The “Unlearning” Dilemma in Learning and Development

Key Stakeholders: IT Managers, IT Directors, Chief Information Officers, Chief Technology Officers, Chief Digital Officers, IT Governance Managers, and IT Project and Portfolio Managers. Top Takeaways: One critical barrier to full adoption is the poorly addressed problem of unlearning. Anytime a new piece of software achieves some goal with a set of motor behaviors that…

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Growing Your Data Science Team: Diversifying Beyond Unicorns

A herd of cloned data scientist unicorns

If your organization already has a data scientist, but your data science workload has grown beyond their capacity, you’re probably thinking about hiring another data scientist. Perhaps even a team of them. But cloning your existing data scientist isn’t the best way to grow your organization’s capacity for doing data science.

Why not simply hire more data scientists? First, so many of the tasks listed above are actually well outside the core competency of data scientists’ statistical work, and other roles (some of whom likely already exist in your organization) can perform these tasks much more efficiently. Second, data scientists who can perform all of these tasks well are a rare find; hoping to find their clones in sufficient numbers on the open market is a losing proposition. Third, though your organization’s data science practice continues to expand, the amount of time your original domain expert is able to spend with the data scientist on a growing pool of data science projects does not; it’s time to start delegating some tasks to operational specialists.

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Cloudera Improves Enterprise Rigor and Reuse by Putting the “Science” in Data Science Workbench

Key Stakeholders: IT managers, data scientists, data analysts, database administrators, application developers, enterprise statisticians, machine learning directors and managers, existing enterprise Cloudera customers Why It Matters: As Cloudera continues its pivot towards becoming a full-service machine learning and analytics platform, its latest updates enhance its ability to retain existing customers of its commercial data lake…

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Revealing the Learning Science for Improving IT Onboarding

Key Stakeholders: IT Managers, IT Directors, Chief Information Officers, Chief Technology Officers, Chief Digital Officers, IT Governance Managers, and IT Project and Portfolio Managers. Top Takeaways: Information technology is innovating at an amazing pace. These technologies hold the promise of increased effectiveness, efficiency and profits. Unfortunately, the training tools developed to onboard users are often…

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Data and Analytic Strategies for Developing Ethical IT: a BrightTALK webinar

BI to AI on Trusted Data - An Amalgam Insights Research Theme
BI to AI on Trusted Data – An Amalgam Insights Research Theme

Recommended Audience: CIOs, Enterprise Architects, Data Managers, Analytics Managers, Data Scientists, IT Managers

Vendors Mentioned: Trifacta, Paxata, Datameer, Datawatch, Lavastorm, Alation, Tamr, Unifi, 1010Data, Podium Data, IBM, Domo, Microsoft, Information Builders, Board, Microstrategy, Cloudera, H20.ai, RapidMiner, Domino Data Lab, Dataiku, TIBCO, SAS, Amazon Web Services, Google, DataRobot.

In case you missed it, I just finished up my webinar on Data and Analytic Strategies for Developing Ethical IT. We are headed into a new algorithmic, statistical, and heterogenous data-defined model of IT where IT ethics and relevance are being challenged. In this webinar, we discussed:

  • Why IT is broken from a support and business perspective
  • The aspects of IT that can be fixed
  • What we can do as IT managers to fix IT
  • Data Prep, Data Unification, Business Intelligence, Data Science, and Machine Learning vendors that can help unlock the Black Boxes and Opt-Out disasters in IT
  • Key Recommendations

This webinar provides context to my ongoing research tracks of “BI to AI on Shared Data” and “IT Management at Scale.” To attend the webinar, please check the embedded view below or click to watch on BrightTALK