If data is the new oil, then data analytics is the oil refinery that the digital world is clambering after. And it’s no surprise either, considering the amount of data users generate in a single day. From Instagram pictures and YouTube videos to Facebook Ads and Snapchat snaps, data today is limitless.
With all this volume, the need for sophisticated analytics is more significant than ever. It’s no problem collecting and storing data nowadays. Instead, the real challenge lies in dissecting the data to find actionable insights. Back at the start of the 21st century, a human analyst was enough to accomplish this task. But today, there’s entirely too much data for one analyst, or even a team of analysts, to analyze.
That’s why data analytics is now a game of AI-based systems, like Google Analytics. But how does artificial intelligence go about analyzing data? Why is it so much better than human-based data analysis? And what challenges do businesses face when conducting data analysis in the 21st century? Answering these questions is the first step to formulating a sound data analytics strategy in the digital age.
But first, let’s get acquainted with the concept itself.
The Hype Around Data Analytics
So what is data analytics?
In layman terms, it’s data analysis to find a pattern that can govern business strategies. Thus, the process involves collecting, assimilating, inspecting, and structuring data to find insights. The true essence of data analytics lies in volume, which is why Big Data is so essential. But we’ll talk more about that later on. For now, let’s look at a practical usage of data analytics through an example.
Suppose you own a sports goods store and regularly run online marketing campaigns. You’ve collected data showing the interaction of users with your campaign. But revenues aren’t increasing as they should, and you’re suffering losses. So you turn to data analytics.
The data shows that men aged 25 to 40 are the ones that view marketing campaigns most avidly. Among them, the ones that consider the campaigns more than once visit your store’s website. Even though the flow of traffic to the website is healthy, data shows purchases are decreasing. That’s because customers exit the website at the payment confirmation screen.
Now you have two insights to work with, thanks to data analysis. The first focuses on men between the ages of 25 and 40 as the primary target audience. It would be doubly helpful to design campaigns that encourage repeat exposure since viewers who viewed campaigns more than once ended up on the store website.
Secondly, the majority of exits from the site are at the payment confirmation screen. That means the products’ price is too high, or (probably) customers don’t prefer the payment method. So if you add more payment methods, more customers would complete transactions. Thus, data analytics helps identify the problems and improve performance for businesses.
But there is a catch!
The Role of Big Data
Data analytics is a powerful tool, but it requires a large amount of data to be effective. Consider the above example. The insight to target men between 25 and 40 is only useful if it originates from substantial data. So, the more amount of data the analysis bases on, the better the insights. That’s why big data is so crucial to the implementation of data analytics.
Suppose the total number of users in the above example was 100. Could the data analysis for 100 users be representative of the entire segment targeted? Of course not! That’s why businesses strive to collect as much data as possible to conduct big data analysis.
Artificial intelligence-based analysis works best if it accounts for more data. That’s because AI systems conduct data analysis in the forms of variables set by human agents. The more variables a business creates, the more thoroughly it can implement data analysis.
Thus, the ability to accumulate significant data is essential in the overall competitive landscape for modern businesses. But merely having big data is not enough to guarantee proper data analytics implementation. A data analytics strategy that integrates AI and data availability must complement big data to make it useful.
Application & Implication of Data Analytics
The advent of data analytics is bringing about a change like never before. Even though businesses analyzed data even before the internet was invented, yet data analysis is something more. It’s reported that the last two years account for 90% of all data produced in humankind’s history! That’s insane!
And with so much data on our hands, the emergence of data analytics was inevitable. The implications of such a technique are profound. Thanks to analyzing terabytes of data instantly, companies like Amazon, Apple, and Microsoft are breaking the trillion-dollar barrier for the first time in history. Companies now understand their consumers better than ever before because of detailed personas built on the back of data analysis.
But how many companies are even using data analytics? Well, according to a report published by Forbes in 2017, a whopping 53% of companies use big data analytics. That is over 30% more than the companies that relied on data analytics in 2016! And with so many firms relying on advanced analytics, the competition to innovate and excel is ever-expanding. That’s why we are seeing smarter, better, and more efficient products and services that satisfy consumer needs like never before.
The implications of data analysis for the future are even more exciting. The more familiar companies become with clients and their needs, the more customized services will become. And as users spend more time online, the more data they generate for companies to analyze. The result is an ever-growing network of analytics that allows businesses to understand and cater to consumer needs like never before.
Formulating Data Analytics Strategy with AI
So how does one go about formulating a data analytics strategy for their business?
The first step is to understand what a strategy entails. According to Michael Porter, a strategy is deciding “what not to do,” which is incredibly relevant for data analytics. Since there’s no shortage of data, companies need to prioritize the data for data analysis. That means knowing what to leave out, and as simple as that sounds, it isn’t. Many firms spend precious time and resources analyzing data that is ultimately irrelevant to their business objectives.
So how do you decide what data is relevant and worth analyzing?
The answer: Artificial Intelligence.
Of course, you’ll need to establish business goals yourself, depending on your firm’s core competencies. But AI systems can’t help you with that part (yet). That’s because humans have the judgment to decide which objectives are relevant, actionable, and achievable. But AI systems can help with what comes next.
By utilizing machine learning to identify relevant KPIs that contribute positively to the end goals, AI categorizes useful data. With access to big data, the analysis becomes increasingly accurate based on large quantities of data. Thus, businesses can gain actionable insights from a data analytics strategy that is well-tailored to their needs.
Modern Day Challenges to Data Analytics
Finally, we come to the challenges associated with data analysis. Chief among these challenges is the fact that data analysis is increasingly complex to carry out. Not to mention, it requires sophisticated systems and trained personnel to generate and interpret results. All of that means additional costs for the firm and more time spent training than carrying out data analytics.
Yet, the most significant challenge data analysis presents to modern-day firms is its nuance. As mentioned earlier, data analytics is simply the analysis of large data sets. Those data sets have to be large enough for the AI system to generate meaningful insights. But even more irritating is that data analytics identifies patterns in past data without predicting the future.
There’s a whole other branch known as predictive analytics to predict future trends. Predictive analytics is concerned with providing probabilistic outcomes depending on existing data. Although it is a branch of data analysis, predictive analytics requires separate software infrastructure. This issue is worrying since predicting future trends is essential to modern-day business success.
A more overlooked challenge is the absence of a proper data analytics strategy. As users generate more data, the primary concern of businesses is to analyze it. But the key to success lies in identifying what needs to be analyzed and what doesn’t. That’s because not all data is relevant to all companies. Many companies spend precious time, money, and resources to analyze data that ultimately yields no value.
If businesses are to make the most of data analytics capabilities, they need to set a strategy. The infrastructure is readily available, but resources are limited. As such, knowing how to approach data analytics can be the difference between business success and failure.