Searching for a baseball ball in an old dark attic is nothing like finding a needle in a haywire…different environments and circumstances demands different methods to get the desired outcome. Likewise, today different types of information reside in so many different environments and are stored in so many different formats in a business. Which means that quickly extracting meaning from what you have is becoming almost impossible and this is where analytics comes into play.
So what is analytics?
Analytics is basically the use of data, statistical analysis, explanatory and predictive models to gain insight and act on complex issues. It could provide insights into a variety of scenarios, uncertainties for a business and for this analytics must start with a question or hypothesis. There’s usually a positive correlation between a company’s analytic needs and its size. Big brands operate on a massive scale, so every action becomes magnified. Analytics that could help them foresee the future and take measures to prove extremely valuable in the upper tiers of such companies. On contrary, lower-level managers or smaller brands concerned with day-to-day operations will likely have no use for statistical modeling. As for analytics, there are four types of analytics which could bring meaning to your data when applied, they are,
1. Descriptive (What happened?)
2. Diagnostic (How it happened?)
3. Predictive (What’s gonna happen?)
4. Prescriptive (What could be done?)
Descriptive analytics is literally what its name implies, it organizes raw data from different sources in order to provide insights from the past. Descriptive analytics are useful because they allow us to learn from our past behaviors, and understand how they might influence future outcomes. Let’s say that you’re running a hotel business and need to see what range of customers generated more profits during the holiday season, likewise, a computer retailer would require to know his weekly sales volume.
However, these insights simply signal that something is wrong or right, but does not give tell you why it happened. This is why, business do not limit themselves with descriptive analytics alone, but also prefer combining it with other types of data analytics. Be it a startup or a big brand, descriptive analysis is a common and a very basic type of analysis that could benefit any size of a business regardless their size or which stage of growth they are.
An analyst could just know what happened using the descriptive data but to know why is where diagnostic analytics comes into play. Ok, let’s say you’re running an e-commerce business and you ran few campaigns for the Black Friday sales. You did have a good feedback and sales on few products you advertised but a few didn’t. So to identify why this happened you would get the campaign results and begin analyzing it. The likes, shares, target group you projected the ad too and finally figure out the reason.
Diagnostic analytics takes a deeper look at data to attempt to understand the causes of events and behaviors. For this to be fairly accurate and complete, businesses or analyst should have detailed information available.
This is more like telling you the future but unlike a fortune teller, here your future is predicted using the available data. Predictive Analytics involve analysis of past data patterns and trends to forecast the future business outcomes. It helps in determining realistic goals for the business and its effective execution and moderating expectations, by influencing the findings of Descriptive and Diagnostic Analytics.
Predictive analytics can be used by businesses to forecast customer behavior and purchasing patterns to identifying trends in sales activities. A common application of the use of predictive analytics is credit scores by financial services to determine the probability of customers making future credit payments on time. The predictive analysis may be helpful for businesses in various stages in their cycle, from forecasting their customer behavior, purchasing patterns, sales activity trends. They also help forecast demand for inputs from the supply chain, operations, and inventory.
This is where AI and data come into play. The goal of Prescriptive Analytics is to prescribe what action to take in order to tackle the future problem, in other words, it’s all about providing advice. It is the next step after Predictive Analytics to help business understand the underlying reasons of complications and devise the best of course of action. Prescriptive analytics use a combination of techniques from mathematical models, algorithms, machine learning, and computational modeling procedures with numerous business rules.
Given that they are relatively complex to manage, most businesses do not use them in their day to day course, but if used right they can bring out the significant impact on businesses from a simple operational decision to top management. Big companies are successfully using prescriptive analytics to optimize production, scheduling, and inventory in the supply chain to ensure they are delivering the right products at the right time and optimizing the customer experience.
Being analytical is nothing new, it’s been there for a long time… Philosophers, Emperors, Strategists, and other important decision-makers in the past also have used it and also thought what it would be like to be analytical. What’s changed in the tools that we use to analyze the data have changed with technology. This, in fact, has given us the ability to conduct much complex analytics on bigger data than ever before.
Speaking of tools and technological advancements, our own data analytics tool Zepto is a simple tool that enables anyone to draw better conclusions with their business data. Integrated with AI and Machine Learning, users can have a detailed view as well as deeper insights that would have missed their eyes due to their piles of data.