Data Science and Data Analytics are two buzz words of the year. Today, data is more than oil to the industries. Data is collected into raw form and processed according to the requirement of a company and then take this data for the decision making purpose. All this process, helps the business to grow in the market. But, who will do this work? Who will process the data? etc. Everything is done by a Data Analytics and a Data Scientist.
Data or information is in raw format. With increasing data size, it has become a need for inspecting, cleaning, transforming, and modeling data with the goal of finding useful information, making conclusions, and supporting decision making. This process is known as data analysis.
Data mining is a particular data analysis technique where modeling and knowledge discovery for predictive rather than purely descriptive purposes is focused. Business intelligence covers data analysis that relies heavily on aggregation, focusing on business information. In statistical applications, some people divide business analytics into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA).
EDA focuses on discovering new features in the data and CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics does forecasting or classification by focusing on statistical or structural models while in text analytics, statistical, linguistic and structural techniques are applied to extract and classify information from textual sources, a species of unstructured data. All are varieties of data analysis.
So, the Data wave has changed the ways in which industries function. With Big Data has emerged the requirement to implement advanced analytics to it. Now experts can make more accurate and profitable decisions.
Looks like confused between Data Science and Data Analytics?
The analysis is an interactive process of a person tackling a problem, finding the data required to get an answer, analyzing that data, and interpreting the results in order to provide a recommendation for action.
A reporting environment or business intelligence (BI) environment involves calling and execution of reports. So, outputs are then printed in the desired form. Reporting refers to the process of organizing and summarizing data in an easily readable format to communicate important information. Reports help organizations in monitoring different areas of performance and improving customer satisfaction. In other words, you can consider reporting as the process of converting raw data into useful information, while analysis transforms information into insights.
1. Business Understanding
The very first step consists of business understanding. Whenever any requirement occurs, firstly we need to determine the business objective, assess the situation, determine data mining goals and then produce the project plan as per the requirement. Business objectives are defined in this phase.
2. Data Exploration
The second step consists of Data understanding. For the further process, we need to gather initial data, describe and explore the data and verify data quality to ensure it contains the data we require. Data collected from the various sources is described in terms of its application and the need for the project in this phase. This is also known as data exploration. This is necessary to verify the quality of data collected.
3. Data Preparation
Next, come Data preparation. From the data collected in the last step, we need to select data as per the need, clean it, construct it to get useful information and then integrate it all. Finally, we need to format the data to get the appropriate data. Data is selected, cleaned, and integrated into the format finalized for the analysis in this phase.
4. Data Modeling
Once data is gathered, we need to do data modeling. For this, we need to select a modeling technique, generate test design, build a model and assess the model built. The data model is build to analyze relationships between various selected objects in the data, test cases are built for assessing the model and model is tested and implemented on the data in this phase.
5. Data Evaluation
Next is data evaluation, where we evaluate the results from the last step, review the scope of error, and determine the next steps to perform. We evaluate the results of the test cases and review the scope of errors in this phase.
The final step in the analytic process is deployment. Here we need to plan the deployment and monitoring and maintenance, we need to produce a final report and review the project. In this phase, we deploy the results of the analysis. This is also known as reviewing the project.
We call the above process as business analytics process.
There are 4 types of techniques used for Data Analysis are-
1. Descriptive Analysis
With the help of descriptive analysis, we analyze and describe the features of a data. Descriptive Analysis deals with the summarization of information. Descriptive Analysis, when coupled with visual analysis provides us with a comprehensive structure of data.
In the descriptive analysis, we deal with the past data to draw conclusions and present our data in the form of dashboards. In businesses, Descriptive Analysis is used for determining the Key Performance Indicator or KPI to evaluate the performance of the business.
2. Predictive Analysis
With the help of predictive analysis, we determine the future outcome. Based on the analysis of the historical data, we are able to forecast the future. It makes use of descriptive analysis to generate predictions about the future. With the help of technological advancements and machine learning, we are able to obtain predictive insights about the future.
Predictive Analytics is a complex field that requires a large amount of data, skilled implementation of predictive models and its tuning to obtain accurate predictions. This requires a skilled workforce that is well versed in machine learning to develop effective models.
3. Diagnostic Analysis
At times, businesses are required to think critically about the nature of data and understand the descriptive analysis in depth. In order to find issues in the data, we need to find anomalous patterns that might contribute towards the poor performance of our model.
With diagnostic analysis, you are able to diagnose various problems that are exhibited through your data. Businesses use this technique to reduce their losses and optimize their performances. Some of the examples where businesses use diagnostic analysis are –
- Businesses implement diagnostic analysis to reduce latency in logistics and optimize their production process.
- Using diagnostic analysis in sales to update marketing strategies that would otherwise lead to a fall in revenue.
4. Prescriptive Analysis
Prescriptive Analysis combines the insights from all of the above analytical techniques. It is referred to as the final frontier of data analytics. Through the details provided by the descriptive and predictive analytics, prescriptive analytics allows the companies to make decisions based on them. It makes heavy usage of artificial intelligence in order to facilitate companies into making careful business decisions.
Major industrial players like Facebook, Netflix, Amazon, and Google are using prescriptive analytics to make key business decisions. Furthermore, financial institutions are gradually leveraging the power of this technique to increase their revenue.
We have already seen characteristics of Big Data like volume, velocity, and variety. Let us now see characteristics of Data Analytics which make it different from traditional kind of analysis.
Data analysis has the following characteristics:
There might need to write a program for data analysis by using code to manipulate it or do any kind of exploration because of the scale of the data.
It means progress in an activity compel by data and program statements describe the data that match and the processing require rather than taking steps of defining a sequence. Many analysts use a hypothesis-driven approach to data analysis, Data can use the massive amount of data to drive the analysis.
For proper and accurate analysis of data, it can use a lot of attributes. In the past, analysts dealt with hundreds of attributes or characteristics of the data source, with Big Data there are now thousands of attributes and millions of observations.
As whole data is broken into samples and samples are then analyzed, data analytics can be iterative in nature. More compute power enables iteration of the models until Data analysts are satisfied. This has led to the development of new applications designed for addressing analysis requirements and time frames.
Following are some of the most popular applications of data analysis –
Fraud Detection & Risk Analytics
In Banking, Data Analytics is heavily utilized for analyzing anomalous transaction and customer details. Banks also use data analytics to analyze loan defaulters and credit scores for their customers in order to minimize losses and prevent frauds.
Optimizing Transport Routes
Companies like Uber and Ola are heavily dependent on data analytics to optimize routes and fare for their customers. They use an analytical platform that analyzes the best route and calculates percentage rise and drop in taxi fares based on several parameters.
Providing Better Healthcare
With the help of data analytics, hospitals and healthcare centers are able to predict early onset of chronic diseases. They are able to predict diseases that might occur in the future and help the patients to take early action that would help them to reduce medical expenditure.
Managing Energy Expenditure
Public-sector energy companies are using data analytics to monitor the usage of energy by households and industries. Based on the usage patterns, they are optimizing energy supply in order to reduce costs and cut down on energy consumption.
Improving Search Results
Companies like Google are using data analytics to provide search results to users based on their preferences and search history. Furthermore, companies like Airbnb use search analytics to provide the best accommodation to its customers. Amazon also makes use of search analytics to provide recommendations to customers.
Optimization of Logistics
Various companies are relying on Big Data Analytics to analyze supply chains and reduce latency in logistics. Companies like Amazon are using consumer analytics to analyze their requirements and send them products without any latency.
In order to have a great analysis, it is necessary to ask the right question, gather the right data to address it, and design the right analysis to answer the question. Then only analysis we can call as correct and successful. So, let’s discuss this in detail.
The framing of a problem means ensuring that must ask important questions and layout critical assumptions. For example, is the goal of a new initiative to drive more revenue or more profit? The choice leads to a huge difference in the analysis and actions that follow. Is all the data required available, or is it necessary to collect some more data? Without framing the problem, the rest of the work is useless.
For a great analysis, we frame the problem correctly. So, this includes assessing the data correctly, developing a solid analysis plan, and taking into account the various technical and practical considerations in play.
We can analyze any business problem for 2 issues:
How the problem is statistically important for decision making. Statistical significance testing takes some assumptions and determines the probability of happening of results if the assumptions are correct.
It means how the problem is related to business and its importance. Always put the results in a business context as part of the final validation process.
Data Analytics tutorial is incomplete without discussing the skills. In today’s world, there is an increasing demand for analytical professionals. It is taking time for academic programs to adapt and scale to develop more talent.
All the data collected and the models created are of no use if the organization lacks skilled Data analysts. A Data analyst requires both skill and knowledge for getting good data analytics jobs.
Must Read Data Scientist vs Data Analyst — The Hot Debate for a Promising Career
To be a successful analyst, a professional requires expertise on the various data analytical tools like R & SAS. He should be able to use these business analytics tools properly and gather the required details. He should also be able to take decisions which are both statistically significant and important to the business.
Even if you know how to use a data analysis tool of any type, you also need to have the right skills, experience and perspective to use it. An analytics tool may save a user some programming but he or she still needs to understand the analytics that occurs. Then only we can call a person as a successful Data analyst.
Business people with no analytical expertise may want to leverage analytics, but they do not need to do the actual heavy lifting. The job of the analytics team is to enable business people to drive analytics through the organization. Let business people spend their time selling the power of analytics upstream and changing the business processes they manage to make use of analytics. If analytics teams do what they do best and business teams do what they do best, it will be a winning combination.
Now let us discuss the required technical and business skills.
Technical skills for Data analytics –
- Packages and Statistical methods
- BI Platform and Data Warehousing
- Database design
- Data Visualization and munging
- Reporting methods
- Knowledge of Hadoop and MapReduce
- Data Mining
Business Skills Data analytics –
- Effective communication skills
- Creative thinking
- Industry knowledge
- Analytic problem solving
Data Analytics will get a new boost in the market. According to research, the global predictive analytics market is expected to grow $14.95 billion by 2023. So, what are you waiting for? Now, its time to uplift your career with Big data Analytics. Now, its time to boost your career with Big data Analytics.
Why data analytics is gaining hype in the 21st century? ›
Based on the analysis of the historical data, we are able to forecast the future. It makes use of descriptive analysis to generate predictions about the future. With the help of technological advancements and machine learning, we are able to obtain predictive insights about the future.Why is there a sudden hype over big data analytics? ›
Although the term Big Data misleads most into thinking it only handles storage and management problems, the real reason behind its tremendous success lies in Analytics. Companies can now gather and process information instantaneously and with unparalleled accuracy.Why has data analytics become so popular? ›
With latest analytics tools, analysis of data becomes easier and quicker. This, in turn, leads to faster decision making saving time and energy.Why data analytics is important in today's world? ›
The role of data analytics is to extract and catalogue data, so that organisations can pinpoint and evaluate relationships, patterns and trends so they can glean insights and draw conclusions based on the data and use these to make informed decisions. Such data can include information on: customers. competitor ...Why data analytics is the future of everything? ›
Business intelligence and analytical tools will continue to grow in the future. Data analytics will become necessary in business and enable individuals to extract information and create reports. Overall efficiency will increase, which will help in reducing human-related limitations.Why is big data analytics so important in today's digital era? ›
Why is big data analytics important? Big data analytics helps organizations harness their data and use it to identify new opportunities. That, in turn, leads to smarter business moves, more efficient operations, higher profits and happier customers.Why data has grown so rapidly in the recent years? ›
Some of the key drivers in the growth of big data include huge volumes of data and futuristic technology space. In addition to this, software development services like CodeLantic are expected to continue to innovate and develop unorganised data.Why is data growing so fast? ›
Evolution of the data landscape
But the real reason why we're seeing this increase is the growing utility of data analytics and automated responses to analytic decisions. The past decade's data explosion created a virtuous circle of data analysis and action, leading to new insights, data creation, and data analysis.
- Clear business goals the company aims to achieve using Big Data mining.
- Relevancy of the data sources to avoid duplicates and unimportant results.
- Completeness of the data to ensure all the essential information is covered.
Data analysis allows you to take informed decisions. I've always been fascinated by programming languages. I programmed in C and C++ during my college days and now, as a data miner, I need to know a lot more programming languages. Right now, I'm in the process of learning R and it's just so much fun!
When did data analytics become popular? ›
In the late 1960s, analytics began receiving more attention as computers became decision-making support systems. With the development of big data, data warehouses, the cloud, and a variety of software and hardware, data analytics has evolved, significantly.Why data is becoming important today? ›
Data allows organizations to measure the effectiveness of a given strategy: When strategies are put into place to overcome a challenge, collecting data will allow you to determine how well your solution is performing, and whether or not your approach needs to be tweaked or changed over the long-term.What is data analytics and why is it important? ›
Data analytics is the process of storing, organizing, and analyzing data for business purposes. This process is used to inform key decision-makers and allows them to make important strategic decisions based on data, rather than hunches.How is data analysis changing the world? ›
A career in data is exciting because data is being used in new ways every day, and the smallest pockets of information are capable of unleashing insights that change the world. Companies can use data to impact the lives of millions, from saving rare species from extinction to creating breakthroughs in medical science.Does data analytics have a good future? ›
Data Analysts are in high demand, and their salaries reflect this. Additionally, many Data Analysts love the opportunity to travel and work remotely or to migrate to a new city or country. Even if the work doesn't appeal to you, the benefits, income, and job stability you'll enjoy are well worth it.What is the future growth of data analytics? ›
The data analytics industry is projected to create over 11 million jobs by 2026 and increase investments in AI and machine learning by 33.49% in 2022 alone.Why big data is becoming one of the most talked about technology trends nowadays? ›
It has become a key technology for doing business due to the constant increase of data volumes and varieties, and its distributed computing model processes big data fast. An additional benefit is that Hadoop's open-source framework is free and uses commodity hardware to store and process large quantities of data.How did data analytics improve the modern technology? ›
Improved Decision Making
Companies can use the insights they gain from data analytics to inform their decisions, leading to better outcomes. Data analytics eliminates much of the guesswork from planning marketing campaigns, choosing what content to create, developing products and more.
LOS ANGELES, Dec. 16, 2022 (GLOBE NEWSWIRE) -- The Global Data Analytics Market Size accounted for USD 31.8 Billion in 2021 and is projected to occupy a market size of USD 329.8 Billion by 2030 growing at a CAGR of 29.9% from 2022 to 2030.How quickly is data growing? ›
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How fast is the data industry growing? ›
TOKYO, Dec. 15, 2022 (GLOBE NEWSWIRE) -- The Global Big Data Market Size accounted for USD 163.5 Billion in 2021 and is projected to occupy a market size of USD 473.6 Billion by 2030 growing at a CAGR of 12.7% from 2022 to 2030.How fast is big data growing? ›
Big Data Market Size, Global Industry Forecast
The global big data market in terms of revenue was estimated to be worth $162.6 billion in 2021 and is poised to reach $273.4 billion by 2026, growing at a CAGR of 11.0% from 2021 to 2026. The Big Data industry is driven by a sharp increase in data volume.
Data analytics can help an organization understand risks and take preventive measures. For instance, a retail chain could run a propensity model — a statistical model that can predict future actions or events — to determine which stores are at the highest risk for theft.What is the impact of big data analytics in the society? ›
The focus of big data in society is moving towards the value that organizations can extract from it. Data science is a field that has grown from this, incorporating disciplines including programming; statistics; data engineering, mining, preparation, and visualization; machine learning; and predictive analytics.What is the most important aspect of data analysis? ›
“The most important aspect of a statistical analysis is not what you do with the data, it's what data you use” (survey adjustment edition)How can data analytics influence people? ›
Data analytics allows you to understand employee and customer interactions, and work with your IT department to improve those interactions. As IT connects with the marketing teams, it helps achieve audience results and goals. So the company better allocates budgets based on customer response.Why are you interested in data analytics answer? ›
“A data analyst's job is to take data and use it to help companies make better business decisions. I'm good with numbers, collecting data, and market research. I chose this role because it encompasses the skills I'm good at, and I find data and marketing research interesting.”Is data analytics in high demand? ›
Job Prospects for Data Analysts
Market Research Analysts, Operations Research Analyst, and Management Analysts are all ranked in the top 20. The Global Big Data Analytics Market is expected to be worth $105 billion by 2027, which reflects a more than 12% growth from 2019 to 2027.
Data analytics converts raw data into actionable insights. It includes a range of tools, technologies, and processes used to find trends and solve problems by using data. Data analytics can shape business processes, improve decision-making, and foster business growth.How data analytics is used in real life? ›
Most search engines like Google, Bing, Yahoo, AOL, Duckduckgo, etc. use data analytics. These search engines use different algorithms to deliver the best result for a search query, and they do so within a few milliseconds. Google is said to process about 20 petabytes of data every day.
Why is there a sudden boom in the field of data science? ›
So this is the 1ˢᵗ reason: QUANTITY of historical data available at present and the QUALITY of data. The researches in the field of Electronics are also increasing day by day. Computer and devices are just becoming faster and faster just in no time. People nowadays can do their whole work in mobile devices or tablet.What happened big data hype? ›
The tech consulting firm Gartner dropped big data from its famous “hype cycle” report in 2015, and it hasn't returned. That isn't because companies were giving up on the concept of mining vast data sets for insights, the company clarified.Is big data overhyped? ›
The Hype Factors
Given the scale and complexity of Big Data solutions, certain inaccuracies and subsequent defects that affect quality adversely are to be expected. As a result, this is not a technology that can be relied upon entirely, when making critical decisions based on data analysis.
Data Science enables companies to efficiently understand gigantic data from multiple sources and derive valuable insights to make smarter data-driven decisions. Data Science is widely used in various industry domains, including marketing, healthcare, finance, banking, policy work, and more.Is data analysis a growing field? ›
Data analytics is a fast-growing field and it will continue to grow over the next decade. The U.S. Bureau of Labor Statistics (BLS) estimates 22% growth through 2030, which is considered much faster than average.How data science is changing the world? ›
Data science is being applied not only as a corporate tool but also for the benefit of society as a whole, from preventing blindness and treating addiction to drugs and alcohol to eradicating poverty.