Introduction to Data Analytics

The principle behind data analytics is simple. It is the process of extracting usable information from any quantity of raw data. The raw data we speak of may take many forms, from customer sales figures to employee payroll and the number of downloads.

Whatever the data, analysis helps SMEs, individuals, and larger businesses find solutions to their problems. Data analysis also helps to identify the successes and failures of new business strategies.

Today, many SaaS providers have designed data analysis software for people without a background in analytics. Such software is known as SSBI or self-service business intelligence.

Here are a few key insights into data analytics:

• Data analytics is the specialized scientific field in which raw data is analyzed to form conclusions.
• The use of data analytics can help businesses increase their profits, efficiency, leadership, and overall performance.
• What once took data engineers many months to complete can now be completed in moments with automated processes and software.
• There are four main approaches to data analysis:
  • Descriptive Analysis – Determining what happened in the past.
  • Diagnostic Analysis – Determining why that thing happened.
  • Predictive Analytics – Determining what is about to happen.
  • Prescriptive Analytics – Determining what needs to be done.
• Data analysis engineers utilize a range of software to aid them in analytics. These include business intelligence platforms.

Debunking the 7 Most Common Data Analytics Myths

We have explained what data analytics is. But what is it not? Here, we will separate fact from fiction by debunking the seven biggest data analytics myths.

Data Analytics is Expensive

Data analytics is a precise science, and as such, it was not something ordinary business owners used to be able to implement. It was confined instead to the realm of academia.

When data analytics first emerged onto the business stage, the only companies which could afford to utilize its many benefits were those large enough to employ a whole wing of graduate data analyzers and engineers.

These days, however, this is no longer the case. Quality data analytics is no longer expensive nor belongs solely to the elite. Costs surrounding the analysis of company data have dropped across three crucial areas to make this service affordable.

1. Data Storage – To analyze data, you need somewhere to store it. Data storage, especially in the Cloud, is becoming more affordable every year. Cloud storage is sometimes free with business accounts linked to the storage provider.
2. Data Analytics Software – Rather than investing in many different applications for data analysis, a single app or program can now do the trick.
3. Data Collection – Collecting data is easier than ever, even if your business has no structure in place for its acquisition. There are apps and software programs designed to analyze data while you work on other things.

You Need a Group of Data Experts for Analysis

This particular data analytics myth is a little more complicated and complex to unpack. Data analytics is – technically – something to which we are naturally inclined. Thus, the question of whether data analysis requires data experts is nuanced.

For example, one could argue that the first proto-humans to conjure fire from flint and kindling were data analysts. They took their raw data (the type, age, and amount of wood, the type of flint, the weather, wind, location, etc.) and analyzed it until they identified the optimal data points for the creation of fire (dry old wood, dry weather, a light breeze, somewhere sheltered from rain).

When it comes to maximizing the efficiency and turnover of a business, however, data analysis becomes much more complex than those first fire-starters could ever have imagined. For example, if your company is considerably large or the issue at hand is particularly complicated, then a team of expert data analysts may be required.

Nevertheless, in most business scenarios you would not need to hire a group of experts. Instead, you can rely on self-service analytics programs (available at affordable prices) to collect and analyze your data.

Data Analytics is Tedious and Time-Consuming

The next big data analytics myth states that data analysis is boring, and requires a lot of time. Indeed, this was once the case.

Before data scientists began producing software and SSBI programs, analyzing data involved sifting through immense piles of raw data looking desperately for patterns.

Today, however, we can thoroughly debunk this myth.

Part of the issue of becoming bored by data lies first and foremost in the overwhelming quantity of it at hand. However, this can be mitigated by employing the proper analytical framework in the first place.

If you need to analyze the impact of new packaging on burger sales in your fast food franchise, for example, you may only need to examine sales figures from the months on either side of the introduction of the new packaging. However, if you try to analyze figures from the burger’s entire lifetime, then yes: you will find the analysis tedious!

Secondly, we can debunk the myth that data analytics is time-consuming. Self-service business intelligence programs and apps allow you to analyze copious quantities of data in just a few simple clicks, provided you know what to look for, thus freeing up your time.

Only Big and Online Companies Benefit from Data Analytics

The myth that only certain companies benefit from data analytics is untrue. Nevertheless, it is easy to see why small, independent, or offline business owners might still believe it.

Companies that have had the greatest successes with data analytics tend to be in tech and online retail (especially giants like Amazon, eBay, Google, and Facebook). Their data analysis of marketing campaigns, ad spending, and consumer habits (though controversial) has spawned billions of dollars in revenue.

We are here to tell you that data analysis is as much for offline and independent companies as it is for mega-corporations.

If you run an offline business, you may not relate to tech companies. Nevertheless, the science of analysis can still play an enormously impactful part in improving your decision-making and the efficiency of your internal structure.

You Need to Acquire Huge Volumes of Data

Have you heard of Big Data? By this point, most of us have, even if most of us still have no idea what the term means!

Big Data refers to any data set too large to be processed (or analyzed) by traditional methods. In our hyper-digital age, Big Data is manipulated for untold profits by companies like Meta. Meta’s ownership of social media platforms Facebook, Instagram, and WhatsApp give them instant access to the user data of billions of people all around the world.

This data is then analyzed with Big Data technology, and the findings can be sold to the highest bidder, even if the bidder has nefarious intentions. We saw clear examples of exactly this type of thing happening with the manipulation of voters by Cambridge Analytica during the 2016 Trump–Clinton Presidential Campaign and the UK’s Brexit referendum.

But does the fact that Big Data exists means that analysis requires huge volumes of data to work? No! Data analysis also works even on micro levels. The question is not one of quantity but quality. To get good-quality data you must first ask good-quality questions about your data sets.

Data Analytics Can Replace Human Manpower

Some people believe that if SSBI data analytics programs have become so intuitive already, and if the world’s data market is expected to double in size by 2027, then AI software might soon replace human manpower.

Certainly, AI (Artificial Intelligence) has improved by leaps and bounds in recent years. Some AI software can operate much faster than humans ever could, processing tens of thousands of data points in mere seconds. Nevertheless, speed isn’t everything.

In addition to the ability to process raw data, quality data analysis also requires human creativity and problem-solving. It requires people – real people – to ask the right questions, and dig for the right insights.

To put it another way: AI data analysis software may turn clay into bricks faster than we can, but it does not have the ingenuity, creativity, or human flair for imagination required to then turn those bricks into an attractive, sturdy, and profitable piece of architecture. That, we must do ourselves.

Data Analytics Could Predict the Future

If you’ve ever watched the hit US TV show Westworld’s third and final season, then you’ll have seen a dystopian sci-fi world in which a Big Data analytics company takes control of everything, right down to the food you eat, the work you do, and the house you sleep in.

How does it do this exactly? By predicting the future with data analytics, and using its predictions to enslave regular people.

Believe it or not, despite the fictional nature of this narrative, the myth about data analytics predicting the future is one that many people still believe. But is it true? Thankfully not.

What is true is that, if you ask the right questions about your data, and if you analyze your data sets in the right way, then data analytics can help you plan for the most probable eventuality.

For example, if data analysis shows that your restaurant’s most popular dish is pepperoni pizza, then you could predict that sales would drop if you took that dish off the menu.

However, does this mean that you’ve predicted the future? No. You’ve just identified the most likely scenario. There is every chance that your current pepperoni customers may just shift to the salami pizza on your menu instead, and sales will remain constant.

Conclusion

Data analysis continues to remain a mystery to many millions of people around the globe, even though their user data is being collected and analyzed by others every second of every day.

What few people realize is that they, too, can implement data analysis to their benefit. Whether they are solo podcasters, identifying which type of content is most popular with their listeners, or C-suite executives solving efficiency issues within their teams, data analytics can help. Don’t believe the myths. Don’t believe the hype. Believe the facts, instead.