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The 4 V’s have been a well known catalyst for the growth of Big Data analysis in last decade. Moreover, we have entered into a new era where new challenges are evolving like “variety” of open source technologies, Machine Learning use cases, and the rapid development across the big data ecosystem. These have added new challenges around how to keep up with the ever-growing information, while balancing how to ensure the effectiveness of advanced analytics in such a noisy environment.
Predictive and Prescriptive analytics is in a transient state, and requires modern infrastructure that traditional data warehouses can’t service. Having a big data platform that enables teams appropriate self-service access to unstructured data, enables companies to have more innovative data operations.
This is common in traditional Business Intelligence and reporting analytics.
This takes Business Intelligence a step further, where the end user could be given a report or have a set of actions sent to them based on the results of the data.
Where a model is applied to the data and a decision or probability score is given based on historical events. This data can also be fed back into Business Intelligence systems to help with future decision making.
Takes the predicted output of the data and places it into a practical application that makes recommendations or alerts end-users (such as with fraud detection or ecommerce shopping). This data usually needs to be put into a data mart that can feed out to an application in near-real time.
How big data analytics worksBig data analytics refers to collecting, processing, cleaning, and analyzing large datasets to help organizations operationalize their big data.
Data collection looks different for every organization. With today’s technology, organizations can gather both structured and unstructured data from a variety of sources — from cloud storage to mobile applications to in-store IoT sensors and beyond. Some data will be stored in data warehouses where business intelligence tools and solutions can access it easily. Raw or unstructured data that is too diverse or complex for a warehouse may be assigned metadata and stored in a data lake.
Once data is collected and stored, it must be organized properly to get accurate results on analytical queries, especially when it’s large and unstructured. Available data is growing exponentially, making data processing a challenge for organizations. One processing option is batch processing, which looks at large data blocks over time. Batch processing is useful when there is a longer turnaround time between collecting and analyzing data. Stream processing looks at small batches of data at once, shortening the delay time between collection and analysis for quicker decision-making. Stream processing is more complex and often more expensive.
Data big or small requires scrubbing to improve data quality and get stronger results; all data must be formatted correctly, and any duplicative or irrelevant data must be eliminated or accounted for. Dirty data can obscure and mislead, creating flawed insights.
Getting big data into a usable state takes time. Once it’s ready, advanced analytics processes can turn big data into big insights. Some of these big data analysis methods include:
Data mining sorts through large datasets to identify patterns and relationships by identifying anomalies and creating data clusters.
Predictive analytics uses an organization’s historical data to make predictions about the future, identifying upcoming risks and opportunities.
Deep learning imitates human learning patterns by using artificial intelligence and machine learning to layer algorithms and find patterns in the most complex and abstract data.
Big Data analytics to identify fraudulent activities and discrepancies. The organization leverages it to narrow down a list of suspects or root causes of problems.
Big Data analytics to analyze how efficient the engine designs are and if there is any need for improvements.
Big Data analytics to make strategic decisions. For example, the company leverages it to decide if a particular location would be suitable for a new outlet or not. They will analyze several different factors, such as population, demographics, accessibility of the location, and more.
Use Case: Delta Air Lines uses Big Data analysis to improve customer experiences. They monitor tweets to find out their customers’ experience regarding their journeys, delays, and so on. The airline identifies negative tweets and does what’s necessary to remedy the situation. By publicly addressing these issues and offering solutions, it helps the airline build good customer relations.
Here are some of the sectors where Big Data is actively used:
The digital revolution has transformed the manufacturing industry. Manufacturers are now finding new ways to harness all the data they generate to improve operational efficiency, streamline business processes, and uncover valuable insights that will drive profits and growth.
Competition is fierce in retail. To stay ahead, companies strive to differentiate themselves. Big data is being used across all stages of the retail process—from product predictions to demand forecasting to in-store optimization. Using big data, retailers are finding new ways to innovate.
Healthcare organizations are using big data for everything from improving profitability to helping save lives. Healthcare companies, hospitals, and researchers collect massive amounts of data. But all of this data isn’t useful in isolation. It becomes important when the data is analyzed to highlight trends and threats in patterns and create predictive models.
For the past few years, the oil and gas industry has been leveraging big data to find new ways to innovate. The industry has long made use of data sensors to track and monitor the performance of oil wells, machinery, and operations. Oil and gas companies have been able to harness this data to monitor well activity, create models of the Earth to find new oil sources, and perform many other value-added tasks.
The popularity of smart phones and other mobile devices has given telecommunications companies tremendous growth opportunities. But there are challenges as well, as organizations work to keep pace with customer demands for new digital services while managing an ever-expanding volume of data.
Forward-thinking banks and financial services firms are capitalizing on big data. From capturing new market opportunities to reducing fraud, financial services organizations have been able to convert big data into a competitive advantage.
