Big Data Archives - DataWye

Steps to Talent Analytics and Big Data

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I was initially going to comment on how big data is being implemented in HR systems (datafication and big data of HR), in an article found here.  However, the key factor of what big data really is stands out in this article.

The question of what big data really is, has been talked about tremendously, and I am not about to jump on that bandwagon.  It is important to identify that big data is not just the accumulation of data from disconnected data sources in a more central, “usable” location.  That’s only really a fraction of the story.  I’d say that data aggregation is the easy part.  Where the real magic comes in how that data is interpreted to make strategic decisions.

There is, however a process in achieving the benefits in what a big data solution offers.  In terms of talent management, the big data analytics journey requires that the following steps be taken in order – building the data foundation, training, and reporting:

 

Reactive, Operational Reporting

  • Improve data quality; create a data dictionary
  • Develop a 1-3 year plan
  • Hire dedicated resources

Proactive, Advanced Reporting

  • Implement self-service reporting tools
  • Build strong techtnical and consulting skills
  • Work on data integration

Advanced Analytics

  • Use statistical models to identify the drivers of certain outcomes
  • Develop data visualization skills
  • Learn to “tell the story” to business leaders

Predictive Analytics

  • Reap the full value of talent analytics

 
As you’ll see in this video, only 15% of HR teams have a strong credibility in metrics and analytics and require a more powerful talent analytics system.  But the benefits of following this plan can be seen, in what else?

Organizations that have followed this methodology are:

  • 2x more likely to improve recruiting
  • 2.5x more likely to improve talent mobility
  • 3x more likely to reduce costs
  • 2x more likely to improve leadership pipelines
  • 30% higher stock returns

 

 

You can read more about the article that kicked this off for me here (http://www.forbes.com/sites/joshbersin/2013/10/07/big-data-in-human-resources-a-world-of-haves-and-have-nots/).

 

 

 

 

 

 

Musical DNA – Big Data in Action

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The Music Genome Project is more than a recommendation engine.  It leverages big data to suggest music that listeners would be most-likely to enjoy based on several factors.  These include a less-weighted “skip song” vs. a “thumbs down”, time of day, and more.

The Music Genome Project is primarily used by Pandora, which was started as result of the project, after it was met with mild industry response in 2005.  It’s big data comes not just from its users and their interactions with the content, but also from actual musicians who rate songs on up to 450 possible characteristics to define a song.

Read more about how big data is being used by Pandora and the Music genome project here (http://www.forbes.com/sites/amadoudiallo/2013/10/06/pandora-radios-dominance-built-on-big-data-edge/).

 

 

Instant Big Data Analytics with Oracle Exalytics X3-4 In-Memory Machine

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Oracle Exalytics X3-4 In-Memory Machine is changing the landscape on how fast data is processed. It’s no longer a question of speed, it’s a statement of instantaneity.

Does replacing 32 servers with two Exalytics systems sound like a good idea?

Roughly 10 times faster performance with flash memory that boosts read-write applications benchmarks.

With the seamless tie-in to Hadoop, this in-memory machine works seamlessly with a wide range of databases, data warehouses and data marts, and is not restricted to the Oracle Exadata Database Machine.

Big Data analytics can happen even faster with this new technology.

Oracle Exalytics for Instant Big Data Processing

Why should marketers be looking at big data, and how is it changing the marketing landscape?

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How is Big Data impacting marketers?

Why Should Marketers care about big data?

Are marketers effectively coping with Big Data?

Marketers understand the importance so there must be barriers that make it exceptionally difficult to invest in generating, understanding, and using data. What are the big barriers?

What Can Marketers do to overcome the barriers and build Big-Data-Based Expertise?

What Can Marketers Do To Manage And Leverage Big Data?