Data Analytics refers to the set of quantitative and qualitative approach in order to derive valuable insights from data. It involves many processes that include extracting data, categorizing it in order to analyze the 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 they are deploying an approach in order to collect more data that is related to the customers, markets and business processes. This data is then categorized, stored and analyzed in order to make sense of it and derive valuable insights out of it.
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“What gets measured, gets managed.” – Peter Drucker
Comparison between the domains of Data Analysis and Data Science :
|Criteria||Data Analysis||Data Science|
|Data Type||Mostly structured data||Any type of data|
|Tools Used||Statistics and data modeling||Hadoop, programming languages, machine learning|
|Span of domain||Comparatively smaller||Very expansive|
|Exploration & new ideas||Not needed||Needed|
Understanding Big Data Analytics
Though the term Data Analytics might seem simple it is anything but simple. Data Analytics is most complex when it is deployed for 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 the 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 that their lives are ever-connected to the online world. It is estimated that by 2020 the cumulative data that will be generated will amount to 1.7 MB every second for every individual on the planet.
This shows the amount of data that is generated and hence the need for big Data Analytics tools in order to make sense of all that data. It organizes, transforms and models the data based on the requirements in order to draw the necessary conclusions and for identifying patterns in the data.
Watch this insightful video to find out what a Data Analyst does in real life:
The larger the size of the data the bigger is the problem. So big data may be defined as the data where the size itself poses the problem and this needs newer ways of handling the data. So the analysis of data at high volume, velocity and variety means that the traditional methods of working with the data do 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 Big Data Analytics makes working so easy?
There are various tools in Data Analytics that can be successfully deployed in order to parse the data and derive valuable insights out of it. The computational and data-handling challenges that are faced at scale means 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. Also the data warehouses cannot 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. For that, they employed data warehouses, but data warehouses generally cannot 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 the analysis based on the rules and recommendations in order prescribe a certain analytical path for the organization.
- Predictive Analytics – This type of 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 this type of analytics we work based on the incoming data and the mining of this data we deploy analytics and come up with a description based on the data.
Working with Big Data Analytics
The subject of Data Analytics is a very vast one and hence the possibilities are also immense. Prescriptive analytics ensures the big Data Analytics can shed the light on the various aspects of the 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. We can deploy the prescriptive analytics regardless of the industry vertical based on the same rules and regulations.
The domain of predictive analytics can 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 sales cycle of an enterprise. It starts with the lead source analysis, analyzing the type of communication, the number of communications, the channels of communication along with the 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. As an example you can work with diagnostic analytics in order 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 in order to diagnose why a certain thing happened.
Descriptive analytics is one of the least popular which is basically used for coming with the methodology of 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 to default is based on his credit history. It takes into consideration the various aspects like the financial performance of the customer, getting inputs from past financial institutions that the person might have approached and other platforms like the social media, online presence based on the web-based solution.
Since no organization today can stay without being inundated with data, it is imperative that Data Analytics is an indispensable part of the data journey of any organization today. So based on the various types of Data Analytics, today’s forward-looking enterprises can actually go ahead and design a very robust path to success based on the data that 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 we will be familiarizing you with the various aspects of the big Data Analytics domain. So herein includes a list of analytical courses that you can take as follows:
- 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 advanced analytical tools that is being used for working with huge volumes of data and derive valuable insights from it.
- Hadoop – It is the most popular big data framework that is being deployed by some of the widest range of organizations from around the world for making sense of big data.
- SQL – this is the structured query language that is used for working with relational database management system.
- 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 derive valuable business insights out of it.
- R programming – R is the one number programming language that is being used by data scientists for the purpose of statistical computing and graphical applications alike.
Companies using Data Analytics
Today regardless of the industry type we are seeing rapid deployment of the various analytical tools and technologies. It could be the various tools for parsing of the data or the one’s which are used for making sense of the data in terms of easy-to-understand visualization tools. Some of the industries that are using Data Analytics tools include:
These are digital-first enterprises for which the data analytical tools are the most important weapons in their arsenal. These could be the Amazon, Facebook, Google and Microsoft which 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 or the one that you just bought. They also make use of data in order to build customer profile in order to sell 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 their users are talking about so that they can understand what are the products and services that they would be interested in. Since it works on ads they need to have the pulse of the users by making sure that the ads that they serve 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 of the year making it one of the most data intensive companies on planet earth. Due to this the need for analytical tools at Google is one of the most pressing. Google is the company that is hiring the maximum number of data scientists and all this shows the importance of Data Analytics at one of the biggest companies on earth.
Data Analytics is one of the most vital aspects that is driving some of the biggest and best companies forward today. The enterprises which can convert data into information and information into insights are the ones which will own the future in a hypercompetitive world where your next competitor can come from any industry vertical. Uber disrupted the taxi hailing business, Airbnb disrupted the hospitality business. Both these organizations are thriving on the sheer power of their deep data analytical mindset. So the way forward for any company worth its salt is have a clear data-driven approach and harnessing the power of big data using transformational data analytical techniques.
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