Welcome!

Industrial IoT Authors: Elizabeth White, Stackify Blog, Yeshim Deniz, SmartBear Blog, Liz McMillan

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

@DXWorldExpo: 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
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...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...
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.
As IoT continues to increase momentum, so does the associated risk. Secure Device Lifecycle Management (DLM) is ranked as one of the most important technology areas of IoT. Driving this trend is the realization that secure support for IoT devices provides companies the ability to deliver high-quality, reliable, secure offerings faster, create new revenue streams, and reduce support costs, all while building a competitive advantage in their markets. In this session, we will use customer use cases...