IBM Datapalooza Takes Aim At Data Scientist Shortage

IBM announced in June that it has embarked on a quest to create a million new data scientists. It will be adding about 230 of them during its Datapalooza educational event this week in San Francisco, where prospective data scientists are building their first analytics apps.

Next year, it will take its show on the road to a dozen cities around the world, including Berlin, Prague, and Tokyo.

The prospects who signed up for the three-day Datapalooza convened Nov. 11 at Galvanize, the high-tech collaboration space in the South of Market neighborhood, to attend instructional sessions, listen to data startup entrepreneurs, and use workspaces with access to IBM’s newly launched Data Science Workbench and Bluemix cloud services. Bluemix gives them access to Spark, Hadoop, IBM Analytics, and IBM Streams.

Rob Thomas, vice president of product development, IBM Analytics, said the San Francisco event is a test drive for IBM’s 2016 Datapalooza events. “We’re trying to see what works and what doesn’t before going out on the road.”

Thomas said Datapalooza attendees were building out DNA analysis systems, public sentiment analysis systems, and other big data apps.

Read more at IBM Datapalooza Takes Aim At Data Scientist Shortage

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Code red: Big data risk management requires a safety net

Code red: Big data risk management requires a safety net

When I advise leaders on a strategy that includes data science, I ask them to consider the probability that their great idea won’t bear fruit. It’s a tough space for visionary leaders to enter — their optimism is what makes them great visionaries. That said, most data science ventures don’t turn out, and most leaders aren’t in touch with the reality that the odds are against them. Having a fallback plan makes good sense, and having a fallback plan for your fallback plan makes great sense.

For instance, when I rolled out an upgraded loyalty platform for a large financial transaction processing company in 2010, we built four plans that successively addressed the failed execution of its predecessor plan. Fortunately, we never had to pull the trigger on even the first fallback plan; however, we were fully prepared for any and all scenarios. It’s a prudent approach that I recommend for you as well, because data science is a risky endeavor.

The colors of cautious management

The best leaders have a backup plan for their backup plan. In fact, when running a strategy that incorporates big data analytics, I suggest you have a series of colored plans: green, yellow, red, and blood red (or black).

  • Green is your plan of record.
  • Yellow is a contingent plan.
  • Red doesn’t meet your minimum expectations, but it doesn’t set you strategically backward either.
  • Blood red is your worst case scenario.

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Leverage cloud financial intelligence systems with AWS

Leverage cloud financial intelligence systems with AWS

The use of cloud financial intelligence systems, typically from cloud financial management system providers, offers insights into cloud usage. Cloud financial management providers, such as Cloud Cruiser and others, can tell you how effective the cloud platforms are in delivery of services. This includes how each service tracks back to cloud resources that support the services, as well as who is consuming the services and by how much.

However, the true value of these systems is not the simple operational cost data that they are able to gather and report on — it’s the ability to leverage deeper analytics to determine usage patterns, and how those patterns will behave over time. This means you have the ability to better understand how your AWS instances (and other cloud services) were put to use in the past, and more importantly, how they will be leveraged in the future, including the ability to properly estimate cloud resource utilization in the context of complex and widely distributed architectures.

It’s all about the ability to make the most out of data from multiple components of the architecture, not just AWS. Most enterprises that deploy cloud-based systems do so using either public and private clouds within a multi-cloud architecture, which may also be mixed with traditional (or legacy) systems. This makes the financial tracking much more complex, but also much more valuable.

For example, a production management system may leverage core storage services from AWS, session management services from their OpenStack private cloud and core database services using a traditional Oracle database running in their data center. Thus, the cloud financial management system needs to gather information for many different system components, including the private and public clouds , as well as the local database. System owners can use this information to determine the amount of resources consumed, as well as patterns of consumption over time. They have a complete picture as to how a holistic system is functioning, including cloud and non-cloud components.

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Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

Magic Quadrant for Business Intelligence and Analytics Platforms

 Magic Quadrant for Business Intelligence and Analytics Platforms

Data discovery capabilities are dominating new purchasing  requirements, even for larger deployments, as alternatives to traditional BI tools. But “governed data discovery” — the ability to meet the dual demands of enterprise IT and business users — remains a challenge unmet by any one vendor.

The BI and analytics platform market is in the middle of an accelerated transformation from BI systems used primarily for measurement and reporting to those that also support analysis, prediction, forecasting and optimization. Because of the growing importance of advanced analytics for descriptive, prescriptive and predictive modeling, forecasting, simulation and optimization (see “Extend Your Portfolio of Analytics Capabilities”) in the BI and information management applications and infrastructure that companies are building — often with different buyers driving purchasing and different vendors offering solutions — this year Gartner has also published a Magic Quadrant exclusively on predictive and prescriptive analytics platforms (see Note 1). Vendors offering both sets of capabilities are featured in both Magic Quadrants.

For this Magic Quadrant, Gartner defines BI and analytics as a software platform that delivers 17 capabilities across three categories: information delivery, analysis and integration.

As a result of the market dynamics discussed above, the capability definitions in this year’s Magic Quadrant have been modified with the following additions and subtractions to reflect our current view of critical capabilities for BI and analytics platforms.
Capabilities dropped:

  1. Scorecard: Most companies do not implement true scorecard/strategy maps using BI platforms — they implement dashboards. Also, most BI vendors report limited sales activity for their scorecard products. Scorecards are primarily delivered by corporate performance management (CPM) vendors (see “Strategic CPM as a Driver for Organizational Performance Management”). Therefore, we have included scorecards as a type of dashboard, rather than as a separate category.
  2. Predictive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”
  3. Prescriptive Analytics: covered in the new “Magic Quadrant for Advanced Analytics Platforms.”

Capabilities added:

  1. Geospatial and location intelligence (see the Analysis section)
  2. Embedded advanced analytics (see the Analysis section)
  3. Business user data mashup and modeling (see the Integration section)
  4. Embeddable analytics (see the Integration section)
  5. Support for big data sources (see the Integration section)

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Five Data Mining Techniques That Help Create Business Value

Five Data Mining Techniques That Help Create Business Value

The term data mining first appeared in the 1990s while before that, statisticians used the terms “Data Fishing” or “Data Dredging” to refer to analysing data without an a-priori hypothesis. The most important objective of any data mining process is to find useful information that is easily understood in large data sets. There are a few important classes of tasks that are involved with data mining:

  1. Anomaly or Outlier detection
  2. Association rule learning
  3. Clustering analysis
  4. Classification analysis
  5. Regression analysis

Data mining can help organisations and scientists to find and select the most important and relevant information. This information can be used to create models that can help make predictions how people or systems will behave so you can anticipate on it. The more data you have the better the models will become that you can create using the data mining techniques, resulting in more business value for your organisation.

If you have any opinion about how data mining help to create business value, post it in the comment box. And contact us for discussion.

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2013 in review: Big data, bigger expectations?

In the parlance of the industry, big data’s feat was a result of the successful convergence of the “three Vs”:

Volume: A large amount of data

Variety: A wide range of data types and sources

Velocity: The speed of data moving from its sources, into the hands of those who need it

Although other Vs have since been contemplated, such as Veracity and Value, the original three attributes promised big data could go far beyond the boundaries of traditional databases, which require data to be stored in rigid rows and columns.

However, over the past year, reality began to sink in: People came to realize what big data could and could not do. Unfortunately, performing large-scale analytics in real time proved to be more daunting than originally thought. Although Hadoop continues to be the world’s most popular big data processing platform, it was designed for batch processing and is far too slow for real-time use.

Reference: 2013 in review: Big data, bigger expectations?

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