What is Data Analytics?

Data Analytics

About Data Analytics

• Data Analytics is the process of developing actionable decisions or recommendations for actions based on insights generated by historical data
• Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information.
• This information can then be used to optimize processes to increase the overall efficiency of a business or system.
• According to the Institute for Operations Research and Management Science (INFORMS), analytics represents the combination of computer technology, management science techniques, and statistics to solve real problems.
• Example: E-Commerce Companies often record the detail of Orders placed by Consumers such as Order Date, Order Ship Date, Order Delivery Date, Inventory Details, and much more such that they can analyze the data further to optimize the delivery time and reach the customer early to maintain the management of supply chain.
• Data analytics is important because it helps businesses optimize their performances.
• It helps Management to make decisions for improving company growth efficiently and optimizing overall productivity of the company which can lead to better products and services.

What are Data Analytics Steps?

The process of Data Analytics involves certain steps as below:

• The first major step is Data Discovery and formation.
• The second step is Data Collection. This can be done through various sources such as Websites, Personnel, Computers, etc.
• Once Data is collected next step is to organize Data into different databases or spreadsheets so that we can analyze it later.
• Once Data is organized next step is Cleaning the Data (Data Preparation step) using various tools. This step helps removes unwanted noise from the data for proper analysis.

Types of Data Analytics?

There are fourmajor types of Data Analytics:

1. Descriptive (or Reporting) Analytics:

• This type tells us “What has happened over a given period of time in the organization.”
• Understanding some underlying trends and causes of such occurrences.
• Example: Have Sales gone up this month? What was the Profit last month?
• This includes Business Reporting, Dashboards, Scorecards, and Data Warehousing.
• This gives us Well-defined business problems and opportunities.

2. Diagnostic Analytics:

• This type mostly conveys ‘Why something happened?’.
• This involves more diverse data inputs and hypothesizing.
• Example: Did Sales of Warm Clothes increase in the Winter season. Does any latest Marketing campaign affect Sales for this month?

3. Predictive Analytics:

• This type will tell us more about “What will happen in the future?”
• Why it will happen?
• Example: What is the Weather tomorrow? Forecasting of Stock market prices.
• This includes Data Mining, Text Mining, Web/Media Mining, and Forecasting.
• This gives us Accurate projections of Future Events and Outcomes.

4. Prescriptive Analytics:

• This type suggests “What should you do?”
• Why should you do it?
• Example: What improvements do we suggest to improve the overall Sales to generate more profit.
• This includes Optimization, Stimulation, Decision Modeling, and Expert Systems.
• This gives us the best possible decisions and course of action.

Why Data Analytics is so popular nowadays?

Data Analytics has become the technology driver of this decade. Companies such as IBM, SAP, Teradata, SAS, Oracle, Microsoft, Amazon, Facebook, Dell, and others are creating new organizational units focused on analytics that help businesses become more effective and efficient in their operations.

Decision-makers are using more advanced tools to support their work. Even nowadays consumers use analytics tools to make direct or indirect decisions on routine activities such as E-commerce, Shopping, Healthcare, Travel, Supply Chain Management, Finance, Marketing, and Entertainment.

The field of Business Intelligence and Business Analytics (BI & BA) has evolved rapidly to become more focused on innovative applications for extracting knowledge (Data Mining) and insights from data streams that were not even captured sometime back, much less analyzed in a significant way.

New applications turn up daily in health care, sports, travel, entertainment, supply chain management, utilities, and virtually every industry imaginable. The term “Analytics” has become mainstream.

Indeed, it has already evolved into other terms such as Data Science, the latest incarnation is deep learning and IoT.

Who Is Using Data Analytics?

• Data analytics has been adopted by several sectors, such as the travel and hospitality industry, where turnarounds can be quick.
• Healthcare is another sector that combines the use of high volumes of structured and unstructured data and data analytics can help in making quick decisions.
• Similarly, the retail industry uses copious amounts of data to meet the ever-changing demands of shoppers.

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