Big data is a popular term used to describe the exponential growth, availability and use of information, both structured and unstructured. Much has been written on the big data trend and how it can serve as the basis for innovation, differentiation and growth.
Volume. Many factors contribute to the increase in data volume â transaction-based data stored through the years, text data constantly streaming in from social media, increasing amounts of sensor data being collected, etc. In the past, excessive data volume created a storage issue. But with todayâs decreasing storage costs, other issues emerge, including how to determine relevance amidst the large volumes of data and how to create value from data that is relevant.
Variety. Data today comes in all types of formats â from traditional databases to hierarchical data stores created by end users and OLAP systems, to text documents, email, meter-collected data, video, audio, stock ticker data and financial transactions. By some estimates, 80 percent of an organizationâs data is not numeric! But it still must be included in analyses and decision making.
Velocity. According to Gartner, velocity âmeans both how fast data is being produced and how fast the data must be processed to meet demand.â RFID tags and smart metering are driving an increasing need to deal with torrents of data in near-real time. Reacting quickly enough to deal with velocity is a challenge to most organizations.
So the real issue is not that you are acquiring large amounts of data (because we are clearly already in the era of big data). Itâs what you do with your big data that matters. The hopeful vision for big data is that organizations will be able to harness relevant data and use it to make the best decisions.
Technologies today not only support the collection and storage of large amounts of data, they provide the ability to understand and take advantage of its full value, which helps organizations run more efficiently and profitably. For instance, with big data and big data analytics, it is possible to:
Analyze millions of SKUs to determine optimal prices that maximize profit and clear inventory.
Recalculate entire risk portfolios in minutes and understand future possibilities to mitigate risk.
Mine customer data for insights that drive new strategies for customer acquisition, retention, campaign optimization and next best offers.
Quickly identify customers who matter the most.
Generate retail coupons at the point of sale based on the customerâs current and past purchases, ensuring a higher redemption rate.
Send tailored recommendations to mobile devices at just the right time, while customers are in the right location to take advantage of offers.
Analyze data from social media to detect new market trends and changes in demand.
Use clickstream analysis and data mining to detect fraudulent behavior.
Determine root causes of failures, issues and defects by investigating user sessions, network logs and machine sensors
Big data is a popular term used to describe the exponential growth, availability and use of information, both structured and unstructured. Much has been written on the big data trend and how it can serve as the basis for innovation, differentiation and growth.
Volume. Many factors contribute to the increase in data volume â transaction-based data stored through the years, text data constantly streaming in from social media, increasing amounts of sensor data being collected, etc. In the past, excessive data volume created a storage issue. But with todayâs decreasing storage costs, other issues emerge, including how to determine relevance amidst the large volumes of data and how to create value from data that is relevant.
Variety. Data today comes in all types of formats â from traditional databases to hierarchical data stores created by end users and OLAP systems, to text documents, email, meter-collected data, video, audio, stock ticker data and financial transactions. By some estimates, 80 percent of an organizationâs data is not numeric! But it still must be included in analyses and decision making.
Velocity. According to Gartner, velocity âmeans both how fast data is being produced and how fast the data must be processed to meet demand.â RFID tags and smart metering are driving an increasing need to deal with torrents of data in near-real time. Reacting quickly enough to deal with velocity is a challenge to most organizations.
So the real issue is not that you are acquiring large amounts of data (because we are clearly already in the era of big data). Itâs what you do with your big data that matters. The hopeful vision for big data is that organizations will be able to harness relevant data and use it to make the best decisions.
Technologies today not only support the collection and storage of large amounts of data, they provide the ability to understand and take advantage of its full value, which helps organizations run more efficiently and profitably. For instance, with big data and big data analytics, it is possible to:
Analyze millions of SKUs to determine optimal prices that maximize profit and clear inventory.
Recalculate entire risk portfolios in minutes and understand future possibilities to mitigate risk.
Mine customer data for insights that drive new strategies for customer acquisition, retention, campaign optimization and next best offers.
Quickly identify customers who matter the most.
Generate retail coupons at the point of sale based on the customerâs current and past purchases, ensuring a higher redemption rate.
Send tailored recommendations to mobile devices at just the right time, while customers are in the right location to take advantage of offers.
Analyze data from social media to detect new market trends and changes in demand.
Use clickstream analysis and data mining to detect fraudulent behavior.
Determine root causes of failures, issues and defects by investigating user sessions, network logs and machine sensors
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