Data Analytics refers to the set of quantitative and qualitative approaches for deriving valuable insights from data. It involves many processes that include extracting data and categorizing it in data science, in order to derive various patterns, relations, connections, and other valuable insights from it.
Today, almost every organization has morphed itself into a data-driven organization, and this means that they are deploying an approach to collect more data that is related to their customers, markets, and business processes. This data is then categorized, stored, and analyzed to make sense of it and derive valuable insights from it.
‘What gets measured, gets managed.’ – Peter Drucker
Let us start exploring the topic further by having a look at the various topics covered in this blog:
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What is Data Analytics?
The term ‘Data Analytics’ is not as simple as it appears to be. It is the most complex term when it comes to big data applications. The three most important attributes of big data include volume, velocity, and variety.
The need for Big Data Analytics springs from all data that is created at breakneck speeds on the Internet. Our digital lives will make big data even bigger, thanks to the ever-increasing penchant of individuals to see their lives ever-connected to the online world. It is estimated that by the end of the next year the cumulative data that is generated every second will amount to 1.7 MB which will be contributed by every individual on the planet.
This shows the amount of data that is generated and hence the need for Big Data Analytics tools to make sense of all that data. It organizes, transforms, and models data based on the requirements for identifying patterns in the data and drawing necessary conclusions.
The larger the size of the data the bigger the problem. So, big data may be defined as the data the size of which itself poses the problem and which needs newer ways of handling it. So, the analysis of data at high volume, velocity, and variety means that the traditional methods of working with data would not apply here.
‘Without Big Data Analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.’ – Geoffrey Moore
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Importance of Data Analytics
Data analysis is an internal arrangement done by presenting numbers and figures to management. With data analytics, the organizations will be able to make decisions on customer trends, and behavior prediction, increase the business profits and drive effective decision-making.
Once the organizations adopt data analytics they can analyze the causes of particular events based on the data, understand the objectives and directives for the business, and they can have technical insights into the business using an easy-to-understand language. Here are five reasons why businesses should adopt the data analysis method and technology.
- Better Targeting: Using Data Analytics, organizations can determine what forms of advertising reach their customers effectively and make an impact on them. Data enables the organizations to understand what methods of advertising their product have the biggest impact on the target audience and at what scale the organization could adopt such advertising.
- Bounce Rates: The data that concerns any online business is the bounce rate of their website. Lower bounce rates indicate that customers interested in the business are genuine and they wish to learn more about its offerings. On the other hand, higher bounce rates are a clear signal that something is a miss. It means that either Google or any other search engines are not identifying the business website for its relevance.
A business can have that super successful product that has global applications. Yet, if the business website has a high bounce rate it means that there is an urgent need to review the content. The content and manner in which the business and its offerings are presented need an immediate review.
- Demographics: Gathering data about who is buying products and from which location is also important. It helps them understand whether the business is reaching the right target audience or getting requests or sales from the markets it cannot serve. It can indicate the existence of a market that the business is unaware of and can mark an entry.
Analysis of data regarding the demographic reach of a business also helps understand why there is a larger interest from a specific geographic zone.
- New Innovations: It also gives the businesses a rough idea of the trends in the future in customer behavior, enabling the businesses to make futuristic inventions and create new innovative services that will put them at the top of the industry. With these inventions, businesses can maintain a sharp edge over their competitors. The good thing about these inventions is that the businesses can patent them and reap from them while at the same time raking in profits.
- Cut Costs of Operation: It will do a lot good if the management wants to have a smooth and effectively run business. With a good system, the management can determine the sectors of the business that are draining the finances unnecessarily and the areas that need more financing.
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Types of Data Analytics
Following are the four types of Data analytics:
- Descriptive Analytics: In descriptive analytics, you work based on the incoming data, and for the mining of it you deploy analytics and come up with a description based on the data.
- Predictive Analytics: Predictive analytics ensures that the path is predicted for the future course of action.
- Diagnostic Analytics: This is about looking into the past and determining why a certain thing happened. This type of analytics usually revolves around working on a dashboard.
- Prescriptive Analytics: This is the type of analytics that talks about an analysis based on the rules and recommendations in order to prescribe a certain analytical path for the organization.
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In this section, you will be familiarized with the tools used in the Data Analytics domain. Here is the list of analytical courses that you can take up for a better career in Data Analytics:
- Apache Spark: Spark is a framework for real-time Data Analytics which is part of the Hadoop ecosystem.
- Python: This is one of the most versatile programming languages that is rapidly being deployed for various applications including Machine Learning.
- SAS: SAS is an advanced analytical tool that is being used for working with huge volumes of data and deriving valuable insights from it.
- Hadoop: It is the most popular big data framework that is being deployed by the widest range of organizations from around the world for making sense of their big data.
- SQL: The structured query language (SQL) is used for working with relational database management systems.
- Tableau: One of the most popular Business Intelligence tools is tableau and it is deployed for data visualization and business analytics.
- Splunk: Splunk is the tool of choice for parsing the machine-generated data and deriving valuable business insights from it.
- R Programming: It is one of the preferred programming languages that is being used by Data Scientists. R programming is used in statistical computing and graphical applications alike.
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What is the Role of Data Analytics?
The role of a Data Analyst varies depending on the organization’s type and the extent to which they have adopted the data-driven decision-making processes. The responsibility of a Data Analyst typically includes the following:
- Creating appropriate documentation that would allow the stakeholders of the organization to understand the steps of the data analysis process and repeat the analysis if necessary.
- Demonstrating the significance of their work in the context of local, national, and global trends impacting both their organization and industry.
- Preparing reports for executives that will effectively communicate trends, patterns, and predictions using their relevant data.
- Collaborating with programmers, engineers, and organization leaders to identify opportunities for improving the process, recommending system modifications, and developing policies for data governance.
- Designing and maintaining data systems and databases which includes fixing errors in code and other data-related problems.
- Mining the data from sources and then reorganizing said data-related problems.
- Using statistics to interpret data sets, and paying particular attention to trends and patterns could be valuable for diagnostic and predictive analytics efforts.
Also, check our blog on Data Vs. Information.
Data Analytics Applications in Different Fields
Following are some of the industries that were re-defined by Data Analytics Applications:
Data Analytics in Finance
Big Data is used to track and monitor the movements in the financial market. Stock exchanges use Data Analytics to catch illegal trade practices in the stock market, banks, retail traders, hedge funds, and other aspects of the financial markets.
The finance industry relies heavily on Data Analytics for anti-money laundering purposes, “Know Your Customer”, fraud mitigation, and demand enterprise risk management.
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Data Analytics in Healthcare
In recent years, data collection in the healthcare environment has become more streamlined. Data helps enhance daily operations and improve patient care. Both historical and current datasets can be used to track trends and make predictions.
Preventive measures and tracking outcomes are now possible with the use of Data Analytics. In cases where hospitalization is required, It can help predict infection risks, deterioration, and re-admission, thereby, lowering expenses and improving patient care outcomes.
Get to know more about what is hierarchical clustering in data analytics.
Data Analytics in Marketing
It now plays a crucial part in Marketing. It aids in learning about customers and consumers with target precision. Some examples are the movie preferences on Netflix and learning about favorite food and items on e-commerce sites or food apps.
Data Analytics in HR
HR professionals have vast amounts of data in their possession but oftentimes, go unused. Using this data can help analyze human resource challenges and engage in HR Data Analytics. Just as analytics has revolutionized marketing, it has positively transformed HR too. It helps
- make smart data-driven decisions
- create HR intervention business cases
- test the effectiveness of interventions
- make the switch from an operational partner to a tactical or strategic partner
Data Analytics in IoT
The IoT market is predicted to reach $6.1 billion by 2024 as reported by Mordor Intelligence. As a result, the global market clearly seems to be in favor of IoT development and its economic potential. Combining IoT and Big Data, dubbed as IoT Data Analytics, is a major contributing component to its growth.
It was extremely challenging and expensive to analyze massive volumes of data even a decade ago. But, with time, the cost of storing data has gone down considerably and it is making huge leaps in this segment creating favorable conditions in IoT. Businesses have started to invest in IoT use cases related to Data Analytics.
Corporate giants like Microsoft, Amazon, GE, Salesforce, and SAP are already implementing Data Analytics in IoT.
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Data Analytics for Business
Data Analytics in business employs a specific set of techniques, procedures, and competencies to effectively make use of past and current business data. The purpose of doing so is to obtain insights about a business and encourage improved decision-making.
It can help a business in major aspects like personalizing a marketing pitch for a customer or identifying and mitigating business risks.
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Data Analytics vs Business Analytics vs Data Science
| Parameters | Data Analytics | Business Analytics | Data Science |
| Concept | Convert and clean raw data | Data Analysis on a business platform | Data is operated on in different domains |
| Scope | Specific issues or questions | Analyzes past data | Past data analyzed for future predictions |
| Data | Mostly unstructured | Static and structured data | Structured and unstructured data |
| Data Storage | Real-time | Stored mostly in data-warehouses | Distributed in real-time clusters |
| Tools | Python, R, MS Excel, Tableau, QlikView | SAS BI, MS Excel, Sisense, Microstrategy | Python, R, Hadoop, SAS, TensorFlow |
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Companies Using Data Analytics
Today, regardless of the industry type, there is rapid deployment of various analytical tools and technologies. It could be the tools for parsing data or the easy-to-understand visualization tools which are used for making sense of the data. Further in this blog, some of the industries that are using Data Analytics tools are discussed.
There are digital-first enterprises for whom data analytical tools are the most important weapons in their arsenal. For example, Amazon, Facebook, Google, and Microsoft cannot survive without the use of Data Analytics. Amazon widely deploys analytics in order to recommend you the right product based on the product that you bought in the past. They also make use of data in order to build customer profiles to serve them better. This way, they can provide a very customized experience to their customers.
A company like Facebook will deploy Data Analytics to find out what its users are talking about so that it can understand what products and services the users would be interested in. Since it works on ads, it needs to know the pulse of its users by making sure that the ads are up to date in terms of customization and other aspects.
Google is sitting on the mother lode of all data. They serve a few billion searches every day making it one of the most data-intensive companies on planet Earth. Due to this, the need for analytical tools at Google is inevitable. Google is also hiring the maximum number of Data Scientists.
Take a deep dive into the data analytics concepts and understand what is descriptive analytics.
How to Become a Data Analyst
Becoming a data analyst requires both academic qualifications and skills. Let us see these in detail below.
Academic Qualifications
It is recommended to have a graduation degree from a data analysis program with a high CGPA. Even if a person doesn’t have a specialization in data analysis, having a degree in mathematics, statistics, or economics from a well-reputed university, can land an entry-level Data Analyst job.
Most entry-level data analyst jobs require at least a bachelor-level degree, Higher level data analyst jobs usually guarantee higher pay and may require a master’s degree. Apart from the degree, the person interested in becoming a Data Analyst enrolls in online courses if they are interested in that.
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Skills
- Technical Skills
- Programming Languages: A Data Analyst must be proficient in at least one programming language. The programming languages that can be used to manipulate data are R, Python, C++, Java, MATLAB, PHP, and more.
- Data Management and Manipulation: A Data Analyst must be familiar with languages such as R, HIVE, SQL, and more. Building queries to extract the desired data is an essential part of Data Analytics. A Data Analyst must create accurate reports and he should have knowledge about standard tools like SAS, Oracle Visual Analyzer, Microsoft Power BI, Cognos, Tableau, etc for doing that.
- Soft Skills
A Data Analyst must provide detailed and accurate information to the management. Hence, data analysts must understand the specific user requirements, along with having a deep understanding of the data. Excellent communication skills are essential for collaborating with others to ensure that the data aligns well with the objectives.
- Practical Skills
- Mathematical Ability: A Data Analyst must have a knowledge of statistics and be comfortable with the formulae required for analyzing data to provide real-world value. As a Data Analyst, one must have a good grasp of mathematics and be able to solve common business problems also, a Data Analyst must know how to use tables, charts, graphs, and more. It is essential to be comfortable with college-level algebra, thereby making the visualization of data more appealing. Knowing linear algebra and multivariate calculus is very helpful for Data Analysts.
- Microsoft Excel: Organizing data and collecting numbers are among the main tasks of Data Analysts. Hence it is beneficial if a Data Analyst is comfortable with using Excel.
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Scope of Career in Data Analytics
A Data Analyst can expect great pay, interesting work, and excellent job security. This career is constantly changing, always different, and involves lots of attention to detail and focus on quality. A career in Data Analytics also affords outstanding opportunities for advancement.
Data Analyst is definitely an upwardly mobile position. The difference between securing mid-and senior-level depends on the experience and additional education. But because there is such high demand for Data Analysts at any level, the projected job growth is positive for each tier over the next decade ranging from 5% for a Financial Analyst to 25% for an Operations Research Analyst.
Of course, the specific growth rate depends on the role, industry, and education that can influence the salary of a Data Analyst.
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Conclusion
Data Analytics is one of the vital aspects that is driving some of the biggest and best companies forward, today. Enterprises that can convert data into meaningful insights would evidently be the winners in this hyper-competitive world. Take Uber and Airbnb, for example. Uber has disrupted the taxi-hailing business and Airbnb in the hospitality domain. For Uber, the key to a growth of $51 billion is the big data it collects and leverages for intelligent decision-making with the help of Data Analytics. Whereas, Airbnb has been using Data Analytics tools mainly to bring out a better user experience. Both these organizations are thriving for consistent growth with the power of their deep data analytical approach. Hence, any company harnessing the benefits of Data Analytics can beat its competitors without a hitch.
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