March 2017

All posts from March 2017

Incorporating machine learning in the data lake for robust business results

by pm2net on March 28, 2017 , Comments Off on Incorporating machine learning in the data lake for robust business results

Building a data lake is one of the stepping stones towards data monetization use cases and many other advance revenue generating and competitive edge use cases. What are the building blocks of a “cognitive trusted data lake” enabled by machine learning and data science?

Source: big data hub

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The data governance story: Building a business language glossary

by pm2net on March 28, 2017 , Comments Off on The data governance story: Building a business language glossary

Data is often the catalyst that drives business direction and growth. However, if data is cryptic and not understood, then how can such data contribute to such direction or growth? Just like in life, we learn from our past, as we gain direction and insight from previous events or activities to make data driven decisions.

Source: big data hub

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Everybody is Sherlock Holmes in the era of Watson-powered team data science

by pm2net on March 20, 2017 , Comments Off on Everybody is Sherlock Holmes in the era of Watson-powered team data science

Data science is a team sport that involves specialists with complementary skills and aptitudes. Successful data science initiatives leverage high-performance team collaboration. Like the fictional sleuth and his partner, IBM’s customers in the data science community must have the right mix of Holmeses and Watsons in their teams working closely together.

Source: big data hub

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A vision of hybrid cloud for big data and analytics

by pm2net on March 17, 2017 , Comments Off on A vision of hybrid cloud for big data and analytics

Quite often, we see that the need for data security and governance makes some organizations hesitant about migrating to the cloud. This is perfectly understandable given the types of data gathered and used by businesses today, the regulations they must adhere to on both a local and global level, and the cost to maintain data and operational infrastructure. Fortunately, the business model for cloud technology is evolving to enable more businesses to deploy a hybrid cloud, particularly in the areas of big data and analytics.

Source: big data hub

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Delivering customized nutrition with cognitive insights

by pm2net on March 16, 2017 , Comments Off on Delivering customized nutrition with cognitive insights

Nutrition is the science of how food effects the human body and focuses upon disease prevention, healing and management of chronic conditions. A dietitians’ field of work is however much generalized. This includes working with different diets, applications, data sources, articles, and multiple recommendations, that sometimes contradict each other. You may ask what it is to do with information, and particularly governance and analytics? Well, that is exactly the case. Food industry, and nutrition specifically, is one of the most complex sciences.

Source: big data hub

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Charting the data lake: Using the data models with schema-on-read and schema-on-write

by pm2net on March 14, 2017 , Comments Off on Charting the data lake: Using the data models with schema-on-read and schema-on-write

In many cases the data lake can be defined as a super set of repositories of data that includes the traditional data warehouse, complete with traditional relational technology. One significant example of the different components in this broader data lake, is in terms of different approaches to the data stores within the data lake. There are a set of repositories that are primarily a landing place of data unchanged as it comes from the upstream systems of record.

Source: big data hub

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Understanding the power of real-time geospatial analytics

by pm2net on March 13, 2017 , Comments Off on Understanding the power of real-time geospatial analytics

With the Geospatial Analytics service in IBM Bluemix, you can monitor moving devices from the Internet of Things. The service tracks device locations in real time with respect to one or more geographic regions. Geospatial Analytics can be used as a building block in applications that support several use cases. For example, a retail business might want to monitor for potential customers nearby its stores and send them promotions via push notifications or tweets.

Source: big data hub

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