TechDogs-"For The Love Of Data Analytics: Part 1"

Data Management

For The Love Of Data Analytics: Part 1

By TechDogs

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Businesses generate a lot of data, don't they? Tons and tons of it, every minute, every second, so much so that even Baa Baa Black Sheep's three bags full are not enough! Now, businesses have the opportunity to make sense of this raw data to draw meaningful conclusions, which can help them gain a competitive advantage in their field. This process of collecting, analyzing and interpreting raw data is called Data Analytics. 

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. 
Read on to find out all about Data Analytics, its history, types and numerous benefits.
TechDogs-Analyze. Adapt. Overcome-"For The Love Of Data Analytics: Part 1"

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

Did you know that it took the United States government seven years and an elaborate team to prepare the census report back in 1880? Imagine their horror when they realized that they'd have to go over the entire process again merely three years later! However, inventor Herman Hollerith decided to save their misery by building the "tabulating machine" for the 1890 census. The machine systematically processed data recorded on punch cards and the 1890 census was finished in 18 months! This is probably one of the earliest examples of automating data collection and processing to gain new insights. 
Data Analytics relied on manual data recording and collation as technological progress was slow. Before we had flashy hard drives and cloud storage, data was stored on physical hardware and could only be handled by a specialized machine. Businesses rarely stored non-essential data, let alone analyze it! However, when IBM invented the hard disk in 1956 (which held a measly 5MB of data), Data Analytics became more feasible for enterprises. Yet, Data Analytics as a business function took off only in the 1970s, when Mr. Edgar F. Codd invented relational databases for computers to store data in Sequel (SQL), which could be retrieved and analyzed swiftly. 
Then came the Internet, intelligent processors, and the age of computers; relational databases could no longer handle the amount of data the world generated. This led to the birth of Data Warehouses, proposed by William H. Inmon. Imagine a mall of data - various shops and stores, each with individual data segments. If you want to analyze specific data, you walk into the store with that data segment instead of analyzing the whole data - talk about efficiency! Today, there's no data mall; instead, information is stored on the cloud without a physical setup. (Yes, much like online shopping replaced physical malls!) This means we can analyze data online without downloading or uploading data, which has boosted Data Analysis rates. 
We've progressed in recording data but there's been a parallel evolution in how we use this data as well. Earlier, we recorded data to know that there was a drop in website traffic by 30% or an increase in quarterly sales by 10%. However, Data Analyst now helps us understand the underlying factors for these changes and help predict them better in the future. 
So, how exactly does it happen? 

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: 
  • Volume

    What is the amount of data you want to analyze?

  • Types

    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.

  • Speed

    What is the speed at which you want to analyze this data? Per second, per hour, daily, weekly, monthly or yearly.

  • Reliability

    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). 
Post collection of data, you organize and clean it, analyze patterns, and record observations. Finally, using these observations, you make interpretations and decisions. For instance, football clubs record data such as each player's shot accuracy, the distance covered, tackles made, etc., statistical analysis for each match. After analyzing the data driven decisions for competitive advantage, managers have a foolproof metric insight to compare players and decide which players to sign for the next season. 

TechDogs-How Is "Data Analyzed?"-Image Displaying Standard Procedure For Data Analysis
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. 

TechDogs-The Four Types Of Data Analytics-Image Showing A Graph Of Data Analytics That Can Be Ranked By The Complexity Involved In The Process -  Descriptive, Diagnostic, Predictive, Prescriptive
  • 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. 
There's no denying that data science programs are and will remain tremendously crucial for predictive modeling businesses worldwide. While we've covered the coursework basics of Data Analytics course, jump to part 2 of our Data Analytics series, where you will find out how Data Analytics can enhance your business, the forces driving it, and its future.

Frequently Asked Questions

What is Data Analytics?

Data analytics is the process of examining raw data to uncover patterns, trends, and insights that can inform business decisions or support hypotheses. Whether it's analyzing player statistics to build a FIFA dream team or investigating sales declines due to market competition, data analytics involves extracting meaningful information from datasets to make informed choices. Essentially, it's about using data to build logical reasoning for decision-making within businesses.

How is Data Analyzed?

Data analysis is akin to preparing a complex potion, like the Polyjuice potion in the wizarding world. To perform effective data analytics, you need to understand and collect data based on specific requirements. These requirements typically revolve around four dimensions: volume, types, speed, and reliability of data. Once you've defined these parameters, data mining begins, where data is organized, cleaned, analyzed for patterns, and observations are recorded. Finally, based on these observations, interpretations are made to drive decisions. This process is fundamental not only for sports teams analyzing player performance but also for various real-life applications, such as cloud computing data analysis.

What are the four types of Data Analytics, and how do they differ?

There are four types of Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive. Descriptive Analytics focuses on analyzing data to describe what happened, often presented through visualizations like charts. Diagnostic Analytics helps understand the reasons behind incidents, offering insights into why certain events occurred. Predictive Analytics forecasts likely future events based on past data, while Prescriptive Analytics suggests actions to avoid pitfalls based on past data analysis. The complexity of analysis increases from descriptive to prescriptive, providing businesses with valuable insights for decision-making.

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