Using the Data You Have to Get the Data You Want

Ryan Kee
Director – Innovation, AI


Points to Address:

  • Looking at data not so much from a tech standpoint but from a process standpoint
  • How can we use data from the back door to the front door?
  • Considering availability: how can we create the product our customers want when they want it?
  • How can we use data to enhance our associates’ lives?
Watch Ryan Kee’s full presentation here

How Walmart Has Been Working With AI 

For the last few years, Walmart has really been on a modernization hunt:

  • Equipping associates with apps that run on their own phones. 
  • Placing robots in stores that scan the shelves and clean the floors. 
  • Equipping our back rooms with systems to help us unload our trucks more quickly and efficiently. 

Not every company has been investing in this path:

  • 90 percent of digital decision makers say that revolutionary data-led projects are discussed, but only result in small-scale changes. 
  • Only 4 percent claim to have revolutionized their businesses.  — Couchbase Global CIO Survey, 2017

Results: only a small-scale change.

  • Ryan’s favorite stat from this study: 84 percent of those survey said that they either reduced scope or completely abandoned their projects due to legacy databases. 

Bottom line: big ideas falter when they are overwhelming to implement. When we have big ideas, we have to keep so many things moving forward at the same time. And when you do that, you tend to slow down. 

Resulting problems from big ideas that may be too big to execute: 

  • Inspiration without execution. We get into the “what if’s.” Example: What if we did this? What if we did that? We can solve this problem this way or that way. You wind up with a death by a thousand decks. 

The flip side of that:

  • Experimentation without direction: we get really excited with the technology, and we want to do it all. Ultimately, though, we wind up with “shiny new toy syndrome” where we are just throwing out new demos. 

Where we really want to land is right between these two extremes. 

  • Big bets must be approached with a maker mindset: we need to get in there and do what we are not comfortable doing. We need people to get into the mud, where they are uncomfortable, and push things forward. 


  • For business people: You should be able to pull your own data. You should be able to analyze it. 
  • For tech people: know the return on investment (ROI) of the businesses you are developing. That is going to help guide you as you move forward. 

Ryan’s guideline when working on data projects: 

  • Let go. Completely. It’s a human tendency to want to develop the perfect solution for every problem, but perfect solutions require perfect problems. Things are messy. Data is noisy. Processes that you design for your associates to follow probably are not going to be followed. So we need to step back and let go of what our expectations are. We’ve got to be curious before we are critical. We need to be open to different ideas and even different solutions — and even different problems. 

Ask yourself these questions: 

  • If the problem won’t work, what will? 
  • What is the step that we can take toward that goal? 
  • What can we do that can at least move us in the right direction? 

Bottom line: Complex problems require complex solutions, so make the problem simpler. Scale it back. 

How to make your data work for you

  • Gather momentum at the beginning of a project, not in the middle. 
  • Fail harder! You have to overshoot a few times in order to land it just right. 
  • Break down your problems into smaller chunks. 
  • Use off-the-shelf tools when you can — the point is to move quickly. 
  • If you don’t have to build it from scratch, don’t build it from scratch. 
  • Look for the changes. The perceived value may change. 
  • Reserve the right to get smarter. Innovation is an S curve. It’s not linear. 

Stick with clean data

Clean data is used to build a permanistic model: it may not get us to where we want to be, but it gets us something right now. Take that same model and start developing features that feed it. That brings us a step closer. There are a lot of small steps we can take to move us toward our final goal. Because what we want are just basic blocks of simple functionality. We want to simplify it and do it again and again. This requires you to have the right team. 

Build the right team

Not every team is currently equipped to do what you need. You really have to form your team and keep your problem space tight. You need certain types of people to really move a data project forward: 

  • Scout: the person on the front line. They have probably been around your company for a long time. They know where all the bodies are buried. They know where all the data is and who owns it and how to get access to it. They are the ones who help us identify what sources might be beneficial. 
  • Designer: somebody who can stare down a problem, frame it up, and identify what is the best approach for attack. They design proof of concepts that move us closer to where we want to be. They try a lot of different things. 
  • Scientist: collecting feedback. They build test plans. They love understanding what actions we are taking and what is the outcome of those actions. They aim to set up an experiment that’s pure, where we can look at our test set against what we are doing. 
  • Advocate: the most overlooked. AI and machine learning is going to lead to some of the largest changes in associates’ careers. It will change the way they work and how they approach their work. An advocate will champion both the technology and how it applies to our associates. The advocate will also interact with the associates themselves and how they use this technology. They are a feedback cycle to our teams so that we can understand how we are approaching our associates. If we don’t get a buy in from our customers and associates, we’re never going to use it. 

Bottom line: bring your teams in tight and close. You need to be constantly communicating and sharing what is currently of value. Always consider the feedback from your associates. Everyone needs to have a shared understanding of the exact same goals.

Tags   •   Retail


Leave a Reply

Your email address will not be published. Required fields are marked *

Related Posts

Recent Posts

Comparison of Popular AI Frameworks - Comparison of Popular AI Frameworks   Introduction "A computer program is said to learn from experience E with respect to…
Here’s The Main Reason Why Most AI Projects Fail - How’s your AI project coming along? If training data challenges are getting in the way of your goals, it may…
How AI is Transforming Telehealth - As technology continues to advance, artificial intelligence (AI) has become an everyday reality. And one industry it is rapidly transforming…
The Best States for New Businesses in the AI Space - The Best States for New Businesses in the AI Space The growth of AI businesses is becoming explosive. While the…
How Levatas Teaches Spot New Tricks -   One of the key components to Levatas’ success: partnerships. Customers looking for accurate analog gauge reading and thermal heat…
How AI is Revolutionizing Education -   Artificial intelligence has become increasingly relevant in a number of major industries. We read a lot about how it’s…
Three Amazing Ways AI is Revolutionizing Healthcare - It may not seem like it was too long ago when the idea of artificial intelligence playing a major role…
How 5G is Going to Impact AI in Automation Within Telecom - During this webinar, an industry expert discussed how an automation project comes to life from the initial business problem through…
How Automation Projects Come to Life in Telecom - During this webinar, an industry expert discussed how an automation project comes to life from the initial business problem through…
The Future of AI in Marketing - During this webinar, industry experts discussed where AI in marketing was heading in the future. We’ve included a short transcription…

Popular Posts

Does Healthcare AI Meet Basic Ethics Principles? - Ingrid Vasiliu-Feltes Chief Quality and Innovation Officer MEDNAX, Health Solutions Partner Over the past decade we have noticed an exponential…
Machine Learning and Artificial Intelligence in Banking - Artit "Art" Wangperawong Distinguished Engineer US Bank Introduction Every company’s AI journey is different. We’re all trying to figure out…
Machine Learning for Pricing and Inventory Optimization @ Macy’s - Jolene Mork Senior Data Scientist Macy's Iain Stitt Data Scientist Macy's Bhagyesh Phanse VP, Data Science Macy's Overview In this…
Artificial Intelligence & Cybersecurity: Math Not Magic - Wayne Chung CTO FBI Introduction The field of cybersecurity has slowly progressed from an art to a science. It has…
AI/ML in Investment and Risk Management: Recent Applications, Use Cases, and Implementation Challenges - Arvind Rajan Managing Director - Head of Global & Macro PGIM Fixed Income Introduction Investing is a completely different ballgame…
Top AI Conferences - Interested in learning the latest in AI this year? We’ve compiled a list of the top artificial intelligence conferences in…
Machine Learning in Production: From Research to the Customer - Ameen Kazerouni Lead Data Scientist Zappos Overview In this presentation Ameen Kazerouni, the Lead Data Scientist at Zappos, walks through…
How COVID-19 is Impacting the State of AI in Banking - On this panel, industry experts (listed above) discussed The State of AI in Banking and how COVID-19 is affecting it.…
“Ask Me Anything” with Zappos’s Head of AI/ML Research & Platforms, Ameen Kazerouni - Ameen Kazerouni Head of AI/ML Research & Platforms Zappos Family of Companies Ai4 recently hosted an "Ask Me Anything" session…
The Autonomous Pharmacy: Applying AI and ML to Medication Management Across the Care Continuum - Ken Perez VP of Healthcare Policy Omnicell, Inc. Ken applies artificial intelligence (AI) and machine learning (ML) solutions to medication…