Democratizing Data Science Is Not As Risky As Many Fear

The role of Citizen Data Scientist has been showing rapid growth, though not without some controversy. Many people are concerned that democratizing data science is about giving people capabilities way beyond what they are ready to handle and, therefore, ensuring disasters as a result. While bad outcomes can certainly happen if things aren’t planned and implemented well, it is possible to minimize risk by approaching a citizen data science program with the right mindset.

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Bill FranksComment
Just How Important Is AI To Your Company?

It’s still early days for artificial intelligence (AI) in the enterprise, and if you have some responsibility for analytics and/or AI within your company, you may be wondering just how hard you should push for the technology. Dip a toe in the water? A bit more substantial investment and effort? Or should you adopt the AI equivalent of “competing on analytics” and go full steam ahead with making your business more artificially intelligent? And what criteria should you employ in making this decision?

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The Fuzzy Line Between Good and Evil Data Science

The vast majority of people building analytics and data science processes have every intention of being good and ethical. As a result, most potentially unethical and evil processes arise in situations where that wasn’t the intention. The problem is typically that proper focus and governance is not in place to keep analytics and data science processes on the side of good. On top of that, what is good and what is evil isn’t nearly as clear cut as we’d wish it to be.

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Bill FranksComment
You Need Some External Data

Historically, organizations used analytics in situations where they had lots of internal data. The data was the outcome of well-structured, repetitive processes supported by transactional systems. If, for example, you had sold a lot of products at a lot of different prices, you could do pricing analytics on data in your order management system. If you’d hired a lot of people, you could do analytics on your hiring process, using data from your HR system. If you had done a lot of promotions, you could do analytics to understand which ones worked best using data from marketing systems.

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Tom DavenportComment
Creating an Analytics Community of Practice

I’ve had the opportunity to work with some incredible organizations while at IIA and one of the activities I enjoy most is listening to different ideas and solutions to solve a similar challenge. One of the perennial hot topics I’ve heard our clients discuss is: how do you get the larger organization informed and excited about analytics projects and results; how do you build data literacy with an audience that is not exposed to data every day? A popular solution is to develop a Community of Practice (CoP) and share learnings and best practices.

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Lise MasseyComment
Insights on Aisle Six

Kroger has stocked the selves with data but is the product right for you?

The great paradox of the massive retail disruption is that everyone agrees that it is indeed a massive disruption, yet often the responses of firms are a lot less than massive.  And since data is at the heart of this disruption, a data driven response is required.  So I give the team at Kroger a lot of credit for going big and bold in their Kroger Restock strategy , which is a well-considered and well-funded approach to use data and technology to better meet customers, drive growth and find entirely new avenues of business.  The recent announcement  of their Stratum offer through their analytics firm 84.51°, takes this data focused strategy further.  Stratum offers CPGs access to the data and insights Kroger has gained from serving 1 out of every 2 American households.  With this volume of data and the depth of data talent (84.51°, has its roots in dunnhumby, among the first retail analytics firms established), this offer has big appeal.   

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Drew SmithComment
The Most Important Step Toward Ethical Analytics: Intentionality

Lately I’ve had a lot of conversations with clients about the intersection of ethics and analytics. I’ve also been presenting on the topic at a number of conferences. The interest in ethics has exploded recently, driven in large part by the rise of artificial intelligence. One common question I get is what my top tip would be for a company to get started in becoming a leader in analytical ethics. I’ll discuss my answer in this post: intentionality.

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In Praise of “Boring AI”

The press, consultants, and IT market research firms would have us believe that AI is the most exciting technology available today. And it is—but in part because there aren’t a lot of exciting alternatives. Blockchain isn’t turning out to be the revolutionary technology it promised to be, and it will take a long time to come to fruition. Similarly, the Internet of Things is taking forever, in part because there are way too many standards, and none of them is sufficiently influential. It’s not surprising, for example, that the vendor C3 changed its name from “C3 IoT” to “C3 AI.”

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On AI, Deflate Both the Positive and Negative Hype

It seems to be inevitable. Any popular technology or approach to business change apparently has to involve a large amount of breathlessly positive media-driven hype, and then must be followed by potshots and disparagement. The positive press usually lasts for a couple of years or so, and then authors, journalists, and speakers who seek attention realize that they can’t get much of it by jumping on the optimistic bandwagon. They then begin to criticize the idea. Both the positive and negative hype are typically equally unrealistic.

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The Fastest Growing Analytics And Data Science Roles Today

Not long ago, the role of Data Scientist was what most companies wanted to discuss with me in terms of roles they needed to understand and add to their organizations. Then, the role of Data Engineer became a big topic of discussion. In the past year, there has been a massive increase of attention being paid to yet another role that is still new enough that its title hasn’t been standardized.

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Welcome to the Age of Explainability

Modern life has been good for those who understand and can develop analytics. From sex to soccer, data and algorithms are having an increasing impact on how important decisions are made. But it hasn’t been as pleasant for those who don’t understand algorithms but are still subject to the decisions they help to make.

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It’s Almost Time to Submit Your ANNY Application!

It’s almost time to start submitting your ANNY application! Throughout the last eight years, IIA has been hosting the ANNY Excellence in Analytics Award. This coveted award has been given to organizations and departments who are making large strides within their organization with their analytics. IIA has this award to help celebrate those in the industry who are real change drivers.

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So, You Hired a Data Scientist – Now What?

You’ve spent a lot of time and energy finding just the right person to fill your new data scientist role. The individual has settled into the position and is proving to be a successful hire for your organization. Looking forward, what can you do to retain the individual and help him or her stay engaged and successful?

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The Questionable Analytics of Censorship

Historically, concerns about over-zealous censorship have focused on repressive governments. In the United States, free speech has been a pillar of our society since its founding. For the most part, government attempts at censorship or speech restrictions receive swift and successful push back. In recent times, however, a new path to censorship has arisen in the form of search engine and social media companies that are building analytically-based censorship algorithms.

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The All-Inclusive Analytics Organization

Whatever your favorite taxonomy or category of analytics, I’m here to argue that it doesn’t matter from an organizational standpoint. That is, I think all the categories should be supported by one organization. I’ll spend the rest of this post arguing why an inclusive approach is more effective.

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AI’s March Toward Industrialization Continues: Highlights from the O’Reilly AI Conference

Last week, IIA attended the O’Reilly AI Conference (#TheAIConf) in New York City. The O’Reilly AI Conference continues to provide an informative and comprehensive overview of artificial intelligence and its accelerating transition from research to industrialization. Sessions covered a broad spectrum of AI topics, including cutting-edge research, open source tools, regulatory considerations, use cases and best practices for implementation. This conference continued to include the AI Business Summit, focused on exploring the AI challenges, opportunities and risks from the enterprise perspective. Over 2,000 people attended the conference.       

The Wednesday and Thursday morning keynotes showcased all these themes and included the following highlights..

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