The World is speeding up and is in acceleration mode. There are enormous amounts of data types and volumes which are also increasing and hence for more data there is a demand for more insight, at a reasonable cost. As of 2012, there were 2.8 Zettabytes of data created and by 2020 the volumes of data would grow to 40 ZB, which is huge. Data is the new coal. Data sets are so complex and large that it becomes elusive or impossible to handle them with your usual database management.
We at Accemy analyze these data and help you handle it with Data Analytics. We also help enterprises observe “unstructured” data like voice recordings, emails, tweets, photos and emails to find patterns. With data analytics, you can get precise answers for hard-to-solve problems, get timely insights to make decisions about fleeting opportunities and uncover new growth opportunities – all with Accemy. Data Analytics has endless applications linked to it and if utilized properly, it can lead to bigger and better opportunities. Accemy uses data analytics to target and better analyze our users by linking together data from their social media accounts and help industries track and analyze their supply chain delivery routes to optimize their processes, and combine this data with live traffic updates. In the healthcare sector too, data analytics can help find new cures for deadly diseases, predict diseases before the emergence of symptoms and optimize treatment. We use Data Analytics for:
- Cost reduction Faster,
- better decision making
- New products and services
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To avoid data lakes from becoming swamps, companies are focusing on business- driven applications. Today, businesses are shifting from the "build it and they will come" data lake approach to a business-driven data approach and the world requires operational and analytics capabilities to address interface to devices, process claims and customers in real time at an individual level. Like for instance, an e-commerce site must provide price checks and individualized recommendations in real time. Media companies though set-top boxes are now personalizing content served. Healthcare organizations are able to block fraudulent claims and process valid claims by combining operational systems with analytics. Ride-sharing companies and auto manufacturers are interoperating at scale with drivers and the cars. Delivering these use cases requires an agile platform that can provide both operational and analytical processing to increase value from additional use cases that span from back-office analytics to front office operations.
Data agility separates losers and winners. Analytic and processing models are evolving to provide a higher level of agility as businesses understand data agility, the ability to take business action and understand data in context is the source of competitive advantage and not just simply having a huge data lake. Software development has become agile where DevOps provides continuous delivery. The emergence of agile processing models is enabling the instance of data to support global messaging, database and file-based models, interactive analytics and batch analytics. A single instance of data can support a broader set of tools in agiler analytic models. The end result is an agile application and development platform that supports the broadest range of analytic and processing models.
In organizations, data analytics enables professionals to convert quantitative and statistical analysis and extensive data into powerful insights that can drive efficient decisions. Also, the rate at which the data is being analyzed, businesses are able to keep tabs on the customer trends in near real time. Thus, with data analytics, businesses can now base their strategies and decisions on data rather than on gut feelings. Hence data analytics promises increased profits and reduced costs.
Moreover, it can be used to exploit new opportunities for revenue, optimize marketing, improve employee productivity, increase the quality of customer service and promote the efficiency of operations. Data analytics is usually performed on large data sets and the information can also be used to create a competitive advantage over your rivals.