one of the best examples of how much can be achieved with data analytics. Considering there are more than 2.3 billion active players in the world (yes, that’s 30% of the entire human race), we can expect to see even more innovations and improvements from that sector. 

Let’s take a look exactly what aspects of gaming are influenced by data analytics:

• Development - creators can design games based on what users prefer. That includes not only the mechanics, but also the whole game concept. 
• Insights - developers can predict the bottlenecks and find ways to manage them.
• Monetization - companies can predict how their product will be best received by the audience. Should there be a premium version that removes ads or will the customers prefer to purchase the game? 
• Graphics - DA and AI are to thank for the advances in graphics. Image recognition and object detection software make realistic, fluent motions and seamless changes of scenes possible.
• Marketing - targeted ads with meaningful messages are the key to attracting players. Finding the proper audience also ensures high activity on the platform, which is crucial for MMO games.
• Infrastructure - if there’s a surge of users in rush hours, server-based games may have difficulties coping. That’s why companies use big data analytics to discover where the weak points are and to come up with solutions.

Energy Management

Considering that data analytics seems to be linked to the internet and modern technologies by definition, you may be surprised by its effectiveness in huge industries like energy.

Actually, it has opened the doors to energy optimization and distribution, as well as smart-grid management. It has also enabled the automation of certain processes. Data analytics allows specialists to monitor devices and manage the network.

This allows dispatching crews to ensure any problems are fixed in a timely matter. It also helps determine the causes for certain issues and prevent them.

So there you go, it’s very likely that any business you imagine can benefit from data analytics in one way or another!

Types of Data Analytics

The process of data analytics can be summed up in four steps. They answer the following questions:

Descriptive analytics: What happened?

Diagnostic analytics: Why did it happen?

Predictive analytics: What could happen in the future?

Prescriptive analytics: How should we respond to those possible future events?

It looks so easy, doesn’t it? While the math behind each answer is not for the faint-hearted, the logic of the whole process is quite intuitive. 

There’s no point in boring you with the technical details, but we can take a deeper look at the definition for each type of data analytics. 

Descriptive

Descriptive analytics is the preparatory step of data processing. It extracts useful information from the collected big data and prepares it for the next steps. In this stage, the data is organized and trends, relationships and patterns become visible.

To put it in simpler words - what happened?

Remember Bob? Let’s once again take his company as an example.

“Bob’s Socks and Stockings” has experienced an increase in sales. Naturally, the faithful sidekick Steve has already crunched the numbers. 

The results from the descriptive analytics show that the company has sold vast amounts of lady stockings. The lacy, unpractical kind.

Time for Steve to employ his programming skills and dive deeper into the data analytics.

Diagnostic 

So why did this happen? What caused this surge of interest in lacy stockings?

That’s where diagnostic analytics comes in. It takes the wrangled data, adds other possibly relevant inputs, and looks for correlations. The techniques it uses include data mining, drill-down, correlations, and data discovery.

Was it the rising temperature outside that affects sales? Or the marketing campaign about baby socks?

In this case, Steve finds that the most likely reason is the time of the year. After all, it’s the week before Valentine’s day.

Predictive 

It’s predictive analytics time! Based on the information of the two previous steps, certain trends have become visible. This leads to conclusions as to how these tendencies will develop. 

Of course, the accuracy of the conclusions vastly depends on the quality of the data. The process also needs to be optimized constantly to ensure the best results.

Naturally, you don’t need to go through the data analytics wiki to figure out that the results are not 100% accurate. After all, they’re just an estimate. However, there’s still a lot to be gained!

So, what will happen to “Bob’s Socks and Stockings” in the future?

Steve can infer that offering a variety of holiday-themed socks can increase sales. However, it’s up to Bob to use this information. That’s where prescriptive analytics comes in.

Prescriptive

Prescriptive analytics uses the processed data from the previous steps to suggest a course of action. However, this is trickier than you might imagine.

To take full advantage of this type, you need to provide the system with thorough data - both historical and external. It then uses advanced tools like business rules and algorithms and even machine learning. Therefore, it’s hard and expensive to implement and manage. 

However, it holds the potential to take care of future issues and utilize trends. That’s why there are multiple examples of companies investing in this type of data analytics and getting great results. 

For instance, airlines use it to adjust ticket prices and hospitals - to improve care and decrease the number of patients returning for additional help.

But let’s get back to the really important services - socks distribution.

Let’s say that Bob has decided to splurge on data analytics and Steve actually has the required skill set.

He feeds the data into the program and finds how they can maximize sales. Before Valentine's day, they need to stock up on sexy lingerie. Of course, socks with hearts and babies armed with bows are still in the theme of the holiday. However, the program is certain these products will not be especially successful. 

Steve also concludes that dedicating more space for lacy stockings on the front page of their website will be beneficial. To accommodate the needs of their customers, Bob’s company will also provide expedited shipping the last days before the holiday.

To Sum Up

Businesses can pick between a variety of data analytics approaches. It depends on the results they need and the effort and resources they’re willing to put in. 

Descriptive and diagnostic analytics are considered reactive approach. They offer a deeper understanding into what has already happened. On the other hand, predictive and prescriptive analytics are proactive. They allow companies to alter their procedures to resolve issues and achieve goals.

However, one thing is certain. Data analytics is constantly evolving and yielding better results.

Methods of Data Analysis

Now that we’ve gone through what data analytics’ role is in business, let’s look into how it actually works. 

Methods of Data Collection 

It’s only logical that before you can analyze a certain volume of data, you need to have it available. That’s why the first step of the process is data collection.

This can be divided into two categories - secondary and primary methods of data collection. They allow the system to test hypothesis and come up with the potential outcomes. 

Let’s look into both. 

Secondary Data Collection Methods

In this method, analytics systems use data that has already been organized and published in journals, books or others. Naturally, that’s a lot of information, so setting the correct criteria is crucial. 

To improve the overall validity of the analysis, factors like reliability of the source, depth of the analysis and date of the publication should be taken into account.

Primary Data Collection Methods

There are two types of primary data collection methods:

• Quantitative data collection. This is the cheaper and faster way to collect information for data analysis. It uses mathematical methods to process data. These include correlation and regression, mean, mode and other math terms that you’ve done your best to forget. Questionnaires with closed-ended questions are typical for this type of data collection.
• Qualitative research. You’ll like this one better - there’s no math. It uses unquantifiable data like words, colours, emotions, etc. The goal is a deeper understanding of the problem. Researchers collect the data by various methods like interviews, focus groups, observation, questionnaires with open-ended questions and others.

Methods of analysis

Depending on the type of data, the methods of analysis will also be different. Let’s check out how the information will be processed based on the manner of its collection. 

Quantitative

As we explained above, this data can be analyzed with the help of mathematical methods. It either is or can be transformed to numbers.

The goal is to find evidence to either confirm or dismiss a theory, formed before the experiment. Data analysts monitors variables - how often one is used or the differences between them. 

It’s important to keep in mind that results can be interpreted in more than one way. That’s why it’s vital to use critical thinking before coming to conclusions.

One approach is to compare the data to other findings in the same field of research. Additionally, reaching a conclusion is only half the work - it’s equally as important to explain what lead to the results. 

Although critical and analytical thinking are vital for the process, there’s software designed to make things easier. While there’s no programming involved in this method of data analytics, there are steps to follow in order to get accurate results:

1. Prepare the data and make sure it’s correct before entering it in the program.
2. Choose appropriate tables and diagrams.
3. Pick the stats that best describe the data.
4. Select appropriate statistics to compare and examine relationships and trends.

While this may seem simple, it’s actually very challenging to choose the correct data. Thing is, even if you enter the wrong information, the statistics and diagrams will still look nice.

Qualitative

Qualitative data, as mentioned previously, can be words, colors, etc. That makes it much harder to work with. Let’s take a look at what the process of qualitative data analysis is.

1. Coding - this is how we can translate the raw information to the computer. There are three types of coding that can be used in analysis:

• • Open - the initial organization of data into groups.
  • Axial - the connection between the categories of codes. 
  • Selective - creating the story through connecting selected categories.

Some popular software products that could help are NVivo, ATLAS.ti 6, HyperRESEARCH 2.8 and Max QDA.

2. Discovering patterns and relationships - because of the nature of the data, there are no universal techniques that can be applied. Researchers need to use their analytical thinking skills to come up with results.Here are some examples of methods that could be used to decipher the data:

• • Looking for word and phrase repetitions.
  • Comparison between the collected data and information from other similar studies and interviews.
  • Searching for information that should have been mentioned, but wasn’t.
  • Comparison with findings from other areas and discussion of similarities and differences. 

3. Summary - the findings of the analysis are laid out in a logical manner, confirming or disproving the hypothesis. Any relevant quotations or links to other studies can be mentioned in the final report.

Conclusion

After all that we’ve learned about data analytics, its importance is more obvious than ever. Even if we’re still wading in the shallows of the ocean that is big data, the results can’t be ignored. Data science will continue to grow and evolve and we can only guess to what revelations it will lead.

And if you’re wondering, Bob’s company is doing fine. In fact, it will soon be known as “Bob and Son’s Socks and Stockings”.

Now that we’ve cleared up exactly what data analytics is, you can see why it’s sought out by such a wide variety of businesses. So consider investing in it for your own company or start learning about it. Chances are that you’ll benefit from it!