For The Love Of Data Analytics: Part 1
What began with storing and organizing data for quick and efficient access has today evolved into a domain for analyzing patterns in data to make informed business decisions.
While most pure blood, not-so-nice magical families would consider house-elves like Dobby and Kreacher as just beings who loitered around the house doing menial chores, they have powers to instantly " Apparate " (to appear magically) to places that even great wizards and witches couldn't. We mean, Dobby could " Apparate " inside Hogwarts, right?
Just like the house-elves in the world of wizardry, the potential of data was not realized by many until the last decade or so. Even when we were generating more than 2.5 quintillion bytes of data (that's 18 zeroes!) every single day, we were not making much use of it.
However, the outlook towards big data changed with the advent of data science. Businesses, researchers, and analysts worldwide realized that data is not just to be stored but can be used to gain precious insights into the past and predict trends for the future.
In this article, let's learn about the rapidly emerging field of Data Analytics and how it is valuable to businesses. (Did you think we'd ramble on about Dobby and the house elves?)
What Is Data Analytics?
Data analytics is not a new concept. We've all analyzed data in some way at some point in our lives. When you build your FIFA dream team based on players' attack and defense ratings or you record a decline in sales and figure out that it's because of a new competitor in the same market segment, you are analyzing data to conclude, gain insights and finally make some decisions.
In simpler words, Data Analytics is the science of making sense of raw datasets to identify trends and common patterns to come to a business decision or prove a hypothesis. For businesses, Data analysis is all about using data to build logical reasoning to make informed business decisions.
While businesses have been analyzing data since forever, the emergence of the field of Data Analytics can be dated back to the 1880s. Here's how the field has evolved since.
Data Analytics Through The Ages
How Is Data Analyzed?
Data analysis is like preparing a "Polyjuice" potion. First, you need to know all the ingredients (including the courage to sneak into the Restricted Section of the Hogwarts library), and only then can you brew it right. Now you'd say that we already know the ingredient for data analysis - data. While you are partly correct, to perform effective Data Analytics you also need to understand and collect data based on certain requirements. For that, you first define your actionable insights on data based on four dimensions:
What is the amount of data you want to analyze?
What are the different types of data you want to include in your analytics? Videos, photos, texts, structured data, spreadsheets – the list could go on.
What is the speed at which you want to analyze this data? Per second, per hour, daily, weekly, monthly or yearly.
Which sources of data will you include in your analysis? This is based on how trustworthy and usable the source data is.
Once you've answered these questions, data mining is based on these requirements. For instance, if you are analyzing customer feedback, you can record the data of 1000 customers, which can be in text (written reviews - unstructured data) and numbers (ratings out of 5 - structured data).
This process is not just applicable for sports teams' analysis; it is the standard procedure for analyzing various types of cloud computing data in real-life uses cases. Talking of types, let's check out the types of Data Analytics.
The Four Types Of Data Analytics
These four types of advanced analytics can be ranked by the complexity involved in the process. The more complex and resource-consuming the analysis, the higher is the value it provides to businesses.
Descriptive Data Analytics
This method analyzes data and describes to you what happened. It is used to make observations and discern progress. For instance, Google Analytics is a descriptive Data Analytics service that reports your social media engagement, website traffic, page views, etc. Visualizations are commonly used for Descriptive Analytics as pie charts, bar charts, or line graphs present data in a more digestible way.
Diagnostic Data Analytics
As the name suggests, Diagnostic Data Analytics helps the Dr. House inside you understand why certain incidents took place. It helps you diagnose the reason behind certain events by analyzing data. For example, diagnostic Data Analytics will tell you that your website traffic dropped suddenly due to a power outage in an area where half your traffic comes from. That's a catchy diagnosis, as Dr. House would say!
Predictive Data Analytics
This type of analysis lets you know something likely to happen in the near future, based on data from the past. No, it won’t tell you if the S&P 500 will go up tomorrow! However, if your store sales peaked during Christmas, Predictive Analysis might inform you that it would probably peak during next Christmas or even Halloween as shopping sentiments are high.
Prescriptive Data Analytics
It tells you the possible action you can take to avoid pitfalls based on analyzing data from the past. While Predictive Analysis would tell you that your sales will spike during Christmas, Prescriptive Analysis would tell you that your website may not be able to handle the traffic, so you’d need to prepare your website and make it reliable for the upcoming peak.
From insightful business analyst decisions to better derive data analytics techniques for customer management, Data Analytics with the help of machine learning can help you build a robust business and better customer relationships through personalized experiences. When you have data skills, you're able to make concrete decisions that are more reliable than opinions and perceived notions of what's right or what'll work. The more data sets we have at our disposal, the broader the scope for the Data analytics program and better decision-making through data visualization.
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