Welcome!

Industrial IoT Authors: William Schmarzo, Elizabeth White, Stackify Blog, Yeshim Deniz, SmartBear Blog

Related Topics: @ThingsExpo, Industrial IoT, Agile Computing, Artificial Intelligence, @CloudExpo, @DXWorldExpo, FinTech Journal

@ThingsExpo: Article

Tips for Data Scientists | @CloudExpo #BigData #IoT #ML #AI #DataScience

I have come to realize that we also need to address the other side of the data science equation

I spend a lot of time helping organizations to “think like a data scientist.” My book “Big Data MBA: Driving Business Strategies with Data Science” has several chapters devoted to helping business leaders to embrace the power of data scientist thinking. My Big Data MBA class at the University of San Francisco School of Management focuses on teaching tomorrow’s business executives the power of analytics and data science to optimize key business processes, uncover new monetization opportunities and create a more compelling, engaging customer and channel engagement.

However in working with our data science teams, I have come to realize that we also need to address the other side of the data science equation; that we need to teach the data scientists in order for them to think like business executives. If the data science team cannot present the analytic results in a way that is relevant and meaningful to the business (so that it is clear what actions the business leaders need to take), then why bother.

In order to engagement more effectively with the business users, here are a couple of key points that the data science team needs to understand as they conduct their analytics:

#1: Tie the analytic results back to the organization’s key business initiatives, and more specifically, the organization’s key business decisions that drive them.
The data science team needs to understand thoroughly the key decisions that the business users are trying to make. Then, the data science team can present where and how the analytic results can help the business users make better decisions.

As part of ensuring that the analytic results are relevant and meaningful to the business, it is also critical to tie the analytic results back to the organization’s key financial or business drivers. Figure 1 shows an example of linking the analytics to the organization’s key financial and business drivers around the following business decision:

Which customers should receive which promotional offers?

Figure 1: Sample of Key Financial And Business Drivers

The Harvey Balls in Figure 1 show the relative impact that the promotional offer analytics would have on 6 key financial and business drivers in support of the customer targeting business decision.

Tying the analytic results back to organization’s financial or business drivers is key to ensuring that the data science work is relevant and meaningful to the business.

#2: Presentation of the analytic results is critical.
Don’t make the business users wade through the analytic output to try to figure out what’s important. Instead, make sure that the most meaningful analytic results stand out loud and clear to the business users. If the data supports it, make it stupidly clear where they should focus their attention and efforts.

For example, Figure 2 shows some sample analytic output that the data science team created around the business initiative of improving ground transportation effectiveness at a large location (e.g., shopping mall, port, arena) during a large event.

Figure 2: Raw Analytic Results

The business users had to look very hard at this slide to see what the slide was telling them about the business, and specifically what to do. That’s not what the business users want, and that is not how we ensure that our data science work is meaningful and actionable.

Instead, let’s apply some basic concepts to surface the meaningful and actionable insights. In Figure 3, we’ve developed some simple extensions to ensure that the meaningful and actionable insights come to the surface.

Figure 3: Presenting Actionable Insights

Instead of expecting the business users to wade through the analytics to determine what to do, Figure 3 highlights the key analytic insights or business “takeaways” (sometimes called “aha’s”) in the blue ribbon. Then the rest of the slide can illustrate how the analytics support the conclusions and insights. In particular, we have:

  • Highlighted the key actionable takeaways in the blue ribbon at the bottom of the analysis
  • We’ve removed extraneous bullet points, words and graphics that are not relevant to the key analytic takeaways.
  • We have highlighted the specific areas of the analysis that most loudly support our key takeaways.

Sometimes less really is more!

And if you really want to drive home your analytic points, get a marketing expert (thanks Phil Dussault) to present the analytic insights into a way that is engaging and exciting, while still being informative (see Figure 4).

Figure 4: Marketing Presentation of Analytic Results

Now that’s way cool!

Summary: “Thinking Like a Business Executive”
Data scientists can increase their value to the organization when they start to think like a business executive; to focus on how their business audience is going to consume the results of the analytics. The effectiveness of your data science work can be dramatically increased by:

  • Tying the analytic results back to the organization’s key decisions and the organization’s key financial and business drivers.
  • Effectively and clearly presenting the analytic results, insights and recommendations in a way that is engaging, informative and actionable to the business users.

When the data scientist has accomplished those objectives, then they’re well on their way to making themselves indispensable to the business and crossing the chasm to “thinking like a business executive.”

To hear a bit more about this “thinking like a business executive” approach, catch my “Respect the Data” presentation at the EMC Global Services booth at EMC World on Wednesday, May 4th at noon.

The post Tips for Data Scientists: Think Like a Business Executive appeared first on InFocus.

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Hitachi Vantara as CTO, IoT and Analytics.

Previously, as a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata.

Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications.

Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

IoT & Smart Cities Stories
Dynatrace is an application performance management software company with products for the information technology departments and digital business owners of medium and large businesses. Building the Future of Monitoring with Artificial Intelligence. Today we can collect lots and lots of performance data. We build beautiful dashboards and even have fancy query languages to access and transform the data. Still performance data is a secret language only a couple of people understand. The more busine...
If a machine can invent, does this mean the end of the patent system as we know it? The patent system, both in the US and Europe, allows companies to protect their inventions and helps foster innovation. However, Artificial Intelligence (AI) could be set to disrupt the patent system as we know it. This talk will examine how AI may change the patent landscape in the years to come. Furthermore, ways in which companies can best protect their AI related inventions will be examined from both a US and...
Enterprises have taken advantage of IoT to achieve important revenue and cost advantages. What is less apparent is how incumbent enterprises operating at scale have, following success with IoT, built analytic, operations management and software development capabilities - ranging from autonomous vehicles to manageable robotics installations. They have embraced these capabilities as if they were Silicon Valley startups.
Chris Matthieu is the President & CEO of Computes, inc. He brings 30 years of experience in development and launches of disruptive technologies to create new market opportunities as well as enhance enterprise product portfolios with emerging technologies. His most recent venture was Octoblu, a cross-protocol Internet of Things (IoT) mesh network platform, acquired by Citrix. Prior to co-founding Octoblu, Chris was founder of Nodester, an open-source Node.JS PaaS which was acquired by AppFog and ...
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Cloud-enabled transformation has evolved from cost saving measure to business innovation strategy -- one that combines the cloud with cognitive capabilities to drive market disruption. Learn how you can achieve the insight and agility you need to gain a competitive advantage. Industry-acclaimed CTO and cloud expert, Shankar Kalyana presents. Only the most exceptional IBMers are appointed with the rare distinction of IBM Fellow, the highest technical honor in the company. Shankar has also receive...
Bill Schmarzo, author of "Big Data: Understanding How Data Powers Big Business" and "Big Data MBA: Driving Business Strategies with Data Science," is responsible for setting the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogge...
The standardization of container runtimes and images has sparked the creation of an almost overwhelming number of new open source projects that build on and otherwise work with these specifications. Of course, there's Kubernetes, which orchestrates and manages collections of containers. It was one of the first and best-known examples of projects that make containers truly useful for production use. However, more recently, the container ecosystem has truly exploded. A service mesh like Istio addr...
Business professionals no longer wonder if they'll migrate to the cloud; it's now a matter of when. The cloud environment has proved to be a major force in transitioning to an agile business model that enables quick decisions and fast implementation that solidify customer relationships. And when the cloud is combined with the power of cognitive computing, it drives innovation and transformation that achieves astounding competitive advantage.
Whenever a new technology hits the high points of hype, everyone starts talking about it like it will solve all their business problems. Blockchain is one of those technologies. According to Gartner's latest report on the hype cycle of emerging technologies, blockchain has just passed the peak of their hype cycle curve. If you read the news articles about it, one would think it has taken over the technology world. No disruptive technology is without its challenges and potential impediments t...