Let us see how relevant Data Analytics is by diving deep into the above questions. And then we will decide if doing a course on Data Analytics is worth our time.
What is Data Analytics? And how much impact is Data Analytics making in our lives?
Data is everywhere. It is raw facts collected from the world to make a piece of useful information. For example, consider a marketing person collecting information about prospective clients. The details he collected are uncooked primary data. Suppose he analyzes the data, filters it, and makes a client list out of it to suit his business model. Then in a sense, he is doing data analytics on a very small scale. He brought useful information from the raw facts he collected and put them into order. From this step, he might have saved some time from dialing to wrong clients. Imagine Data Analytics as something much larger and more complex when it comes to business and governments.
Simply put -
“Taking data from the world- identifying the characteristics of the extracted data- analyzing the pattern - and devising information to meaningful decisions - so that we (business or any establishments) could land high performance and growth” is what Data Analytics implies.
Data Analytics is used in almost all sectors of our world. We hear industry giants, multinational companies, businesses, and governments are all leveraging Data Analytics to perform in a better way.
So, “betterment” is the term.
With Data Analytics we could improve the functioning of an organization or multiply the return of investment (ROI) of a particular business or forecast economic fluctuations for making wiser marketing decisions or draft government policies that could impact lives. Analytics could be applied to any sector – Information and Technology, Management, Marketing, HealthCare, Finance, Security, and anywhere that comes with data. For Example, large manufacturing companies calculate the runtime, downtime, and work queue of the machines and then use data analytics to distribute the workload so that machines operate at optimum levels.
How is Data Analytics done? Is it necessary?We simply stated – “Do analytics for better results” and not how. Let us look into how Data Analytics is done in theory and see how different entities are using their data to drive improvements in all terms.
In layman's terms, Data Analytics is done by these four primary steps.
- Taking the data
- Arranging the data, cleaning the unnecessary data
- Observing the patterns and taking the essential numbers
- Drawing the final PictureOfficially we could say the primary underlying steps in a Data Analytics are
Officially we could say the primary underlying steps in a Data Analytics are
1) Data Mining
2) Data Management
3) Statistical Analysis
4) Data Presentation
Data MiningTaking the necessary data is the first thing to do in Data Analytics. Whoever is trying to do Analytics need data relevant to their sphere. For instance, an online shopping platform that we use to buy our favorite essentials will have all the consumer data. If that online shopping company decides to bring analytics to their process, they will need to have software tools to extract all the information from their business transactions. Once they do, they will get a whole load of data with customer details, product selling information, sales figures, and the rest of it. This is the data chunk they need to chip off to get useful information so that the company gets meaningful insight into customer buying behavior, finding high-demand products, removing stale products out of inventory, and forecasting on stocking.
Observe advertisements while searching the internet. It tends to show product ads based on what we have searched for before. If we search for a pair of jeans on one of our eCommerce websites, there is a greater chance of jeans adverts popping up during our stay on the internet or maybe as a YouTube Ad. This type of advertising uses analytics to track our search patterns and come up with displaying advertisements based on our liking and finally draw us to purchase the item.
Data Mining takes a great deal of time. Once the raw data are extracted, they will be transformed into a format where we can work to look for patterns.
Data ManagementOnce we get a grip on the data from Data Mining, the next is to segregate the data and convert it into calculable blocks. Designing databases is the core step in data Management. This is where Data Analysts or Data Analytic tools are required. Imagine the raw data received from Data Mining as the ‘clay’ and the database as the ‘mold’. The shape of the mold determines the shape of the clay figure. Likewise, Analysts or Analytic platforms help design and create molds (databases) to convert raw data into usable information. Databases give easy access to the results of Data Mining.
Statistical AnalysisThis is the arithmetic part. Using advanced statistics and machine learning techniques patterns and trends are identified from the structured data. In this stage, the large and incalculable data is transformed into simple representations and figures that anyone could analyze their work as contours and can make informed and insightful decisions for their undertakings.
Data Presentation
Visual representation is a strong storyteller. Pictorial representation of the data, or Data Visualization is a powerful instrument to communicate with personnel across all departments irrespective of their background to give clarity on the current state of affairs. Functionaries in higher echelons can assimilate the data as valuable insights and make strong impacts in decision making.
Industries Using Data Analytics
We emanate tons of data every day- while shopping online, visiting a hospital, driving with our GPS navigation on, chatting with friends on social media, and likewise. We are all interconnected visibility and subtly with multitudes of organizations. Businesses, organizations, industries, and governing bodies always work with data. It was with the coming of Data Analytics these piles of stored data got transformed into pointers for people to make informed decisions in their working fields. Almost all major industries around the globe are using Data Analytics -Agriculture, Banking, Finance, Education, Energy, Government, HealthCare, IT, Logistics, Manufacturing, Media, Real Estate, Retail, and the rest of the world.
Look at the following statistics on different sectors of the world.
On Banking
As per Soccer Nurds- the Market for Big Data analytics in the banking sector could rise to $62.10 billion by 2025.
- When taking figures in 2013, 64% of the global financial sector had already integrated Big Data as a part of their infrastructure.
- In 2015, the industry reached a market size of $12 billion.
- In 2019, the Big Data banking analytics market hit $29.87 billion, which could probably eventuate at a CAGR of 12.97% between 2020-2025.
Data generated by banks across the world are used to set new levels of customer services, help banks to design custom packages for customers, and better risk mitigation.
On health care
‘ Globe News Wire’ reports-
- The Big Data healthcare analytics market was over $14.7 billion in 2018. · In 2019, it was worth $22.6 billion and expected to grow at a CAGR of around 20%.· Big Data analytics in healthcare could be worth $67.82 billion by 2025
Healthcare is a major contributor to data. Patient diagnosis, medicine data, Patient feedback, Treatment procedures, medical billings, and the rest of the large transactions generate multitudes of possibilities for improving care for the community. What could data analytics mean to healthcare is-
- Reduced healthcare costs for communities
- Improved treatment capabilities of healthcare professionals
- Effective control of preventable disease
- Outbreak prediction, epidemic warnings
What will be the future of Data Analytics?
Even though businesses of all scales have Data Analytics for their stratagem, most of them are struggling to harness the true potential of data, mainly due to a lack of capable resources, expertise, quality in data, and mediocre insight conversion. We are in the early stages right now.
As we discussed before, the Data Analytics development cycle stages are
from - What happened (Descriptive) to Why did it happen (Diagnostic) to What can we learn from the past (Discovery)
to What is to happen (Predictive) to What should be the best to step to take (Prescriptive Analytics).
Establishments often find themselves in stage 2 or 3, probably in the ‘discovery’ stage. Data Analytics is sure to give many favorable results in decision making but in the future, it is going to be more advanced with multiple revolutionary technologies like Artificial intelligence, Business Intelligence, and Machine Learning.
Careers in Data Analytics Why choose a career in Data Analytics? One of the most sought after, with lucrative payscales, and superior advancement options make a profession in Data Analytics worth a lifetime.
Starting your career as a Data Analyst doesn’t come with heavy qualification pre-requisites. Careers in Data Analytics have a solid path of progression and It is one of the most demanded professions of this century. The number of organizations implementing data analytics is growing at a higher pace. And more businesses are readying up to get expertise in data analytics to work on their data to become future proof in their frontier.
This is Why.
- Above Average Salary Packages
- Less Qualification Pre-requisite
- Broader Career Advancement Options
- Ever Growing Demand
- Flexibility to work in varied industries
- Challenging, never tedious
Career Paths in Data Analytics
Once you have developed enough skills and earned the required certifications you might get hired as a Junior Analyst. If you have professional experience, based on your level you may land as a Data Analyst. After gaining enough knowledge and experience as a Data Analyst you can progress and advance as a specialist, manager, consultant, scientist, or engineer.
Data AnalystData Analysts are responsible for managing large data associated with an entity and transforming them into performance indicators. They use dedicated tools to extract and analyze complex organizational data and make them readable as business variables for the management to make data-driven decisions.
Suppose if you work as a Data Analyst for a retail network, you will extract the selling data, product data, and inventory data and analyze patterns that could draw customer behavioral patterns, inventory performance, and revenue leakages. From these patterns, you could suggest valuable insights on how to make thoughtful buying based on market conditions or how to engage with customers for high retention.
Data Scientist
Data scientists are the tool makers. They make the framework for Data Analytics to happen. Remember the ‘clay molds’, they design statistical models for converting data into insights and actionable decisions. The extracted data is churned to bring data products. New frameworks are developed and existing ones are constantly polished and upgraded to give accurate data analysis as per business requirements. Data Analysts should hone their skills in programming, advanced mathematics, and machine learning to make their move as Data Scientists.
SpecialistsWhen a Data Analyst specializes in a dedicated industry and finally earns mastery in that field, they are called Specialists. Their knowledge will go deep in finding solutions specific to the industry. Business Analysts, Financial Analysts, Marketing Analysts, Operations Analysts, Systems Analysts, and Health Care Analysts are among the popular ones. Business analysts work with data to shape organizational operations, power structures, and workforce management to align into an efficient harmony. Financial analysts dig deep into investments, revenue opportunities, demand forecasts, and crisis management. Operations analysts oil the structural and technical machinery of an organization. Marketing analysts meditate on trends and design strategies to place competitive offerings, advertising, lead conversions, and customer engagement. Systems analysts give ideal insights on technological infrastructure, cost management on technology acquisitions, and effective upgrades on technology. Health care analysts help hospitals, and clinical facilities deliver better and more efficient care. They draw data from patient records, health records, surveys, insurance transactions, clinicians, and medical professionals to design potent care strategies.
Still Uncertain about taking a Course on Data Analytics
We have a curated certification course on Data Analytics that will train you and transform you to be an expert in Data Analytics. This is an Advanced Course in Data Science with Python. We guess you might have some understanding of what Data Analytics is after reading this blog. Our course is highly elaborated and detailed and we have the curriculum divided into digestible parts. We will guide you on the basics of Data Analytics, and different types of Analytics. You will walk through one of the most popular programming languages called Python. Work with data sources on SQL
(Structured Query Language), learn Business Intelligence with PowerBi and master Data Visuatatlion skills with Tableau. If you need to cement fundamental concepts in Data Analytics check out our free courses on Data Analytics Concept Mastery, Python Concepts, Tableau Concepts, and SQL basics. Statistics tell some great predictions of our future
The Analytics Market size is predicted to grow to a $57 million industry by 2023.
https://financesonline.com/relevant-analytics-statistics/#:~:text=60%25%20of%20companies%20around%20the,effectively%20(MicroStrategy%2C%202020)The Big Data Analytics market is projected to reach $ 105.08 billion by 2027 at a CAGR of 12.3% period from 2019 to 2027 (Businesswire, 2020). The estimated total value of the business analytics software market in 2023 is $57 million (Allied Market Research). Small- and medium-sized businesses are seen to drive the market’s growth (Allied Market Research). The Asia-Pacific region will experience the highest growth at a CAGR of 12.10% (Allied Market Research). Customer analytics are expected to retain domination of the market in 2023 (Allied Market Research). Hybrid deployment is projected to have the highest growth (Allied Market Research). Amazon Web Services still dominates the analytics market with earnings of $10.8 billion for Q2 2020 (CloudTech, 2020). North America leads the big data analytics market and accounts for a share of more than 35% of the total revenue (Businesswire, 2020). 47% of enterprises’ analytics platforms are cloud-based in 2020, up from 39% in 2018 (MicroStrategy, 2020). 65% of global enterprises increased their analytics spending in 2020 (MicroStrategy, 2020). 27% of organizations worldwide cite security as the most important factor in selecting an analytics solution (MicroStrategy, 2020).
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