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  • Gunjan Syal

Data-Driven Innovation: Supply Ecosystems, Cybersecurity, Data Quality and Circular Economy ($650)

Updated: May 27

This is a recap of the 1.5 hour GoEmerald ‘Data-Driven Innovation’ workshop presented on January 22, 2022. This is a practical masterclass for business and technology leaders to accelerate their disruptive innovation ROI.


If you prefer a live, private session of this workshop catered to your team, it would be our pleasure to discuss further: enquiries@goemerald.ca




If you prefer a private session of this workshop catered to your team, it would be our pleasure to discuss further. A certificate of completion is provided to all attendees: enquiries@goemerald.ca


We hope you enjoyed this workshop. Your feedback helps us grow and bring you more global discussions: https://www.goemerald.ca/feedback




Workshop Goals

Data is to information, and innovation is to impactful actions.


We extensively researched 350+ organizations in 25 industries over the last 2 years. We also delivered innovation initiatives with 12+ clients in 5 industries (medium-to-large enterprises and technology startups).


Our findings indicate that it is no longer sufficient to remain a data-driven leader. Innovation focus is required for sustaining business success in the post-pandemic world. This is a practical masterclass for data-driven leaders seeking to accelerate their innovation ROI.

  1. What are the fundamentals and benefits of data-driven innovation?

  2. What are the barriers to data-driven innovation in an organization?

  3. How to productively introduce data-driven innovation in an organization?



Who Will Benefit
  • Board leaders defining the post-pandemic business strategy

  • C-level leaders owning the innovation strategy

  • VP & Director leaders owning the innovation efforts

  • Data teams supporting data-driven decisions


Gunjan's Notes

The on-demand workshop requires payment but here are Coles... nay, Gunjan's Notes from the GoEmerald and transform this research we presented during this workshop. Please note that the data and information below is a result of insights from 350+ global organizations we researched in 25 industries throughout 2020–2021. It does not represent a complete global viewpoint past these organizations. The data was collected with their permission, with gratitude:

  1. You need data-driven innovation to move from supply chains to supply ecosystems: Archaic business models are causing worldwide supply chain issues. You didn't need us to tell you that. Freakley already did in this Bloomberg article, backed by data. You do need us to share this: value chains and supply chains will never support dynamic business models. If the goal is to design an organization that can handle drastic market changes, we need a mix of dynamic strategy, data and decisive leadership to create supply ecosystems. We will share more insight on how to accomplish this in future workshops.

  2. Use data to protect your organization with zero-trust culture: Cybersecurity continues to be one of the most costly issues for organizations around the globe. As mentioned above, we researched 350+ global organizations in the 2020–21 period. The collective loss to these organizations neared 800MM USD during this time period. At least 64% of the (known) threats experienced by these organizations were traced back to human errors, malice or compromised manual entry points (such as hacked emails). Identifying and establishing what we call a “zero-trust culture MVP” will help reduce this frequency. We touched upon this last year during the Data-Driven C-Suite: CISO perspective workshop. Again, we will share more on how to identify and set up your 'zero trust culture MVP' in an upcoming workshop.

  3. Data quality issues are rampant: Collectively, at least 78% of data stores at the organizations we researched suffered from data quality issues within the first quarter of operationalizing a new data store. This included loss of data integrity that required continuous manual re-work, despite the investments in automated models. The cost of this rework to these organizations was unavailable in most cases, because each organization uses varying processes, standards and tools for tracking human hours (apples and oranges, anyone?). Data quality also compounds issues with model-trustworthiness long-term. How much data is actually needed to train a model remains somewhat of a debate. Andrew Ng's campaign for data-centric AI pushes for focus on smaller datasets of higher quality over large and flawed data volumes, when it comes to training models. The premise is that more volume of lower quality data will not make a model superior, instead it will degrade the reliability. Data quality issues and biased datasets also continue to threaten development and governance of long-term responsible AI. Some big data fanatics disagree with Andrew Ng, still insisting on larger data volume. There is a reasonable concern around smaller datasets containing higher levels of bias. More experiments and a comparison across various model-categories will be beneficial in resolving this debate; or at least identifying which practice works better for certain use cases. I will certainly keep my eyes peeled for new issues of The Batch with developing insights ("Dear friends,...").

  4. Sustainability… nay, Circular Economy: *Soap-box-alert ON* Sustainability is to capacity counting, and circular economy is to actionable models that can scale. Most sustainability conversations center around net-zero or ESG scores, whereas if we focus on embedding circularity in our business models and products diligently, we accelerate the overall benefit to the climate and also obtain net-zero by default. I already covered this last year in another post. From now on, I will automatically replace ‘sustainability’ with ‘circular economy’ in all conversations. *Soap-box-alert OFF*. Circular economy is no longer an afterthought. It is not a band-aid to be placed on products, along with ESG scores, after they have already been developed. This is the beauty of circular economy - while ESG scores can be greenwashed, circular economy indicators are based on visible business model and behavior indicators which are difficult to fake. If circularity is not an embedded behavior in the organizational culture and every decision, it will not yield IRL results. 89% of the organizations we researched did not yet have a viable strategy towards circularity. 23% had begun the conversations internally, and lacked a vision as of Nov 2021. While we are revising organizational business models to overhaul supply chains into supply ecosystems, I urge you to take time to advocate for circular economy in your organizations for the benefit of the next seven generations. It leads to new revenue streams with re-designed business models, according to the Ellen MacArthur Foundation (heck, you can even refer to it as ‘sustainability’, if that makes you happy!). Again, we will share more in an upcoming workshop — we could only cover so much in 1.5 hours this time around.

I hope you enjoyed this month’s GoEmerald workshop recap. There are more actionable strategies to share from our research. We will continue the conversation in Feb during the Innovation-Driven C-Suite workshop.



About Gunjan

Gunjan Syal is Data-Driven Chief Strategy Officer at GoEmerald. She innovates businesses and ecosystems in a way that the results are visible, measurable and repeatable. She enables businesses as a fractional CSO, CPO, COO and a board member. She has led innovation initiatives for 38+ US & Canadian businesses in 10 industries. She advocates for responsible innovation at a global scale. She is also a plant hoarder (55+ indoor potted plants) and an astronomy geek (aka, stalks JWST every chance she gets).



If you prefer a live, private session of this workshop catered to your team, it would be our pleasure to discuss further: enquiries@goemerald.ca


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