If there is a buzzword that confounds and intrigues, it’s ‘Data’. Everyone knows just enough about it for every discussion to quickly become a case of ‘too many cooks.’ But data analytics is not a flash in the pan or a fad technique. While we weren’t paying attention, it stealthily shaped the new world order, from election trends to self-driving cars, and has a hand in everything, from Alexa to targeted personalized ads that follow you all around the internet. Despite its omnipresence, do we know how analytics works?
In a nutshell, data analysis encompasses a wide variety of techniques used to reveal patterns and trends in the data and wield them to draw actionable insights. Before the big data boom of the century, organizations relied on industry experts who leveraged decades of experience to find patterns in customer behavior, market fluctuations, and its ensuing impact on business. But human intelligence sometimes falls short, and even the sharpest of intuition can be surpassed as times change and customer preferences morph minutely but steadily with time. Analytics uses data and fills the gaps in the narrative formed by human intelligence, and its dependability has established it as the most sought-after skill in today’s market.
Big data stats indicate that more than 30% of data will be uploaded to the cloud by next year which can improve a business’s agility by 29%
Early data science was rooted in statistics but naturally branched out as it focused beyond the crunching of numbers and used advanced statistical techniques instead to answer hypotheses that would provide directional insights. As early as 1994, market forces were abuzz with database marketing, with a keen eye set on humongous amounts of data, although back then, companies were crippled by computational limitations to utilize it effectively. By the early 2000s, computing techniques to work data were well on their way to discovery, and experts suggested christening this newly established domain ‘Data Science’. Since then, the relentless expansion of both technology and technique under the umbra of data science has positioned it as one of the most coveted approaches for problem-solving in an organization.
Surprisingly, 99.5% of collected data never gets used or analyzed!
Data used by corporations is sourced from both public domains (geo data, zip codes, weather and temperature data, population census, etc.) and is collected by the organization itself or a third-party vendor collecting crucial information such as retail data, customer details, purchase timings, foot traffic, etc. When embarking on solving a problem, like low sales or frequent stock-outs, there are certain hypotheses that form the questions, and their answers are often hidden in the data. One chooses factors (sales information, date, number of items, etc.) from the data that will help answer the question but bear in mind that sometimes the unlikeliest of factors can influence the witnessed outcome. After analyzing the factors for their weightage in the outcome as well as their relevance, a mathematical model appropriate for the problem at hand is designed, which is essentially an equation where the behavior of different factors (X1, X2, X3, and so on) is measured on the outcome, Y. Other times, the probability of Y occurring or not occurring is ascertained from the performance of the factors. It goes without saying that the larger the amount of data available for the relevant factors and how far back it goes historically, plays a critical role in the accuracy of the model and makes the result more bullet-proof.
Companies that harness big data’s full power could increase their operating margins by up to 60%!
The broadest takeaway from the process described above is identifying, assessing, and solving business problems in real-time. It is, however, far more fascinating than that, the intricacies of which are best explained with some examples:
Predictive Analysis:
Whether you want to know who will win the highly contentious match between Manchester United and Arsenal, or something as crucial as how much customer traffic you can expect this New Year based on the last few years, predictive analysis comes to your rescue.
Detective Analysis:
From fraud analytics catching instances of malpractice even in the most chaotic banks to detecting which bookings are being made by bots versus actual human customers for concerts, this technique can segregate the anomalous from the anticipated.
Prescriptive Analysis:
What visibility should a brand aim for on social media to hike up sales by 20%? What kind of margins must a multi-outlet chain maintain throughout the year to give massive deals in the week before Christmas? Prescriptive Analytics focuses on crucial questions that can create an executable plan with clearly defined steps.
Descriptive Analytics:
This branch of analytics can aid in getting a complete picture between two choices, such as the premium health insurance plan between several options or simply ascertain the impact of an HR policy like inter colleague feedback on the morale of employees over a period of time.
In all these analytics techniques, there is considerable scope to account for ‘Black Swan’ events, such as the Covid crisis, which is what makes them more dependable.
79% of executives believe that failing to embrace big data will lead to bankruptcy. This explains why 83% of companies invest in big data projects
Analytics is already in motion, with the present moment witness to the data revolution. The present we occupy is more astonishing than the future envisaged by the minds of the past. For instance, a slew of tools has emerged to measure engagement and other such metrics on Instagram, an app whose existence would befuddle a time-traveler from merely a decade ago. Instagram insights reveal a clearer picture about the demographic of one’s followers and their most applauded content so that you can focus on these pointers and grow your tribe.
Similarly, YouTube’s ‘recommended videos’ are so faithful to our taste that it has caused a widespread binge epidemic with its own moniker – the ‘YouTube spiral’. The recommendation engine learns a user’s preference based on the data of their previous viewings and keeps up a steady supply of related content, designed to drum up YouTube viewership and consequent advertising engagement, which is their means to earn profits.
On Google alone search queries to 1.2 trillion searches yearly!
While multinational corporations seem to have analytics firmly pegged as an integral part of their toolkit, there’s no reason why it cannot be a trusty aide for small businesses as well. Every checkpoint of customer interaction with your brand or service generates data, whether online or in-store, collected automatically or manually. With manageable amounts of data and a bevy of easy-to-use data analysis tools that have cropped up, it’s less about knowing the technique in-depth and more about understanding the concise analytical efforts that could put your business into high gear. That requires more business acumen and less analytical prowess, although it would be potentially beneficial for small businesses to set up in-house analytics efforts, for this technology is only set to burgeon in scale and accuracy in the upcoming future. Leveraging analytics could have a wide-ranging impact, from increased customer retention to better-performing marketing campaigns.
More data was generated in the last two years than in the entire human history before that
Human surveillance may sound like a mere conspiracy theory, but to varying degrees, world inhabitants are concerned about personal data collection and distribution without permission and its use by corporations without persecution. At the end of the day, a human’s quest for reasonable privacy against a corporation’s will to convert them into a customer is an unequal one; the human is simply not expert enough to know the forces they are up against, something that is discussed in the Netflix documentary ‘The Social Dilemma’. When it comes to how stealthy the impact of analytics is on human preference and behavior, precedence should have been set for transparency. Still, we’re past the foundation and rising fast, and all towers of Babel eventually come crashing down. The world might create a problem it does not have a solution for, if the illegal collection and sale of data and its acquisition by nefarious parties continues unchecked. After all, with great power comes great responsibility.
Smart devices (for example, fitness trackers, sensors, Amazon Echo) produce 5 quintillion bytes of data daily
As humans, retaining and processing information defines us. So, it is understandable that data science, a discipline that not only mimics our brain’s core function but scales it up, with fewer errors, would inspire confidence in us. It is fast becoming irrelevant how we did business before data. Analytics is climbing up the ladder; utilizing it within the bounds of ethics and fair use can make every business perceptive about the future in store for them. One thing is certain, with the way data science is rapidly replacing the backbone of existing systems while simultaneously providing the building blocks of new ones, it’s here to stay.
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