Data Analytics

Data Analytics

Gone are the days of thick stacks of paper placed one over the other, containing all organization-related information. This is the new world of Data Analytics.

Far from imagination is the fact that these stacks of paper were read, organized and analysed ‘manually’ to understand industry trends & use it to devise newer methods of functioning.

This is how history looked like for all organizations trying to run faster in the race of corporate dynamism, fuelled by an increasing emphasis on using & analysing data.

Revolutionized by the advent of Data Analytics & its underlying methods, using data is much more convenient, flawless & insightful than it was before.

What is Data Analytics?

Data Analytics is the scientific use of raw data which is collected, collated, organized & analysed to draw insights & conclusions about the information contained in the data. Facilitated by several data analytics tools & software, the science of data analytics is an umbrella term for its application in several industries including healthcare, gaming, manufacturing, human resource management etc.

Data Analytics is useful for revealing existing trends & predicting new trends in an industry to enable organizations to engage in strategic planning & implement new business processes & techniques for efficient functioning & better results.

Data Analytics is where statistics is married to information technology in business. What follows suit from this union is accuracy in data warehousing, mining, management & analysis – a successful betrothal.

Types of Data Analytics

Contained within the larger field of data analytics are its four major types, based on their utility in business & goal of application:

    • Descriptive Analytics – This is the type of data analytics that helps answer the question “what has happened/occurred?” The primary usage of descriptive analytics is seen in the development of Key Performance Indicators (KPIs) to understand how successful the Return on Investment (ROI) is for various industries & their underlying business decisions & strategic planning.
    • Diagnostic Analytics – The type of data analytics that helps answer the question “why things happened the way they did?” Once descriptive analytics has helped answer the ‘what’, diagnostic analytics helps ‘diagnose’ & ‘understand’ why something happened. The primary usage of diagnostic analytics lies in identifying anomalies, collecting data about them & using statistical computations to explain their existence.
    • Predictive Analytics – The type of data analytics that answers “What does the Future look like?” Predictive analytics looks at existing data & helps draw relevant insights/predictions about future trends & what will work better, using statistical & machine learning techniques like decision trees, regression analysis, data modelling etc.
    • Prescriptive Analytics – The type of data analytics that answers “What are the prescribed next steps?” Prescriptive analytics makes use of insights generated from Predictive Analytics to remove anomalies & develop business strategies, enabled by the tenets of scientific computation of data.

All these primary types of data analytics enable businesses in their operations, systems & processes to improve decision-making, predictive abilities & resource allocation strategies to ensure best ROIs.

What does a Data Analyst do?

The job of a data analyst revolves around:

  1. Design & maintenance of data management tools & software.

  2. Mining data from various sources & collating it to properly organize & manage it to draw insights

  3. Use statistical computations & techniques – inferential & descriptive – to interpret collated data & generate insights

  4. Communicate the insights generated for other stakeholders to understand

  5. Work collaboratively with stakeholders & partners to suggest & implement improved business operations, create clear KPIs & improve performance.

Trends in Data Analytics?

Data Analytics is a revolution of the past & continues to indicate its intention to revolutionize the future. Following are some upcoming trends in Data Analytics:

  1. AI-driven Data Analytics is all set to revolutionize the future by employing Artificial Intelligence, Machine Learning, Reinforcement Learning, Natural Language Processing etc for handling & integrating complex data.

  2. Blockchain has helped improve predictive analytics & make it more error-proof than before. Blockchain allows for data to be protected & analytics to be more structured for usage of clean, error-free data to make error-free predictions.

  3. Data Fabric enables creation of a ‘fabric of data’ allowing different sources of information to be connected to each other in meaningful ways to allow efficient networking & use of multiple sources of data for apt decision-making through analytics.

  4. Creation of Data Marketplaces which allow selling & buying of third party data from a single monetized ‘marketplace’ to consolidate & make data centrally available.

Conclusion

Data analytics, more than a growing fad, is a necessity. It has helped businesses use data to make predictions & draw relevant insights for business improvements.

An important question now left to ponder over is:
Based on the pros & cons of the existence of data analytics in the market, how openly should it be allowed to spread its wings?

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