Big Data and Analytics

Big Data and Predictive (Advanced) Analytics

Big Data is used to describe a massive volume and exponential growth of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques.

Big Data can be explained by the four Vs:

Volume – The quantity of data that is being generated. Big data requires processing high volumes of low-density, unstructured data of unknown value, such as Twitter data feeds, click streams on a web page and a mobile app, network traffic, sensor-enabled equipment capturing data at the speed of light, and many more.

Variety – The category to which Big Data belongs to is also an essential fact that needs to be known by the data analysts. Data types such as unstructured and semi-structured like text, audio, and video require additional processing to both derive meaning and the supporting metadata.

Velocity – The term velocity refers to the speed of generation of data or how fast the data is generated and processed to meet the demands and the challenges which lie ahead in the path of growth and development.

Veracity – The term Veracity refers to uncertainty of source data. It is often overlooked yet may be as important as the 3 V’s of Big Data: Volume, Velocity and Variety. Having a lot of data in different volumes coming in at high speed is worthless if that data is incorrect. Organizations need to ensure that the data is correct as well as the analyses performed on the data are correct.

Predictive Analytics is forward-looking analysis and is an enabler of Big Data: providing future looking insights on the business—predicting what is likely to happen and why it’s likely to happen and answers to questions that cannot be addressed by business intelligence.

Predictive analytics enable organizations to use big data to move from a historical view to a forward-looking perspective of the customer. For example, stores that use data from loyalty programs can analyze past buying behavior to predict the coupons or promotions a customer is likely to participate in or buy in the future.

Predictive analytics builds predictive models and optimization algorithms at the lowest levels of the business—at the individual customer, product, campaigns and store level and looks for predictable behaviors, tendencies, and business rules that can be used to predict the likelihood of certain behaviors and actions.

Some of the business questions that can be answered using Predictive analytics include the following.

• Who will be my most valuable customers?
• What will be my most important products?
• How many of my stores likely to generate revenue below threshold limit during next Christmas?
• What is the projected revenue trend for a specific product line over the next 12 months?
• What are my revenue potential regions by country, state, city? Etc.

Business Intelligence is required to know what has really happened in the past, predictive analytics is required to know what is likely to happen and ultimately help your resources to make decisions and take actions for the future.

Big Data and Predictive (Advanced) Analytics technologies are being embraced as fundamental requirements to drive business outcomes.

Advanced analytics solutions provided by AccuBI help every type of organization perform deeper analysis and improve decision-making:

• Businesses attract customers, strengthen their loyalty, identify and prevent fraud and reduce risk.
• Public sector organizations evaluate program effectiveness and proactively respond to public safety issues.
• Educational institutions improve classroom performance, increase retention rates and support institutional advancement.

Industrial operations predict equipment failure, avoid costly downtime and reduce maintenance and warranty costs.