What is Data Analytics?
The most phenomenal aspect of the 21st century has been the explosion of the pace and quantity at which the data is being generated. So much so that 90% of the world’s total data has been generated in the last few years. The meteoric rise and spread of internet accompanied by the massive decrease in the storage cost of data which has fallen from $300,000 for a GB (gigabyte) in 1981 to $0.10 by 2010 speaks volumes of the data revolution. The availability of huge amounts of data has led to a range of techniques that allow us to use this data for achieving numerous goals.
These techniques of analyzing data in modern times is collectively called Data Analytics. As this term is in high usage and is thrown around in almost all the business domains with each of them interpreting it in their own way, it is easier for its definition to get lost in the translation. From a non-domain-specific point of view, Data Analytics means the scientific usage of numerous statistical and programming tools to analyze the data through the use of statistics, mathematics, and visualization.
Before the turn of the 21st century, the data was generated as a result of various operations however, this data was not necessarily put to use to create further products. However, as the quantity and quality of data improved, so did the techniques and technologies to use this data and thus the field of Data Analytics came into being and became prominent as a field of study.
Why is Data Analytics So Important?
Just a quick google search or spending some time scrolling through any job portal can make one realize that the demand for Data Analyst and its related positions is ubiquitous. In all business fields, whether be it banking or medicine, engineering-related filed sot social science, all of them require specialized professionals to handle data and make the most out of it. Surely, there are reasons for the strong demand and growth prospects for a data analyst. Firstly, the key to excelling in any business is to have correct and useful information. Information in modern times in becoming synonymous with the term data, thus companies require to get hold of this data and perform various operations on it to extract this information and this is where data analytics comes in handy. The field of data analytics is responsible for
- Storing the data and making sure that the data is in the required format
- Analysing the data using a range of techniques to determine the present situation of the business,
- Formulating strategic policies based on the insights provided by data and
- Even creating products based on data that can be used for achieving short term to long term goals.
Thus data analytics is essential for the very survival of any business in today’s times. The importance of Data Analytics can be more precisely understood by understanding their use in some of the key industries. The industries related to Finance depend heavily on data analytics for their functioning. Data Analytics allows them to look for frauds, find loopholes in the policies, detect avenues of improvement. On a more advanced level where predictive modeling is performed, financial institutions such as Banks use them for detecting potential customers for cross-selling their products or detecting loan application that have a higher probability of becoming defaulters.
Another common industry is healthcare where data analytics is extensively used. Here, data analytics allows for creating automatic reports by analysing the data from the patient’s tests which leads to higher efficiency and reduction in the time for finding a diagnosis.
How Is It Different From Business Analytics?
As the field of analytics, in general, is fairly new, there is still a lot of enigma regarding the various terms where the word “analytics” is used. To get some idea for these jargons, it is important to distinguish between the two most commonly used and closely related terms viz. Business Analytics and Data Analytics. First of all, we must acknowledge that there is a good amount of overlap between these two fields as they both deal with scientifically analysing the data, Thus, the tools and techniques for analysing the data in both these fields are often common. However, where they differ is the end goal. Business Analytics is a type of Analytics that deals with analysing the data especially for solving typical business-related concerns. The aim of business analytics is to get hold of the data generated by the business, to solve the objective and strategic problems of the business.
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These typically include finding ways to increase the profit, reduce the cost, finding the current financial situation of the business, identifying loopholes in the policies among and finding solutions of other peculiar problems. Data Analyst on the other hand is a generic field and doesn’t always use data to solve the challenges faced by businesses. Thus, data analytics is often used to analyse the data such that new products can be created.
For example, analysing data to predict the next word is something the doesn’t particularly solve a business problem but in itself is a product that can be used and sold separately. Therefore, the range of problems that the field of data analytics covers is more in breadth while business analytics get into the depth of a particular business to solve the challenges faced by it.
How Is It Related To Data Science?
Another prevalent misconception is regarding the differences between Data Science and Data Analytics. These two fields are not different from each other and their differences can be understood in multiple ways.
As these fields are still at nascent stages if compared to other traditional fields of medicine and physics, their definition can vary from source to source. One understanding is that Data Science is the blanket term for all the technologies that use data for their functioning. This way, Data Engineering, Data Analytics, Business Analytics, etc. all come as the specialised fields under the major field of Data Science. The other understanding which directly pits Data Science and Data Analytics against each other is more on the technical side of things.
Here the use of advanced methodologies such as Machine and Deep Learning for the purpose of creating industry-specific products and/ or processes are considered to be a part of Data Science. Data Analytics on the other hand considers the performing of analytical operations on the historical data to get insights and create analytics-based products and/ or processes.
Evolution of Data Analytics
Data Analytics in itself is an umbrella term as a range of techniques are used in this field. In order to understand all these different techniques that eventually lead to different types of data analytics, it is imperative to understand the whole idea behind data analytics in depth. The technicalities of data analytics can be understood by going through the evolution of it.
Wave 1 (1970s) – ERP (Enterprise resource planning)
The first wave of Data was Operation centric also known as the ERP (Enterprise resource planning ) era. This is where data was used for day to day operations. Data was analysed to create reports making the business leaders understand the current situation and giving a very little glimpse of the way ahead. As this stage descriptive statistics were put to use along with the use of various basic visualisation techniques. As far as the tools are concerned, spreadsheets, basic tools such as SQL, in some cases SAP among other platforms were prominently used.
Wave 2 (1990s) – CRM (Customer relationship management and Business Intelligence)
This customer-centric phase marked the use of data analytics from a solely internal point of view (i.e. understanding the inner working of the business) to external (understanding the interaction of the business with the clients and customers). The data used in this phase was the one generated by the customers. This included analysing the customer’s preferences, behaviour, insights, etc. Now data was being used to understand market trends and formulate solid business strategies based on the insights provided by the analysis of the historical data. With this the techniques in use became more sophisticated and the use of inferential statistics and statistical modeling became common. More sophisticated tools such as SPSS and SAS were brought into play and this can be considered as the most crucial phase for the field of Data Analytics.
Wave 3 (2000s) – Ecommerce (Web Centric)
Till now, mostly descriptive and very basic form of predictive analytics was performed. This phase marked the moving of data analytics from answering questions such as What, Where and Why is there a problem to What will happen next, What should be done, etc. This was made possible because of the huge amount of data on which predictive models could be created using statistical and machine learning techniques. Now from finding hidden insights, data analysts were responsible to figure out insights that will surface in the future. This called for the use of sophisticated machine learning algorithms and their deployment using a programming language such as R and Python.
Wave 4 (2010s) – Big Data and AI
The current phase of data analytics is the most advanced and unquestionably the most exciting one. As the data is widely available in real-time, this phase has marked the automation of decision-making using AI. The techniques nowadays do not focus on finding what can happen but rather focuses on finding the most likely scenarios given various conditions and finding what decision will lead to which outcomes. The use of advanced machine learning as well as deep learning algorithm on big data platforms marks this phase. Products are being created that are used by the employees for very detailed and informed decision making.
Different Types of Analytics
By now you would have got fair amount of understanding about Data Analytics and may realize term Data Analytics is broad term. To understand it further you should also know about different types Data Analytics, which is best summarised as following:
Important Tools Used for Data Analytics
A doctor has a stethoscope while a solder wields a weapon, all marking certain typical tools required for completing the job. Similarly, there are certain particular tools that data analysts use for performing their day to day operations. Among the most common tool that a Data Analyst must know is MS Excel and SQL. Both these tools are the different end of the spectrum still are highly related to each other. While SQL queries provide the Data Analyst with the capabilities to quickly subset parts of data from an otherwise large dataset, MS Excel provides the opportunity to understand the data on the micro-level and is often used to present the final analytical results especially if they are in a tabular format.
After these basic tools, an intermediary tool is Tableau or any other visualization tool for that matter, as they are generally easy to handle and provide great aid in the report generation which is the cornerstone of data analytics.
Lastly, and most importantly, data analysts must be familiar and comfortable with advanced tools such as Python and R that allows them to perform advanced analytical operations on data. Moreover, these tools allow for statistical evaluation and testing of data and for automating repetitive data preparation tasks. If the aim is to create predictive models then also these tools are the best option.
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Different Type of Analytics Job Roles
There are a number of data-related job positions viz. Data Analyst, Data Engineer, and Data Scientist. Each of these roles focuses on a specific aspect of data-related operations and requires different skill sets. To understand these roles, one has to understand all the key steps in any analytics-based project. The initial step of any project is to understand the business problem and figuring out the requirement to execute that project.
This where people with business and strategy acumen come in and guide the process. Next is the determination of the analytical approach along with understanding the structure and format of the data, understanding the type of required data, and collecting such data.
This is where Data Engineers provide support by creating architecture to perform ETL (Extraction, Transformation, and Loading) of data. The next steps include data cleaning, data mining, exploratory data analysis (that includes visualization and statistical testing of data) which is mainly performed by data analysts. The next step of creating data-based models is something done by the Data Analyst or Data Scientist depending upon the nature of the business problem.
Here if the models are based on Machine or Deep Learning algorithms then Data Scientists lead the process and are responsible for the evaluation and validation of such models and even assist in the final deployment of the model into production.
It is important to note that every step of this process is important as, for example if the data will not be extracted and loaded correctly then all the insights will be based on the wrong data causing the whole project to fail. The role of Data Analyst is equally important as they are responsible for making the data fit for the development of various predictive models. Whereas Data Scientist create more advanced forms of predictive models and get involved in the testing phases of the model too.
Again, the following table can easily explain the scope, the background required to get into such roles, and how one can get started for preparing for them.
Responsibilities And Ethics
Power and responsibilities share a highly positive correlation. A Data Analyst can provide insights, find the drivers responsible for an event, and can influence the decision making. This makes the role of data analytics a powerful one. This leads to an important question – “What are the responsibilities of a Data Analyst?”. Apart from the typical qualities required for being true to any job, data analytics particularly requires the professionals to consider the impact of their actions.
A Data Analyst must not hide any key information or insights for vested interest. Also, while determining the drivers or while selecting the features for creating a predictive model, personal biases and prejudices must not interfere with the analyst’s decision making. In today’s world where data is often highly sensitive, a data analyst should be aware of the privacy rights of individuals and must never use the data with mala fide intentions.
There is no doubt that Data Analytics is a field that is in high demand and will remain so in the foreseeable future. People who plan to enter this field must be aware of the various applications of it, the various types of analytics performed under it, and all the other related fields.
Also, to start a career in this field, an appropriate course that covers all the major aspects of data analytics based projects is of immense importance. Thus people with interest in uncovering facts from large amounts of data and revealing insights that could only be found by the careful examination of data must look into the field of Data Analytics.
As this field is an amalgamation of multiples disciplines such as business, statistics, reporting, and programming, people from any of such fields can begin their career and learn the art of performing analytics using data.