Data Analytics refers to the set of quantitative and qualitative approaches for deriving valuable insights from data. It involves many processes that include extracting data and categorizing it in order to derive various patterns, relations, connections, and other such valuable insights from it. Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying an approach to collect more data that is related to their customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense out of it and derive valuable insights from it.
‘What gets measured, gets managed.’ – Peter Drucker
Comparison Between the Domains of Data Analysis and Data Science
||Mostly structured data
||Any type of data
||Statistics and data modeling
||Hadoop, programming languages, and Machine Learning
|Span of Domain
|Exploration & New Ideas
Understanding Big Data Analytics
The term ‘Data Analytics’ is not a simple one as it appears to be. It is the most complex term, when it comes to big data applications. The three most important attributes of big data include volume, velocity, and variety.
The need for Big Data Analytics springs from all data that is created at breakneck speeds on the Internet. Our digital lives will make big data even bigger, thanks to the ever-increasing penchant of individuals to see their lives ever-connected to the online world. It is estimated that by the end of the next year the cumulative data that is generated every second will amount to 1.7 MB which will be contributed by every individual on the planet.
This shows the amount of data that is generated and hence the need for Big Data Analytics tools to make sense of all that data. It organizes, transforms, and models data based on the requirements for identifying patterns in the data and drawing necessary conclusions.
Watch this insightful video to find out what a Data Analyst does in real life:
The larger the size of the data the bigger the problem. So, big data may be defined as the data the size of which itself poses the problem and which needs newer ways of handling it. So, the analysis of data at high volume, velocity, and variety means that the traditional methods of working with data would not apply here.
‘Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.’ – Geoffrey Moore
How Does Big Data Analytics Make Working so Easy?
There are various tools in Data Analytics that can be successfully deployed in order to parse data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale mean that the tools need to be specifically able to work with such kinds of data.
The advent of big data changed analytics forever, thanks to the inability of the traditional data handling tools like relational database management systems to work with big data in its varied forms. Data warehouses also could not handle data that is of extremely big size.
The era of big data drastically changed the requirements for extracting meaning from business data. In the world of relational databases, administrators easily generated reports on data contents for business use, but these provided little or no broad business intelligence. It was for that, they employed data warehouses. But, data warehouses too generally could not handle the scale of big data, cost-effectively.
While data warehouses are certainly a relevant form of Data Analytics, the term ‘Data Analytics’ is slowly acquiring a specific subtext related to the challenge of analyzing data of massive volume, variety, and velocity.
‘In God we trust, all others must bring data.’ – W. Edwards Deming
Types of Data Analytics
- Prescriptive Analytics: This is the type of analytics that talks about an analysis based on the rules and recommendations in order to prescribe a certain analytical path for the organization.
- Predictive Analytics: Predictive analytics ensures that the path is predicted for the future course of action.
- Diagnostic Analytics: This is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard.
- Descriptive Analytics: In descriptive analytics, you work based on the incoming data and for the mining of it you deploy analytics and come up with a description based on the data.
Working with Big Data Analytics
The topic of Data Analytics is a vast one and hence the possibilities are also immense. Prescriptive analytics ensures that it sheds light on various aspects of your business and provide you a sharp focus on what you need to do in terms of Data Analytics. Prescriptive analytics adds a lot of value to any organization, thanks to the specificity and conciseness of this domain. You can deploy prescriptive analytics regardless of the industry vertical based on the same rules and regulations.
Predictive analytics can also ensure that the domain of big data can be deployed for predicting the future based on the present data. A good example of predictive analytics is the deployment of analytical aspects to the sales cycle of an enterprise. It starts with the lead source analysis, analyzing the type of communication, the number of communications and the channels of communication, along with sentiment analysis through heightened use of Machine Learning algorithms and more in order to come up with a perfect predictive analysis methodology for any enterprise.
Diagnostic analytics is used for the specific purpose of discovering or determining why a certain course of action happened. For example, one can work with diagnostic analytics to review a certain social media campaign for coming up with the number of mentions for a post, the number of followers, page views, reviews, fans, and such other metrics to diagnose why a certain thing happened.
Descriptive analytics is the least popular which is basically used for coming up with a methodology for uncovering patterns that can add value to an organization. As an example, you can think about the credit risk assessment. It involves predicting how likely a certain customer is to default based on his credit history. It takes into consideration various aspects like the financial performance of the customer, inputs from past financial institutions that the person might have approached and other platforms like social media, and online presence based on the web-based solutions.
Since no organization today can stay without being inundated with data, it is imperative that Data Analytics is an indispensable part of the life cycle of data in any organization . Based on various types of Data Analytics, today’s forward-looking enterprises can actually go ahead and design a very robust path to success with the data they have.
‘If you torture the data long enough, it will confess.’ – Ronald Coase, Economist.
What Are the Various Tools Used in Data Analytics?
In this section, you will be familiarized with the tools used in the Big Data Analytics domain. Here is the list of analytical courses that you can take up for a better career in Big Data Analytics:
- Apache Spark: Spark is a framework for real-time Data Analytics which is part of the Hadoop ecosystem.
- Python: This is one of the most versatile programming languages that is rapidly being deployed for various applications including Machine Learning.
- SAS: SAS is an advanced analytical tool that is being used for working with huge volumes of data and deriving valuable insights from it.
- Hadoop: It is the most popular big data framework that is being deployed by the widest range of organizations from around the world for making sense of their big data.
- SQL: The structured query language (SQL) is used for working with relational database management systems.
- Tableau: This is the most popular Business Intelligence tool that is deployed for the purpose of data visualization and business analytics.
- Splunk: Splunk is the tool of choice for parsing the machine-generated data and deriving valuable business insights out of it.
- R Programming: R is the Number 1 programming language that is being used by Data Scientists for the purpose of statistical computing and graphical applications alike.
Watch this insightful video to learn more about the job role of a Data Analyst:
Companies Using Data Analytics
Today, regardless of the industry type, there is rapid deployment of various analytical tools and technologies. It could be the tools for parsing data or the easy-to-understand visualization tools which are used for making sense of the data. Further in this blog, some of the industries that are using Data Analytics tools are discussed.
There are digital-first enterprises for whom data analytical tools are the most important weapons in their arsenal. For example, Amazon, Facebook, Google, and Microsoft cannot survive without the use of Data Analytics. Amazon widely deploys analytics in order to recommend you the right product based on the product that you bought in the past. They also make use of data in order to build customer profiles to serve them better. This way, they can provide a very customized experience to their customers.
A company like Facebook will deploy Data Analytics to find out what its users are talking about so that it can understand what products and services the users would be interested in. Since it works on ads, it needs to know the pulse of its users by making sure that the ads are up to date in terms of customization and other aspects.
Google is sitting on the mother lode of all data. They serve a few billion searches every day making it one of the most data-intensive companies on planet Earth. Due to this, the need for analytical tools at Google is inevitable. Google is also hiring the maximum number of Data Scientists.
Watch this video on Data Analysis with Python Tutorial
Data Analytics is one of the vital aspects that is driving some of the biggest and best companies forward, today. Enterprises that can convert data into meaningful insights would evidently be the winners in this hyper-competitive world. Take Uber and Airbnb, for example. Uber has disrupted the taxi hailing business and Airbnb the hospitality domain. For Uber, the key to a growth of $51 billion is the big data it collects and leverages for intelligent decision-making with the help of Data Analytics. Whereas, Airbnb has been using Data Analytics tools mainly to bring out better user experience. Both these organizations are thriving for a consistent growth with the power of their deep data analytical approach. Hence, any company harnessing the benefits of Data Analytics can beat its competitors without a hitch.
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