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  1. Avoid These Pitfalls to Get Maximum Value from Data Science
    Data science techniques are getting better, cheaper, and easier to use. Even small and medium sized organizations can now tap these technologies. But, if you fail to properly introduce, support, and integrate data science capabilities, a lot of money can be wasted.
  2. IT Leadership: Navigating Talent Management, Millennials, #MeToo
    Whether you are a CIO, IT manager, or an IT pro, today's dynamic workplace presents new issues and challenges. Here are some of the highlights of the upcoming Interop ITX Leadership and Professional Development Track, to help you succeed.
  3. An Intuitive UI: Not Just for End Users
    Having a simple, understandable user interface on an application aids user acceptance, and even considerations such as interoperability and security.
  4. Unlock the Value: From Data Quality to Artificial Intelligence
    Data quality, data privacy, and advanced technologies such as AI, machine learning, neural networks, and more, are of top concern to data analytics pros and IT managers, says Karen Lopez, Data & Analytics Track Chair for Interop ITX 2018.
  5. 5 Reasons Data Scientists Should Adopt DevOps Practices
    Enterprise software development teams have historically had trouble ensuring the code that runs well on a developer's machine also runs well in production. DevOps has promoted more collaboration between developers and IT operations. Data scientists and data science teams face similar challenges, which DevOps concepts can help address.
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