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What is Data Analytics

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Author: Palak Kumar

What is Data Analytics

Data analytics is the process of examining datasets to draw conclusions about the information they hold within. A lot of companies generate a huge amount of data which can hold valuable information. For example, companies may collect information on audience’s demographics, interests, behaviours, purchase patterns etc. It is practically impossible for humans to dive into this data and gather useful information due to the vast size of the data. Data analytics techniques can help uncover the patterns from raw data and derive valuable insights from it which can provide a lot of value to businesses. Data analytics can help businesses get valuable insights to improve performance and understand their customers. They can help businesses get an accurate profile of their customers, how they think, what their preferences are, and where they will spend their money. This information helps companies monetize the data and win in the marketplace.

Data analytics is an intersection of Information technology, business, and statistics. These fields are combined for businesses and organizations to succeed. The primary steps of the data analytics process are data mining, data management, statistical analysis, and data presentation. The importance and balance of these steps depend on the data being used and the goal of the analysis.
  • Data mining is an essential process for many data analytics tasks. It is extracting data from unstructured data sources. It is generally the most time-intensive step in the data analysis pipeline.
  • Statistical analysis of data is the heart. This is how the insights are created from data. Both statistics and machine learning techniques are used to analyse data. Big data is used to create statistical models that reveal the trends in data. The final step in most of the data analytics processes is data presentation. This step allows insights to be shared with stakeholders.
  • Data visualization is often the most important tool in data presentation. Compelling visualizations tell the story in the data that help executives and managers understand the importance of these insights.

Sources of Data

The types of data that companies collect can broadly be put into three categories. First party data collected by the company from its own customers, second party data that was obtained from a known organization or third-party data bought from the marketplace. In the past, companies had access to internal data sources such as their customer profiles, sales records and customer services provided etc. They would collect data through email champaigns and customer surveys. However, with the advent of Internet connectivity, Internet of Things (IOT), smartphones and other technologies, a lot more data is now available. Companies can extract information from the websites visited by customers through tracking of cookies, tracking GPS location of customers, through social media accounts and a lot more sources.

Types of Data Analytics

Once data is collected, there are 4 primary types of data analytics that can be performed to help you make data driven decisions. Each type of data analytics has a different goal and a different place in the data analysis process. Types of Data Analytics
  • Descriptive analytics: It helps in answering questions about what happened. These techniques provide a summary view of large datasets to describe outcomes to stakeholders of the business. They help in developing Key Performance Indicators (KPI) that can be helpful in tracking successes or failures. They also give information on metrics such as return on investment (ROI) that care vital for many industries and investors. The metrics obtained can also help track performance and development in specific industries. Descriptive data analytics requires the collection of relevant data, processing of the data, analysis of the data, and visualization of the data. It uses 2 primary techniques, namely data aggregation, and data mining. The year over year pricing changes, month over month sales growth, total revenue per subscriber, number of users, social metrics such as likes, and tweets are all descriptive analysis.
  • Diagnostic analytics: It helps in answering questions about why things happened. These techniques supplement basic descriptive analytics. They take the findings from descriptive analytics and dig deeper to find the cause. The performance indicators are then further investigated for discovering why they got better or worse. This process generally occurs in three steps:
    1. Identifying anomalies in the data. These may be unexpected changes in a metric or a particular market.
    2. Data that is related to these anomalies are then collected.
    3. Statistical techniques are used to find relationships and trends that explain these anomalies.
    It uses techniques such as drill-down, data discovery, data mining, and correlations.
  • Predictive analytics: It helps to answer questions about what will happen in the future. These techniques use historical data to identify trends and determine if they are likely to reoccur. Predictive analytical tools provide valuable insight into what may happen in the future and its techniques include a variety of statistical and machine learning techniques, such as neural networks, decision trees, and regression. Predictive models are used by businesses to better understand their customers and predict buying patterns, potential risks, and likely opportunities. It is used in sectors such as retail, weather, health, insurance, and more.
  • Prescriptive analytics: It helps answer questions about what should be done. We can make data-driven decisions by using prescriptive analysis. This allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning strategies that can be used to look for patterns in large datasets. By analysing past decisions and events, the likelihood of different outcomes can be estimated. “Waymo”, Google’s self-driving car is an example of prescriptive analytics in action. It makes millions of calculations on every trip that helps it to decide when to turn and whether to slow down or speed up during lane changes.

In summary, data has become one of the most valuable resources and companies need to leverage the collection and use of data to outsmart the competition and win in the global competitive marketplace.

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