Summary: The term “big data” refers to data that is so large, fast or complex that it’s difficult or impossible to process using traditional methods. The act of accessing and storing large amounts of information for analytics has been around a long time. But the concept of big data gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data as the three V’s:
Volume: Organizations collect data from a variety of sources, including business transactions, smart (IoT) devices, industrial equipment, videos, social media and more. In the past, storing it would have been a problem – but cheaper storage on platforms like data lakes and Hadoop have eased the burden.
Velocity: With the growth in the Internet of Things, data streams in to businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors and smart meters are driving the need to deal with these torrents of data in near-real time.
Variety: Data comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents, emails, videos, audios, stock ticker data and financial transactions.
The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyse it to find answers that enable 1) cost reductions, 2) time reductions, 3) new product development and optimized offerings, and 4) smart decision making.
Big data analysis is not useful in short run. It needs to be analyzed for longer duration to leverage its benefits.
Lots of big data is unstructured.
Traditional storages can cost lot of money to store big data.
Big data with predictive analytics, high performance computing systems, machine learning, and other strategies have been used in the past and will continue to be used heavily in the future of computational physics. By using these big data-related systems, engineers and scientists have been able to more easily design cars, airplanes, and other vehicles. They have also been able to more accurately predict daily weather as well as natural disasters. Big data analytics has affected the field of computational physics almost since computational physics was created.
BI & Analytics:
Summary: Business intelligence (BI) is a technology-driven process for analyzing data and delivering actionable information that helps executives, managers and workers make informed business decisions. As part of the BI process, organizations collect data from internal IT systems and external sources, prepare it for analysis, run queries against the data and create data visualizations, BI dashboards and reports to make the analytics results available to business users for operational decision-making and strategic planning.
The ultimate goal of BI initiatives is to drive better business decisions that enable organizations to increase revenue, improve operational efficiency and gain competitive advantages over business rivals. To achieve that goal, BI incorporates a combination of analytics, data management and reporting tools, plus various methodologies for managing and analyzing data.
the role of business intelligence is to improve an organization's business operations through the use of relevant data. Companies that effectively employ BI tools and techniques can translate their collected data into valuable insights about their business processes and strategies. Such insights can then be used to make better business decisions that increase productivity and revenue, leading to accelerated business growth and higher profits.
A successful BI program produces a variety of business benefits in an organization. For example, BI enables C-suite executives and department managers to monitor business performance on an ongoing basis so they can act quickly when issues or opportunities arise. Analyzing customer data helps make marketing, sales and customer service efforts more effective. Supply chain, manufacturing and distribution bottlenecks can be detected before they cause financial harm. HR managers are better able to monitor employee productivity, labor costs and other workforce data. BI platforms are increasingly being used as front-end interfaces for big data systems that contain a combination of structured, unstructured and semi structured data. Modern BI software typically offers flexible connectivity options, enabling it to connect to a range of data sources. This, along with the relatively simple user interface (UI) in most BI tools, makes it a good fit for big data architectures.
Difficulty Analysing different data sources.
The implementation of BI will improve the decisions taken by the mangers. The BI systems will in fact empower all the employees with enough information and make them capable of decision making.