Big Data 2.0

Big Data 2.0 is the next generation of Big Data to explore the power of data to bring data awareness and take more advantage from the data. Previous Big Data is focusing on the technologies used to process huge volume of data; Big Data 2 is focusing on high latency and transformational insight. What is actually in the data and what value within data? That what are going to be exploited.

Big Data 2.0 bring possibilities to analyze a variety of massive data set from multiple points of data sources both in batch and real time; sensors, social media, devices and internet, to gain competitive insight, to establish the value of the data, to provision that data to different people and applications that need the data.

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More people will gain access to the data; time to value will be accelerated. Using Big Data in a real time fashion can be an extremely efficient way to constantly search for optimal solutions. Especially in environments that are in constant flux such as traffic, stock markets or energy usage, big data can help optimize actual solutions and bring about reduced cost or increased revenue. Big Data can be applied to ensure a constant flow of traffic while constantly analyzing the current traffic situation and divert traffic on the fly when required. The energy sector Big Data applications can be used to coordinate the future smart grid where the location of energy production and energy usage will vary almost constantly. Data cleansed and processed and decisions are made, all without any human intervention by deploying real-time alerts for data quality issues.

In business environments the increasing amount of information collected can also be used to explore new ways of production, new delivery processes, new markets, new customers, etc. By closely recombining data, new patterns emerge, from which better or new insights can be derived. This could lead businesses to reorganize the way they manufacture their product or re-align their logistics in order to reduce time-to-market, manufacturing cost or enhance quality of the product and service. From a commercial perspective studying data patterns from customers and prospects leads to better insight into customer behavior and changing market conditions.

More opportunities will be carried from lot information being collected and captured.

  • Identify new market segments. Exploring big data sets of commercial information not only leads to better customer insight
  • Create customer centric assortments. Gauge the probability of selling a particular product at certain time and place ultimately optimizing assortments by location, time and profitability. Correlate customer data from multiple channels to have a better understanding of customers by targeting the right customers with the right products
  • Online advertising. Defining marketing tactics at micro segment level with higher returns on advertising investment
  • Online business. Provide offering in real time based on customer preferences
  • Product Innovation. Using information captured from products via sensor sent the data over internet, use the information to make product more efficient
  • Provide services or products that resonate with consumers. Transform enormous tweets and unstructured data from internet into insights
  • Target-sharing program to drive exceptionally high turnout. Asses individual online activities and ascertain campaign tactics were producing offline results
  • Supply chain operation. Monitor physical product movement by capturing data from RFID and micro sensors
  • Retail chain operation. Identify overstocks at one store that could be sold in another by capturing pricing, promotion and loyalty data to create insights into what, when and why customer buy
  • Rail car sensor track. Pull data from sensor to prevent disaster
  • Intelligent rail cars. Store data for whole ecosystem; cars, rails, rail roads, crossing, whether pattern that justifyrail movement and so on
  • GPS based data for shipment tracking and logistic