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Table of Contents
Big Data adoption helps organizations simplify and reduce the costs of taking data from the source and converting it into actionable insights for regulatory reporting for business results into undiscovered possibilities to critically analyze each piece of information before taking a business decision.
Big data analytics allows examining voluminous data to obtain actionable insights regarding correlations, market trends, customer preferences and other useful information.
Successful use of analytics requires great emphasis with a clear focus of what business activity or business processes they might impact and what particular business outcomes they might improve. Several Big Data Analytics companies are helping business focus to identify and prioritize the analytics most relevant to improve reaching business goals.
Advantages of Big Data Analytics
Big data analytics solutions help companies leverage data to identify and explore new opportunities, resulting in strategic decision making, streamlined operations, improved bottom-line results and satisfied customers.
Reducing Costs: Big data technologies such as Hadoop and cloud-based analytics provide advantage related to cost when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business.
Faster, better decision making: With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately – and make decisions based on what they’ve learned.
New products and services: Big data analytics allows organizations to create a roadmap for designing and developing new products and services based on customers’ needs and preferences. Analytics empowers companies to create new products
Data-driven decision making
The availability of voluminous data allows organizations to make strategic decisions based on thorough analysis of evidence rather than intuition. This makes it relatively simpler to evaluate opportunities based on potential cost reduction and revenue growth. New solutions allow discovery of new business opportunities and identification of the best areas for future investment.
Type of Big Data Analytics
Prescriptive Analytics:
Focused to help you find the best course of action for a given situation. Prescriptive analytics is related to both descriptive and predictive analytics. It uses optimization and simulation algorithms to provide possible outcomes of a given situation.
Prescriptive analytics comes across as the perfect analytics tool when it comes to leveraging a future opportunity or minimizing a future risk. In practice, prescriptive analytics can process new data to improve accuracy of predictions, eliminating guesswork and providing better decision options.
Predictive Analytics:
With a variety of statistical algorithms, modeling, data mining and machine learning techniques, Predictive Analytics aims to identify the future outcomes based on historical data. Predictive analytics aims to forecast the likelihood of what might happen in the future; however it cannot predict the future. Several businesses right from mid-sized to large enterprises are turning to predictive analytics to improve bottom line results and competitive advantage.
In today’s highly volatile markets, businesses can gain the much needed competitive differentiation by discovering insights about the future.
Descriptive Analytics:
Descriptive Analytics is the simplest and the most basic form of analytics to find out the reasons behind success or failure in the past. Descriptive analytics is a preliminary stage of data processing providing historical data to gain useful information for further analysis. The ‘Past’ refers to any particular time in which an event had occurred to understand how they will impact future outcomes.
Big Data Implementation
Here are examples of Big data implementation and Big data analytics solutions helping businesses across diverse industries.
Retail:
Data helps us understand not only how customers are buying, but also data analytics will help this become even more accurate. The retailers are utilizing data-first towards understanding the buying behavior of customers, syncing with products, and planning marketing strategies to sell their products to register increased profits.
Healthcare:
Hospitals are now increasingly using Big Data in healthcare to provide the best clinical support, reduce the cost of care measurement and manage the population of high-risk patients. Hadoop applications help healthcare experts analyze incessant data pouring in real time from diverse sources such as financial data, payroll data, and electronic health records
Insurance:
Big data analytics offers comprehensive understanding of customer behaviors from various sources to anticipate future behaviors, offer relevant products and identify the right segmentations.
Big Data Implementation Challenges
Talent Deficit
Demand for data scientists is extremely high within most industries, whereas the supply is acute. The shortage is especially high in sectors such as auto and industrial equipment. Companies often need to assess the practical advantages of building, buying, or borrowing analytic talent to develop the capabilities they need.
Operations-related Issues
There are several issues such as incorporating analytics into high-level decision-making and even changing the perception of employee mentality to rely more on actionable data generated by analytics and not on intuition.
Securing Big Data
One of the most important challenges in Big Data Implementation continues to be security. Big data stores contain sensitive and important data that can be attractive for hackers. Several companies are using additional security measures such as identity and access control, data segmentation, and encryption.
Big Data: The Way Ahead
Based on the demographics and personal behavior patterns, big data helps marketers develop assumptions based on analytical data about their consumers. Big data helps you come across crucial metrics related to consumer behavior.
Here are some of those important metrics:
- Consumer Acquisition
- Customer Retention
- Customer Satisfaction Index
With big data analytics, the shift is now from processing data to optimizing insights from the data. Quicker availability of big data allows decision makers on focus on budgeting and performance monitoring and the discovery and development of new business opportunities.
