According to Statista, an online platform for market and consumer data research, the number of companies around the world was approximately 213 million as of 2020. According to the research team of the World Federation of Exchanges, there are 58200 listed companies as of 2021. These companies have a workforce of 250 plus. In this world of mergers and acquisitions, most companies either have multi-brands under one roof or lots of multi-products under the same brand name. There are 4.5 billion estimated consumers as of 2023 and expected to rise up to 5.6 billion by 2030. These numbers alone forecast the vast Data Analytics Scope in the future.
An Introduction to the History of Data Analytics –
Data Analysis has been present since the last 19th century. The basis of data analysis was making the customer experience more delightful by making it more personalized, and this was possible only when the past shopping data of the customer was recorded, maintained, and evaluated over a period of time.
This was all handwritten. Then came the age of industrialization and production lines. Frederick Winslow Taylor was the first to propose the first-time management study to improve the productivity of the production line in an organization. Henry Ford later followed in his footsteps to use methods to figure out the speed of the assembly line.
With the advent of computers in the 1990s, a large amount of unstructured data was being gathered, but without a clue how to put it to use. In 2006, Yahoo came up with Hadoop which was the first to allow it to run big data applications on clustered programs.
By the 2010s Facebook, Yahoo, and Google were using data analytics scope to personalize the experience. In the same year companies like banks, financial sectors, retailers, manufacturing companies and healthcare companies stepped into the world of big data to gain from the data analytics scope data wise.
The Rise in Data Analytics Scope in the Current Scenario-
In 1880, the US Census Bureau took 7 years to complete a report and present the findings. Soon after Herman Hollerith invented the tabulating machine which took precisely 18 months to compute the very same data and present it for analysis. Starbucks earlier used to manually note down the buying behavior of repeat customers and feed it into computers, to ensure better service, lesser serving time, and in total better customer satisfaction level by data personalization.
In the 1990s there were only 3 million people using the internet. By 2022 there were 4.9 billion users, roughly 69% of the world population. It is expected that by 2025 another 900 million users will be added to this pool.
With the advent of the Internet of Things (IoT), there has been a huge increase in digitalization and the adoption of new technologies. With the rapid increase in business transactions and website interactions, a huge amount of unstructured data is generated every hour. And to structure and make sense of this data, arises the data analytics scope.
Main Types of Data Analytics –
The main Data Analytics scope is the discovery of meaningful patterns, by means of data visualization of a business and deriving actionable solutions and insights which can be communicated to the concerned in a comprehensible manner, to lead to solutions meant to increase business profitability. There are four ways to reach to this which are as follows :
A) Descriptive Analytics: What Happened
This branch of Data Analytics is the root of all other branches of analytics. It deals with simply understanding the trends from the pool of unstructured data gathered from the company’s history. There are three main tools of this which are :
★ Data Aggregation
★ Data Mining &
★ Data Visualization
The reports are classified into two ways, namely :
★ Canned reports: These are reports pre-designed by the Analytics team which give all the data trends related to the object in question.
★ Ad hoc reports: These reports are generally created to temporarily address the object in question.
A few examples of generally used descriptive analytics reports are
★ Key Performance Indicators Dashboards
★ Monthly Revenue Reports
★ Sales Leads Overview
★ Data Queries
★ Data Statistics
★ Data Dashboard
The unstructured raw data gathered is structured to be presented in the forms of charts, pies, and graphs which are easily comprehensible. The best examples of descriptive analytics are Google Analytics and HubSpot.
B) Diagnostic Analytics: Why did it happen
The next Data Analytics scope branches into diagnosing the variances in a business. The main concern of this branch is establishing a relationship between the different data trends and variables. A Data Analyst here has to find a connection between the patterns and envisage the pattern of behavior. A few of the techniques used for this are as follows :
★ Data discovery
★ Probability Theory
★ Data Mining
★ Regression Analysis
★ Time Series Analysis
★ Query & Drill downs
C) Predictive Analysis: What will be the future trends
This line in Data Analytics scope is considered quite important as here the Data Analyst, on the basis of the trends gathered from the historical data of the business, relates it to the reasons for the pattern and behavior, and variances, in order to predict the future trends and events. On the basis of the information gathered forecasting is done which paves the way for actions to be taken by the company. The following are the various techniques used in the predictive analysis :
★ Machine learning
★ Game Theory
★ Linear Regression
★ Data Mining
★ Time Series Analysis & Forecasting
★ Predictive Modeling
★ Decision Analysis & Optimization
★ Logistic Regression
★ Transaction Profiling
A few examples of the areas in which it is put to use the most are as follows :
★ Lead conversion
★ Risk assessment
★ Sales forecasting
D) Prescriptive Analysis: What is the best course of action
This is considered the most important in Data Analytics scope. The data analyst has to take all the factors mentioned in the last three branches and prescribe the most optimal data-driven solution. Actionable strategies are made on the basis of concrete data derivation, trend analysis, and behavior forecasting. This complex analysis involves the following tools :
★ Statistical Methods &
★ Computational Modeling
In real life, the best example of Prescriptive Analytics is Google Maps which as per algorithms and computations, prescribes the best possible ways to reach a destination.
4. Emerging Trends with Data Analyst Scope:
Here is a look at the emerging trends in the Data Analytics area and their potential benefits:
A. Big Data Analytics: Before the advent of the internet, companies maintained and managed their database either manually, or through spreadsheets and Excel sheets on a computer. The volume of data generation post the introduction of the internet grew to such a humongous volume which was not possible through the traditional ways, this is the birth of Big Data Analytics in the Data Analytics scope.
The tools used for Big Data analytics have low latency, thus the main characteristics are speed and efficiency. The following defines the main characteristics of Bid Data Analytics :
★ Volume: By the year 2020 the data was being generated at the speed of 1.7mb every second. This gives an idea of the volume of the data being generated by a normal company. Big Data termed was used thereby for the first time.
★ Variety: There is a variety of data to be stored, managed, and processed. This variety can include records, past history of the overall business, social media posts, customer interactions, employee databases, etc. These are either in the form of raw data or unstructured data.
★ Value: The data captured is of significant value to the user., because it enables the discovery of trends and patterns which results in quantifying business strategies.
★ Velocity: The data generated is high in volume and highly non-static in nature, thus prompting companies to devise ways to store, manage and analyze the variances and trends at lightning speeds.
★ Veracity: This humongous data is authentic by nature and accurate to the finer details, which in turn exudes managerial confidence in it.
★ Variability: Different computations, modeling, and analysis tools enable to capture of data with a higher degree of variance, which helps in foreseeing the different changes in the business or marketing processes.
B. Internet of Things (IoT) Analytics: The Internet of Things is the latest and The most highly emerging trend in the Data Analytics Scope. The main features of this analytics are as follows :
★ The data is received from physically connected devices and stored on the cloud or any other company-specified platform, and analyzed with no human intervention.
★ All the physical devices are assigned Unique Identifiers(UID), which is a chip embedded in smart watches, smart cars, kitchen appliances, heart implants, animals, machines in the production lines, and many more.
★ The chips have the ability to self-report in real time to analyze the trends and variances in split seconds.
C. Social Media Analytics: There are 4.89 billion people who are social media users in 2023, which is expected to grow to 6 billion by 2027. This number presents huge data on current customers and potential customers.
Social Media Analytics is a different area in the Data Analyst’s Scope which does not rely only on likes, dislikes, follows, and requests. But in reality, it is the practice of social listening, to be aware of the likes and dislikes of the current customers. The following are the different types of social media analytics and their main characteristics:
★ Social Media Monitoring: This involves analysis of posts, and keeping track of any increase or spikes in activities on social media.
★ Social Media Listening: This analytics involves checking the effectiveness of the social media campaigns, the current products, or the new launches, by keeping track of the interactions of the customers, and their posts. In a nutshell, learning about the likes, dislikes, and the likes to dislike of the consumers.
★ Social Media Intelligence: This analysis involves simply monitoring social trends and conversations.
★ Social Competitive Analytics: This stream of analytics involves keeping a check on the customer conversation and posts of the rival brands.
5. Major Industries with a Data Analysts Scope:
With the expectation of the consumer base to grow to 5.6 billion by 2030, one can only imagine the humongous size of the data that will be generated. At present the consumer base is around 4 billion, thus, the requirement for data analytics in all sectors, may it be government or business. We have narrowed down 10 sectors with a huge Data Analytics Scope, while all these sectors are pioneers in using data analysis:
The following are the areas where data analytics has been of great use to the government sector:
i) Health-related research and data analysis to identify trends and patterns region-wise. This helps in analyzing public health and keeping a real-time check on any major illnesses.
ii) Population census recording, storage, and analysis with advanced data analytical tools.
iii) Public activity analysis to keep criminal activities in a particular area or region, and illegal activities like human trafficking, and drug trafficking in check.
iv) Better segregation and identification of people who require aid, and faster response in aid provision.
v) Keeping a check on weather patterns, leading to more accurate weather predictions, and checks on natural disasters, which lead to better preparation and management of rescue teams.
vi) Energy exploration.
vii) Data analysis has helped in better coordination between various public departments, leading to lesser duplication of work, cost optimizations, and faster turnaround timings.
★ Banking & Securities
The banking sector was amongst the first pioneers to use data analytics. The areas where data science is deemed helpful in the banking sector are as follows:
i) Easy analysis of the credit and risk characteristics of the entire customer base.
ii) Better and faster decision-making for the authorities in imparting credit services to customers based on their credit predictive analysis.
iii) Ease in the analysis of the credit tendencies of individual customers.
iv) Customer services like discounts, credit cards with benefits, and accounts were easier to be customized on the basis of the trend analysis of a region or even an individual.
v) Better trade prediction analysis, leading to better investment analysis.
vi) Predictive financial market analysis, which also led to fraud, money laundering, and financial market anomalies prevention.
The healthcare industry was the most benefited from data analytics and continues to do so with newer innovations and ideas. Following are the areas where data analytics scope has made a huge impact:
i) Data analysis has given a new and specific insight into better diagnosis, clinical treatments, and patient-specific treatments.
ii) Data collection has helped in maintaining health records, predictions in health variances, and early treatments.
iii) Government has used data analytics in predicting epidemics and potential health risks amongst the public.
iv) With the help of the database alternative therapies, ease in pinpointing the symptoms, and detection of newly discovered diseases have been made which aids doctors in treating the patients more specifically.
v) Detection of more cost-effective ways of treatments can be made.
vi) Healthcare industries have been able to improve their products and enhance customer satisfaction.
The Data analytics scope has now extended itself into the sports arena too. Here are the following ways data analysts have made changes:
i) Data analysts collect data on various athletes and analyze them to enable the sports organizations in making effective decisions regarding the athlete’s performance, training programs, and diet plans.
ii) With the help of data analysts simulators and virtual devices have been invented to help in improving performance.
iii) Data is collected and analyzed on wearable gear which helps in the reduction of sports injuries.
iv) Sports Organizations take the help of data analysis to plan marketing campaigns, increase ticket sales and reduce operational costs through various methods.
v) Sports media channels use data analytics for enhancing sports reporting to increase fan satisfaction index, and more involvement of fans, which in turn helps to increase their TRPs.
vi) Sporting equipment manufacturing companies modify their current brands, innovate new products, and decide on pricing, on the basis of big data from consumers.
★ Media and Entertainment
With the changes in the media industry and customer base not only OTT platforms but even the theater industry is relying more on data analytics for the following :
i) Proper segmentation of the customer database to offer customized packages to reduce churn.
ii) Social media listening to increase the quality of experience, reduce unsubscriptions and audience disengagement
iii) Identifying customer segregation on the basis of the devices they browse on and the specific time period, to enable personalized ad modifications.
iv) Data analysis aids in the development of new products and services to incentivize every customer’s behavior to increase retention.
The education industry has seen numerous benefits with the introduction of data analysis, like :
i) The combination of pedagogy and technology to analyze each student’s performance and curate a curriculum that is in accordance with the individual child’s development.
ii) The trainers can with predictive analysis modify and create new training programs depending on the student engagement and education trail.
iii) Predictive analysis and prescriptive analysis help in focusing on the weaker students and enable more personalized training to prevent school dropouts.
iv) Data analysis helps in the optimization of school resources by ways of better planning, employment, and tracking.
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★ Manufacturing :
With the invention of the Internet of Things and data analytics, the biggest benefactors have been the manufacturing industry with the following benefits:
i) Supply chain management has been optimized by keeping a check on resources, suppliers, and manufacturers which has helped in cost optimization and waste reduction.
ii) Real-time check on machine performance, productivity, maintenance schedules, predictive analysis of breakdown, and increase in the uptime.
iii) Data analytics help in better customization of products, enhancing product quality and aiding in product development.
iv) Data analytics has made end-to-end visibility of production lines much easier which has helped in cost optimization.
v) Safety measures can be timely assured with the help of trend and variance analysis of the machines.
vi) Better inventory management to ensure no stock outs, damage, waste management, and reduce inventory overstocking.
vii) With prescriptive analysis manpower hiring is done with the most suitable experience to reduce training time and turnaround time.
The transportation sector is termed as one area where data analysis can impact in a huge way. Although major companies and the Government are using data analysis for the following benefits:
i) Real-time information of the vehicle route end to end.
ii) Better time, and resources management in destination, routes, distance, and travel time planning to optimize cost.
iii) Internet of Things has made it easier to regularly analyze the maintenance time period and any breakdown signals. This in turn reduces maintenance costs and breakdown time.
iv) Trains, bus operators, and flight operators using analytics in better load planning, customer occupancy, and enhanced customer satisfaction levels.
v) Regular maintenance alerts and variance data help the companies to keep a better check on safety measures.
Insurance analytics is based on predictive analysis by insurance companies, ranging from health, vehicle, property, and other sectors. A few areas in which insurance analytics works are as follows :
i) Big data has enabled the generation of organic leads, better customer acquisition, and customization of product offerings.
ii) Insurance analytics has benefitted companies in modifying their marketing campaigns according to locations and customer segments.
iii) Insurance analytics has eased the underwriting process by using predictive data in order to make better comprehensive risk assessments.
iv) Cases of fraud have been reduced by using historical data for advanced and predictive analytics.
v) Data analytics has helped in higher customer satisfaction which in turn has increased the number of policy renewals.
Energy companies NTPC, Powergrid, TATA Power, Equinor, and Shell are pioneers in energy analytics due to the following benefits :
i) Energy analytics helps to identify the requirement load down to the zip code with better customer database and segregation.
ii) Data analytics helps in identifying the variations and deviations in the supply and demand and the specific timings of higher and lower demands.
iii) Energy analytics helps in the efficient management of energy production to consumption by reducing overproduction, waste, leakages, and energy thefts.
Data Analytics scope has had a huge impact on the retail industry, impacting not only the online but the offline sector as well. Retail giants like Walmart, Reliance, Amazon, Future Group, and Tata Group are using retail analytics for the following advantages :
i) Retail analytics capture data on the basis of visitors, browsing patterns, purchase patterns, and demand patterns to customize the marketing communication.
ii) Data analytics help in predicting the pattern and trends of customer demands, facilitating precise data-driven data for better inventory management.
iii) With the analysis through data collection companies and outlets can plan focused offers to increase sales and attract new customers.
iv) Customer feedback data analysis aids companies in changes in products, ad campaigns, offers, and benefits offered for better retention.
v) Data Analytics can predict the trends in the market.
vi) Retail analytics also aids in choosing retail outlets to get a better return on investment.
FAQs: Data Analytics Scope
A) What are the primary skills required for becoming a data analyst?
B) What are the career opportunities and average salary in the field of data analytics?
At a predicted growth rate of 18% in the field of employment of data analyst experts, a data analyst can be employed as a Data Scientist, Data Engineer, Financial Analyst, Business Analyst, Production Analyst, Machine Learning Engineer, Quantitative Analysts, Data Visualization Specialist, Functional Analyst, Data System Developer, Marketing Analytics Manager, Risk Analyst, and Data Governance Analyst to name a few. The average starting salary of a Data Analyst in the US is between $70-80k/year. The starting salary of a data analyst in India starts from Rs.4lacs/per annum.
C) What are the important responsibilities of a Data Analyst?
A data analyst has to collect unstructured data, filter it and identify complex trends, and patterns in them. Data Visualization is the most important job aspect. The Analyst should be good at report generation and presentation. Communication should also be a strong point as one needs to make the management understand the patterns and trends. Identification of new data trends, data security, and implementation of the analysis is also important aspect of the Data Analyst.
Conclusion: Data Analytics Scope
Data Analysts are an upcoming and emerging need of all, may it be the business sector or the Government. It enables data-driven decision making which has lower chances of risk and a deeper insight into future trends. Businesses can not only optimize the manufacturing process and cost reduction ways, but the Government can induce faster processes, accurate data, and better decisions for the future like smart cities and automated processes. As compared to the traditional route the risk mitigants in all the areas get greatly reduced as compared to decisions made manually and on guesswork.
Data analysts not only aid in manpower management, hiring, and training needs in companies but also help educational institutions to prepare students for a better and smarter tomorrow by personalizing course curriculums. Better training programs not only reduce the number of school and college dropouts but are eventually preparing a better future for the country too. Thus, data analytics should be incorporated in as many sectors as possible for efficiency.