By any chance, were you under the impression that data analysis is a 21st-century invention?
The facts say otherwise.
1663 — data about the bubonic plague was collected, analysed and stored.
1884 — the first punch card tabulating machine was invented to keep tabs on the US population numbers.
1943 — A processing computer was created to interpret Nazi codes.
1966 — Data machines began to store tax records in the US.
1990 — the Internet made an appearance.
2001 — big data began to change the corporate world.
Throughout history, great minds have found ways to collect, store and analyse data. The arrival of the Internet quickly sped up the process, thanks to the immense quantity of digital information that was suddenly available. Online transactions and consumer patterns began to reveal hidden insights. Companies were able to understand their customers better. They received valuable information that helped them improve their performance. The internet never forgets. Every time you enter information in a search engine or an e-commerce shopping cart, it leads to advancements in analytics.
Also Read – 14 Excel functions every Data Analyst must know
Industries Benefiting from Data Analytics
At present, data analytics has something to offer to every industry. Whether a business wants to earn more money or get more customers, data analytics can help. Major industries benefiting from effective data analysis are banks, financial institutions, insurance companies, healthcare organizations, education, and governance.
These organizations are employing specific fields of data analytics connected to their industry’s processes and operations. As a result, we today have different domains of data analytics. These include;
- Marketing Analytics: Marketing analytics involves using data to assess and analyze the efficiency of marketing activities. With the help of deep insights, marketers can generate specific campaigns and higher ROI. Based on the requirements, the marketers can choose from different analytics models, including;
- Media Mix Models (MMM)
- Multi-Touch Attribution (MTA)
- Unified Marketing Measurement (UMM)
For instance, marketing analytics is used to take a deep dive into the brand’s products and check their performance in the market. They gather information by speaking to consumers, taking polls, and conducting surveys to understand the audience’s preferences and determine their competitive advantage. With this information, the brands can align their products with customer preferences and increase conversions.
- Financial Analytics: Financial analytics involves answering specific questions about the business’ financials. By collecting and analyzing financial data, businesses attempt to forecast future financial scenarios and devise a befitting growth strategy.
Financial analytics helps an organization work with factual insights rather than working with intuition. In this field, CFOs and analysts analyze historical data, gather current financial data, and forecast future trends associated with the organization.
Some key areas where financial analysis is used include measuring a product’s profitability, analyzing Y-o-Y growth rates, and running top-down analysis.
- HR Analytics: HR analytics measures and analyzes human resources-related data. Also called workforce analytics, talent analytics, or people analytics, this field tracts and measures the impact of HR strategies on growth and performance.
Experts use HR analytics to assess the credibility and effectiveness of the strategies. One of the areas where HR analytics is used is employee turnover. Higher employee turnover means that the company needs to revamp its policies and culture, giving the employees a better working environment.
Similarly, other industries like manufacturing, transportation, insurance, retail, and energy can leverage data analytics according to their niche requirements.
As a result, the C-Suite executives get a clearer picture of their organization. They get complete information about the sales channels and which measures to take for better results.
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What is the Role of a Data Analyst Specialist?
A data analyst’s responsibilities include data organization collected and extracted from sales, market research, logistics, and other areas. As data is collected from a number of sources, a data analyst analyzes the same and presents ready-to-use information for marketers, operations managers, production managers, and other stakeholders.
Primarily, a data analyst is tasked with converting data sets into insights that communicate with businesses. Here’s an overview of the roles and responsibilities of a data analyst;
- Data Extraction: They extract data from several primary and secondary resources connected to an organization. In addition to extraction, the data analysts also store data within the defined parameters.
- Data Refining: Under data refining, the data analysts sort and categorize the data while removing the corrupted data from the data set. They fix any coding errors and issues related to the same. As a result, the earlier cluttered and unorganized data is easier to read and understand.
- Data Analysis: Under data analysis, professionals model data to structure and design databases. They choose the type of analysis that is required according to the situation and establish categories of data. Moreover, under analysis, the analysts also decide how to present data.
- Statistical Tools: An important part of a data analyst’s job is using the right set of tools and technologies to complete all their functions. They need to choose from Microsoft Excel, Google Sheets, Tableau, Python, SAS, Microsoft Power BI, Jupyter Notebooks, etc. Using these tools; the data analyst can present the data and make it readable.
- Trend Analysis: Data analysts are responsible for identifying business-oriented trends at the local, national, and global levels. They figure out trends that have the potential to change how an organization works and lay out its future course of action.
- Report Generation: Another important function of data analytics professionals is generating bespoke reports. They design and create reports with pre-selected parameters according to the requirements.
According to Dataversity, the utilization of analytics goes as far back as the 19th century. Frederick Winslow introduced time management exercises. The second milestone for analytics came with Henry Ford’s assembly line arrangement.
How data analytics is affecting the finance industry
Today, the process of data analysis has become a multi-billion dollar industry worldwide. Raw, unadulterated data is processed, organised and analysed. Large amounts of facts, figures and statistics are transformed into coherent stories, meaningful insights and valuable corporate currency.
The finance industry has also benefited from hiring professionals who analyse data.
Now, banks carry out analytics before approving loans. They apply the technique of logical regression which results in two outputs — yes and no. Depending on the answer, the loan is approved or rejected. Credit card companies also use it to decide credit limits for the customer.
Professionals who analyse data for a living are a hot commodity. But first, they have to learn how. It is important to get certified in this field, as you should know the concepts, tools, skills and technology. Data analysis requires technical knowledge and acumen.
What is financial analytics?
The finance industry is a complex field filled with uncertainties and economic fluctuations.
Financial analytics helps to restore order and accuracy to this chaos. The process of data analysis helps finance professionals dig deep for reliable insights that can help with monetary growth and future investment opportunities.
How to become a financial data analyst?
- You should have one or more of these degrees — Bachelor’s, Master’s, BBA or MBA.
- Your knowledge of core finance concepts should be good.
- A financial analyst certification can also boost your chances.
- Your skills in maths and statistics should be exemplary.
- You must understand the various nuances of the financial industry at a deeper level.
- You get the theoretical knowledge and practical understanding to face financial challenges.
- You learn how to optimise the allocation of funds and budgets.
- The process of data analysis can lead to better, more informed financial decisions.
- Financial analytic tools can also help to protect sensitive data, avoid fraudulent transactions and improve overall security.
- You can analyse data for various tasks such as creating valuation, forecasting and financial planning.
Job opportunities in financial analytics
- You can work in banks, investment services, NBFCs and international finance corporations.
- Companies such as Ernst & Young, Amazon, Tata Consultancy Services, Citi Group, and Accenture hire financial data analysts.
- Financial professionals who can analyse data can earn between Rs 3 to Rs 15 lakhs per year.
Become a Financial Analyst in just 6 months
What is business analytics?
Companies use analytics to gain new customers, fix business problems and improve performance. Analysts hired to analyse data are savvy, driven professionals who understand how the corporate world functions. They use advanced technologies and methods to create data-driven strategies that lead to growth.
How to become a business data analyst?
- Get certified through a reputed course that teaches the process of data analysis.
- Learn the fundamentals of analytics such as forecasting, data modelling, etc.
- Develop practical skills through real-world applications.
- Work on your presentation and communication skills.
- Helps you make smarter business decisions.
- Improves marketing techniques that lead to an increase in sales.
- Streamlines processes and operations within the organisation.
- This leads to cost-cutting and improves budgeting.
Job opportunities in business analytics
- You can work in many diverse industries such as retail, e-commerce, real estate, auto and manufacturing.
- Big companies such as Amazon, Dell, Jio and Deloitte hire professionals to analyse data.
- Salaries range from Rs 3 lakhs to Rs 15 lakhs a year.
5 steps of data analysis
Every touch on an iPad screen, click of a keyboard button and swipe of a credit card online is registered in the large swirling pool of big data. How does Netflix know what shows and movies to recommend? How do ads on Instagram know what’s in your shopping cart and remind you to buy them? Data-backed research is fuel for growth. Experts call it the ‘new oil’ which is going to improve the economy and enable prosperity. All you need to know is how to access and utilise this digital goldmine.
There is a definite process of data analysis that every professional must know. It involves a scientific approach, critical thinking and logical reasoning. Here is a step-by-step explanation of the tasks involved. Experts analyse data by following these five steps.
Identify the problem
To ensure you receive optimum results, you first need to articulate the problem. A vague question will receive generalised solutions. You must be crystal clear when defining your query to analyse data. It is similar to a hypothesis.
For instance, instead of asking, “Why is the product not selling?”
You could frame the question as, “What are the specific factors affecting the sale of the product?”
“How to reach more consumers while lowering retention costs?”
Once you have the exact question, you can move on to the next step.
We are now ready to move on to step two in the process of data analysis. It is now time to access the data. You will need a strategic plan to accumulate the relevant information. Depending on the question, you can choose between quantitative data, which deals with statistics and numbers, and qualitative data, which looks at reviews, likes, posts and other customer-related interactions.
You can also scan through first-party, second-party and third-party data.
First-party data is collected directly from your online websites and consumers.
Second-party data is collected from the first-party data of other companies.
Third-party data is collected from several sources by a research-driven organisation.
Various data tools such as DMP and SAS are used during data collection.
Most of the data gathered may be raw or unstructured. There also might be repetition, irregularities and inconsistencies. A good analyst will roll up their sleeves and proceed to clean up the data so that is it ready for the process of data analysis. Some of the cleaning-up activities include:
- Eliminating data that doesn’t fit into your research.
- Correcting typos, duplicated information and minor errors
- Organising the information methodologically.
This is the reason why we are all here, isn’t it? Now is the part where different techniques carry out the process of data analysis. There are four types of methods used to analyse data:
- Predictive analysis
- Prescriptive analysis
- Descriptive analysis
- Diagnostic analysis
You pick the method according to the insights you hope to gain.
Sharing your insights and solutions
You’ve done it. You’ve figured out some incredible insights. Now it’s time to share them with the company or client. However, make sure your findings are clear and easy to comprehend. Many analysts use dashboards and visualisation techniques so that people outside the data industry can understand them better.
4 methods that help the process of data analysis
As the name suggests, predictive analysis helps to forecast growth opportunities. Over the years, the technology has evolved to offer staggeringly accurate results.
Industries such as e-commerce, retail, finance, entertainment and healthcare benefit substantially from using predictive methods to analyse data.
Companies such as Walmart, Amazon, Google and Netflix are just a few that have profited immensely. In fact, Amazon uses predictive analysis to predict sales. Before the customer even places the order, the product is already shipped! This leads to super-quick deliveries and very impressed customers.
Even the healthcare and insurance industries can use predictive AI algorithms to determine which demographic is more likely to have specific health conditions.
The diagnostic method is a process of data analysis that tries to find out exactly what happened or what went wrong. Like a doctor with his tools, this form of analytics looks for symptoms and underlying causes. For instance, a diagnostic analysis of the failure of Google Glass would tell you that the eyewear launch was ambiguous and confusing. It would also reveal that people were baffled about its function and purpose. When you analyse data through a diagnostic lens, you discover insightful nuggets that can lead to better, more efficient products as well as marketing techniques.
Many analysts use descriptive analysis to understand trends and relationships in the industry. It summarises the information and helps you figure out what has happened and what can be done next. Software systems such as MS Excel, Tableau and Google Charts help break down the data into simple characteristics. Companies like eBay and Spotify use descriptive analysis to understand their customers better and how to keep them happy. It is a crucial part of the process of data analysis and plays a key role in decision-making strategies.
The prescriptive method is a complex and intricate technique in the process of data analysis. It uses data gathered from the other three types of analytics to make recommendations about future products and services. They help to fill the gaps in the market, or even create new, breakthrough products.
Analysts use machine-learning algorithms to sieve the data while looking for specific conditions. Banks and financial services can use this method to predict possible cases of fraud. Investment bankers can use prescriptive analysis when deciding future investment prospects. And now you know how Netflix decides what content you might like.
|PREDICTIVE ANALYSIS||DESCRIPTIVE ANALYSIS||DIAGNOSTIC ANALYSIS||PRESCRIPTIVE ANALYSIS|
|Uses past or correct data to make predictions for the future.||Summarises data to decode patterns and trends.||Delves deep to discover the root cause of a problem or specific factors affecting a company.||Offers ideas, insights and solutions through advanced algorithms and data-based techniques.|
|Techniques used to analyse data:
||Techniques used to analyse data:
||Techniques used to analyse data:
||Techniques used to analyse data:
|Finds solutions to the question:
“What should happen?”
|Finds solutions to the question:
“What did happen”
|Finds solutions to the question:
“Why did it happen?”
|Finds solutions to the question:
“What should we do next?”
Proschool offers courses in financial and business analytics
At Proschool, you have the opportunity to learn from the leaders in finance and business analytics. As current experts in their fields, the teachers offer a slice-of-life education that is practical and holistic. The student experience is enhanced through hands-on training, active learning methods and out-of-the-box thinking. You work on real-world projects and case studies so that you understand how the industry operates. Here are some additional benefits of learning the process of data analysis with one of India’s best coaching institutes, Proschool.
- The syllabus includes essential data analytics tools and methods such as MS Excel, Python, Tableau and SQL.
- You learn how to analyse data, create strategies and data interpretation.
- The course prepares you to take on real challenges in the corporate world.
- You join one of the coaching centres near you or enrol for the course online.
- The Proschool placement portal has access to many jobs in business and financial analytics.
- You are also prepped for interview scenarios and given training in resume writing
According to a research study done by McKinsey & Company, organisations that implement the process of data analysis are 23 times more likely to perform better than their competition. These companies are also better at retaining customers and achieving their targets.
This means that professionals who analyse data are in demand, especially in business and finance. All you need is a certification in data analytics and a commitment to your career.
What skills are needed to analyse data?
- Analytical and critical thinking
- Communication and presentation skills
- Industry knowledge and awareness of the latest developments
- Proficiency in data-analysing methodologies and techniques
- Good software and technical skills
Do data analysts need a background in IT?
No. Students from any background can learn about the process of data analysis.
What are the tools used in the process of data analysis?
Some of the more popular tools are:
- MS Excel
- Power Bi
- Rapid Miner