The 4 Ways Intelligent Automation Helps Achieve DataOps Background

Quick Takeaways: Argyle Leadership In Big Data and Analytics

May 1, 2018
· 3 min read

Last week I had the pleasure to attend the Argyle Leadership in Big Data and Analytics executive event in Atlanta, GA. For those who have never attended one of these regional events, it is a great opportunity to network with peers around the specific topics and hear from thought leaders across industries about their experiences and approaches. In short, a great networking and learning experience. In this blog I will try to highlight some of the key themes and talking points from the various sessions.

Digital transformation is a journey

The first session was on the digital journey at GE – or as the speaker called it, their journey to become an “Algorithmic business.” Transformation usually starts with some idea. There will be many ideas – some of them will turn into prototypes and then eventually the truly successful ones will become fully funded operational initiatives.

Beyond the tech – it is about changing the culture

The impact of change on people and culture came up repeatedly. While this is talked about frequently, often it sounds like just empty promises. The speaker from GE offered a very practical approach to dealing with changing the culture from a “gut feel” to “data driven” approach. He suggested a mantra of “Show me; Advise me; Do it for me.” What he refers to is how we bring data into the decision making process. First we provide the data as an insight. Show the number.  Once that is accepted we start with advising or recommending – showing some data driven recommendation.  Once that is settled you can move to the automation side where you can actually make the decision for them.  A far more systematic approach than just forcing your way over the line.  Some final advice included: stay grounded in business challenges and keep the process lightweight.

How do you determine the value of big data? 

The obvious answer was to tie it to business outcomes. For healthcare orgs it could be patient outcomes. But it gets more interesting when you consider how to fund building your big data foundation which might not be tied to single business outcome. In many organizations it is considered that if the company does not want to invest in that big data foundation then they are doomed to fail. Some speakers suggested they have been successful in building their foundation in iterative fashion – one big data project at a time.

Recruiting, developing, retaining the right skills

Everyone made it clear that the unicorn data scientist (that know both data science AND the business) is an elusive skillset to find. McKinsey estimates a shortage of some 200,000 data scientists in the US alone. While the perfect data scientist is difficult to find, making sure your team has the skills diversity of business acumen and context, data scientists, and also some newer roles like data engineers and citizen data scientists is fully achievable.  For many, teams exist of reskilled employees combined with new team members recruited for specific skills from outside.

How does one deal with the fast pace of technology innovation?

Speakers suggested different approaches depending on where your organization is in their journey. Most seem to balance the enterprise needs for standardization and establishing economies of scale while being very clear to select the right tool for specific use cases. There is no one tool that fits everyone’s needs.  One way to help resolve the potential free for all of the project based tool choices is to centralize vendor relationships and contracting. In that way there is always some level of oversight.

Parting thoughts

During panel discussions and networking time, participants consistently referenced and acknowledged the real struggle of preparing growing data volumes for analytics and making data consumable for people outside of IT.  Contact us to find out how we can help shorten the data preparation cycle and accelerate demonstrable business value from your big data initiatives – we’d love to tell you more!

Hopefully this gives you a couple of thoughts to take into consideration as you traverse your big data journey.

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About the author

Value-Driven AI

DataRobot is the leader in Value-Driven AI – a unique and collaborative approach to AI that combines our open AI platform, deep AI expertise and broad use-case implementation to improve how customers run, grow and optimize their business. The DataRobot AI Platform is the only complete AI lifecycle platform that interoperates with your existing investments in data, applications and business processes, and can be deployed on-prem or in any cloud environment. DataRobot and our partners have a decade of world-class AI expertise collaborating with AI teams (data scientists, business and IT), removing common blockers and developing best practices to successfully navigate projects that result in faster time to value, increased revenue and reduced costs. DataRobot customers include 40% of the Fortune 50, 8 of top 10 US banks, 7 of the top 10 pharmaceutical companies, 7 of the top 10 telcos, 5 of top 10 global manufacturers.

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