Big Data
Big Data Analytics
Big data analytics is where advanced analytic techniques operate on big data sets. Hence, big data analytics is really about two things—big data and analytics—plus how the two have teamed up to create one of the most profound trends in business intelligence (BI) today.
Change is rampant in business, as seen in the multiple “economies” we’ve gone through in recent years. Analytics helps us discover what has changed and how we should react.Second, as we crawl out of the recession and into the recovery, there are more and more business opportunities that should be seized. To that end, advanced analytics is the best way to discover new customer segments, identify the best suppliers, associate products of affinity, understand sales seasonality, and so on.
Discovery analytics against big data can be enabled by different types of analytic tools, including those based on SQL queries, data mining, statistical analysis, fact clustering, data visualization, natural language processing, text analytics, artificial intelligence, and so on. It’s quite an arsenal of tool types, and savvy users get to know their analytic requirements before deciding which tool type is appropriate to their needs.
Why is it needed?
Reduces Cost
Big data tools such as Hadoop and cloud analytics aid business intelligence that reduces costs and improves the efficiency of operations. Processes like quality assurance and testing involve many complications particularly in industries like biopharmaceuticals and nanotechnologies.
Effective Decision Making
Packaged with Hadoop & in-memory analytics, Big Data analytics can analyze past data to make predictions about the future. This gives them a competitive edge and provides a more agile framework for decision making and risk handling.
New Offerings
Real time market analysis allows businesses to understand shifts in demand and supplies of products and changes in consumers’ behavior. This helps charting customer oriented marketing. The increased demand for personalized services can also be empowered by analyzing consumer needs, preferences and buying behaviors.
The central promise of Big Data is to provide means to gain insights into the challenges businesses face every day. Culturing vast quantities of data was not feasible previously, because the technologies that facilitate that process did not exist. Also, organizations did not generate all-digital data due to the low scale of installed systems.
- Banking: Retail banks use data extensively to understand how their customers use their accounts and to help identify security risks. Information gathered may also be used to make their banking experiences smoother.
- Agriculture: Data analytics are now crucial for agriculture – and they are poised to grow only more important as predicting the weather and squeezing maximum productivity out of the land become essential for feeding a growing world population.
- Real Estate: Real estate firms are leveraging Big Data for better property analysis, better trend analysis, and better understanding of their customers and markets.
- Healthcare: Healthcare sector has at length suffered from the lack of a technology to gather, store, transmit and selectively share health-related data to required institutions for their need to evolve and provide better care. Big Data analytics may be an answer to that.
- Analytics Optimization.
- Data Visualization Solutions.
Big Data involves a very large volume of data, which means adequate storage is an issue. Going further, there are issues relating to quicker rummaging through the data to gather an insight. Therefore, the services entalin quicker gathering of data, faster processing time to access the data and draw an insight strong enough to put out a forecast.
We help you to make better use of the information that flows into your organizations every day. When you combine big data with high-powered analytics, you can accomplish tasks such as:-
- Determining root causes of failures, issues and defects in near-real time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behavior before it affects your organization