5 technology innovations for commodity analytics

5 Technology Innovations

for CommodityAnalytics

Learn about the technology innovations that are paving the way for the most effective commodity analytics.

1In-Memory Data Grids

(IMDG)

In-memory data grids (IMDG):

In-memory data grids (IMDG):• Provide unprecedented processing speeds

In-memory data grids (IMDG):• Provide unprecedented processing speeds• Enable advanced calculations to be performed in

minutes instead of hours

In-memory data grids (IMDG):• Provide unprecedented processing speeds• Enable advanced calculations to be performed in

minutes instead of hours• Support hundreds of thousands of in-memory

data updates per second

In-memory data grids (IMDG):• Provide unprecedented processing speeds• Enable advanced calculations to be performed in

minutes instead of hours• Support hundreds of thousands of in-memory

data updates per second• Can be clustered and scaled in ways that support

large quantities of data

2Schema-On-Read

Schema-on-read:

Schema-on-read:• Allows data to start flowing into an analytics

system in its original raw form and parsed only at process time

Schema-on-read:• Allows data to start flowing into an analytics

system in its original raw form and parsed only at process time

• Enables users to ask any question not just those baked into a predefined data model

Schema-on-read:• Allows data to start flowing into an analytics

system in its original raw form and parsed only at process time

• Enables users to ask any question not just those baked into a predefined data model

• Supports versatile organization of data

Schema-on-read:• Allows data to start flowing into an analytics

system in its original raw form and parsed only at process time

• Enables users to ask any question not just those baked into a predefined data model

• Supports versatile organization of data• Is ideally suited for working with large sets of raw

data

3Dynamic Visualization

Dynamic visualization:

Dynamic visualization:• Enables users to ask iterative questions to get

timely answers

Dynamic visualization:• Enables users to ask iterative questions to get

timely answers• Provides interactive displays

Dynamic visualization:• Enables users to ask iterative questions to get

timely answers• Provides interactive displays• Supports the ability for users to view large

amounts of data to discover patterns and make faster and more accurate decisions

4Machine Learning

Machine learning:

Machine learning:• Evolved from the study of pattern recognition and

computational learning theory in artificial intelligence

Machine learning:• Evolved from the study of pattern recognition and

computational learning theory in artificial intelligence

• Explores the study and construction of algorithms that can learn from and make predictions on data

Machine learning:• Evolved from the study of pattern recognition and

computational learning theory in artificial intelligence

• Explores the study and construction of algorithms that can learn from and make predictions on data

• Is used for such analysis as cash flow predictions based on factors around invoices and counterparties

5Predictive Analytics

Predictive analytics:

Predictive analytics:• Is the branch of data mining concerned with the

prediction of future probabilities and trends

Predictive analytics:• Is the branch of data mining concerned with the

prediction of future probabilities and trends• Enables complex forecasting models and scenarios

to be run to answer the “what if” questions

Predictive analytics:• Is the branch of data mining concerned with the

prediction of future probabilities and trends• Enables complex forecasting models and scenarios

to be run to answer the “what if” questions• Answers such questions as “what happens to my

bottom line if market prices go up/down?”

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