innovation

Blockchain, Data Governance, and Smart Contracts in a Post-COVID-19 World

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The last few months have been very disruptive to nearly everyone across the globe. There are business challenges galore; such has managing large remote workforces – many of whom are new to working remotely, and managing risk while attempting to conduct “business as usual.” Unfortunately for most businesses, their systems, processes, and internal controls were not designed for this “new normal.”

While there have been many predictions around Blockchain for the past few years it is still not widely adopted. We are beginning to see an uptick in adoption with Supply Chain Management Systems for reasons that include traceability of items – especially food and drugs. But large-scale adoption has been elusive to date.

Image of globe with network of connected dots in the space above it.

My personal belief is that we will soon begin to see large shifts in mindset, investments, and effort towards modern digital technology driven by Data Governance and Risk Management. I also believe that this will lead to these technologies becoming easier to use via new platforms and integration tools, and that will lead to faster adoption by SMBs and other non-Enterprise organizations, and that will lead to the greater need for DevOps, Monitoring, and Automation solutions as a way to maintain control of a more agile environment.

Here are a few predictions:

  1. New wearable technology supporting Medical IoT will be developed to help provide an early warning system for disease and future pandemics. That will fuel a number of innovations in various industries including Biotech and Pharma.
    • Blockchain can provide the necessary data privacy, data ownership, and data provenance to ensure the veracity of that data.
    • New legislation will be created to protect medical providers and other users of that data from being liable for missing information or trends that could have saved lives or avoided some other negative outcome.
    • In the meantime, Hospitals, Insurance Providers, and others will do everything possible to mitigate the risk of using the Medical IoT data, which could include Smart Contracts as a way to ensure compliance (which assumes that there is a benefit being provided to the data providers).
    • Platforms may be created to offer individuals control over their own data, how it is used and by whom, ownership of that data, and payment for the use of that data. This is something that I wrote about in 2013.
  2. Data Governance will be taken more seriously by every business. Today companies talk about Data Privacy, Data Security, or Data Consistency, but few have a strategic end-to-end systematic approach to managing and protecting their data and their company.
    • Comprehensive Data Governance will become both a driving and gating force as organizations modernize and grow. Even before the pandemic there were growing needs due to new data privacy laws and concerns around areas such as the data used for Machine Learning.
    • In a business environment where more systems are distributed there is an increased risk of data breaches and Cybercrime. That will need to be addressed as a foundational component of any new system or platform.
    • One or two Data Integration Companies will emerge as undisputed industry leaders due to their capabilities around MDM, Data Provenance & Traceability, and Data Access (an area typically managed by application systems).
    • New standardized APIs akin to HL7 FHIR will be created to support a variety of industries as well as interoperability between systems and industries. Frictionless integration of key systems become even more important than it is today.
  3. Anything that can be maintained and managed in a secure and flexible distributed digital environment will be implemented as a way to allow companies to quickly pivot and adapt to new challenges and opportunities on a global scale.
    • Smart Contracts and Digital Currency Payment Processing Systems will likely be core components of those systems.
    • This will also foster the growth of next generation Business Ecosystems and collaborations that will be more dynamic in nature.
    • Ongoing compliance monitoring, internal and external, will likely become a priority (“trust but verify”).

All in all this is exciting from a business and technology perspective. It will require most companies to review and adjust their strategies and tactics to embrace these concepts and adapt to the coming New Normal.

The steps we take today will shape what we see and do in the coming decade so it is important to quickly get this right, knowing that whatever is implemented today will evolve and improve over time.

Innovation, Optimization, and Business Continuity

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Originally posted on LinkedIn.com/in/chipn

What direction are you leading your team in?

Recently I read that the U.S. is experiencing a significant jump in unemployment claims. Much of that is understandable given the recent decline in many businesses, concerns about how long this crisis may last, and the need to protect ongoing viability by business owners and executives. But, in the near future business activity will resume and it will very important that businesses have maintained a pipeline of business and retained the qualified staff to deliver its products and services.

Now could be the ideal time to challenge your team to focus on improving your business. Look at business processes and identify:

  1. What works well today?  Are you able to identify what makes it work so well? Simplicity, automation, and lack of friction are typical attributes of effective and efficient systems and processes that have a positive impact on any business.
  2. What could be improved and why? Specific examples and real data will help quantify the impact and support the prioritization of follow-on activities.
  3. What is missing today?
  • Good ideas have likely been raised in the past so why not revisit them?
  • What are competitors or businesses in other segments doing that could be helpful?
  • Brainstorm and consider something completely new that could help your business.
  • Start a list, describe the need and benefits, provide specific examples, and then estimate the potential impact and time to value for each idea.
  • Take the ideas having the greatest promise and estimate the cost, people/skills needed, other dependencies for each to see how they stack up.

Something else to consider is the creation or updating of Business Continuity Plans. Now is a perfect time – while everything is fresh in the minds of your team. Not only will this help for the future, but there could also be several useful ideas for the coming weeks.

For example, do you have documentation that is sufficient for someone who is not an expert in your business to be able to take over with a relatively small ramp-up time? How will you maintain quality and control of those processes? Are your plans stored in a repository that is accessible yet secure outside of your organization? Do you have the processes and tools in place to collect documentation and feedback on things that did not work as documented or could be improved? Are your Risk Management plans and mitigation procedures up-to-date and adequate?

Investing in your business during this time of slowdown could have many benefits, including maintaining good employee morale, enhancing employee and customer loyalty, retaining employees and the expertise and skills they have, and increasing sustainability and long-term growth potential.

Could a New Channel Model Lead to Sales Amplification?

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Over the years I have helped both successful companies and start-ups improve and strengthen their Channel and Strategic Alliances programs. Those companies do a great job closing deals but usually have concerns about not generating or receiving enough new business leads. Or, they develop strong relationships with one or two vendors, only to find later that a key vendor has been sending deals to a competitor. You may not have experienced this yourself, but if you have please read on.

Word cloud for strategic thinking.

Most traditional channel models support Distributors, Resellers, OEMs, and ISVs. Business mainly flows upwards to the main vendor. If that vendor has popular and widely used products then business can be good because there is sufficient demand. But when that is not the case your sales pipeline usually suffers.

Doing something the same way as everyone else may not be a bad approach when there is enough business for everyone and your growth goals and aspirations are aligned with your competition.

Sales Channel business is usually not the main source of revenue for most companies, but it does have the potential to become the largest and most scalable revenue source for nearly any business. Just think about the money that is being left on the table by not adopting a growth mindset and executing a new and better strategy.

In the summer of 2016 I attended the “Sage Summit” in Chicago. It was impressive to see the Sage Group’s efforts to build, strengthen, and protect their community of Customers and Channel Partners. They made the effort to foster higher levels of collaboration between the various types of partners – implementation services, consulting and staff augmentation services, complementary product vendors, etc. They had created their own highly successful Business Ecosystem, which is an excellent proof point.

When designing a channel partner program my personal focus has always been on finding the balance between promoting and protecting the business of partners with helping ensure that the end customers have the best experience possible (and have some recourse when things do not work out as expected). There are a variety of methods I have used to accomplish those goals, but the missing component has always been the inclusion of a systematic approach to seed relationships between those partners and facilitate an even greater amount of business activity.

Nearly a year ago I began working with a management consultancy run by Robert Kim Wilson, which has a business vision based on his book, “They Will Be Giants.” I will provide links at the bottom of the post for this book and other relevant resources. Kim asserts that Entrepreneurs with a Purpose-Driven Business Ecosystem (PDBE) are more successful than those without one and provides examples to prove his point. Having experienced Kim’s own PDBE I see how purpose fosters trust and collaboration.

As I did more research I have found that, especially over the past two years, there has been a lot of focus placed on Business Ecosystems and Business Ecosystem Organizers (such as Sage in the earlier example). Those findings reinforced the PDBE approach, and external validation is always a good thing.

Just as important from my perspective is that this concept applies to businesses of any size, and it is especially helpful to small to midsize businesses. The fun part for me is exploring a specific business, analyzing what they do today, and quantifying the benefits of adopting this new strategy.

So, how does this new type of Business Ecosystem work?

  • The Business Ecosystem Organizer expands the overall network, vets new “Business Ecopartners,” and provides a framework or infrastructure for the various Business Ecopartners to get to know one another, exchange ideas, and discuss opportunities.
    • This can become an incredible source of sustainable revenue for companies willing to invest in the necessary components to grow and support their own Business Ecosystem.
  • Business Ecopartners will have access to trusted resources that can augment existing business and take-on new, bigger projects by leveraging the available expertise.
    • Suppose that you have products or services that work with commercial CRM (Customer Relationship Management), ERP (Enterprise Resource Planning), or SCM (Supply Chain Management).
    • You have seen a growing demand for functionality that relies on highly specialized technologies like:
      • Cryptocurrency support.
      • Blockchain for both financial transactions and things like traceability in your supply chain or IoT data.
      • AI (artificial intelligence), ML (machine learning) to detect patterns and anomalies – such as with fraud detection, Deep Learning/Neural Networks for image recognition or other complex pattern recognition.
      • Graph databases to better understand a business and infer new ways to improve it.
      • Knowledge Graph/Semantic databases to assist with Transfer Learning and deeper understanding.
    • It would not be practical or cost-effective for most businesses to build these practices in-house so partnering becomes very attractive to your company.
      • This type of business can also be very attractive to a Business Ecopartner because someone else is handling sales, billings, account management, etc.
  • Other Business Ecopartners could leverage your products or services for their projects and engagements, thus becoming another source of revenue.
  • By leveraging this network your business can essentially compete on imagination and innovation – something that could become a huge source of differentiation from your competition.

Value realized from this New Business Ecosystem model:

  1. These new sources of business and talent can become a real competitive advantages for your business.
  2. This becomes the source for Sales Amplification because your business is extending its reach and expanding its growth potential – directly and indirectly.
  3. The weighted (based on capabilities, capacity, responsiveness, and Ecopartner feedback) Business Ecopartner network model could lead to exponential business growth over time – and that is a winning strategy for any business.

References:

IoT and Vendor Lock-in

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I was researching an idea last weekend and stumbled across something unexpected. My personal view on IoT has been that it provides a framework to support a rich ecosystem of hardware and software products. That flexibility and extensibility foster innovation, which in turn fosters greater use and ultimately adoption of the best products. It was quite a surprise to discover that IoT was being used to do just the opposite.

My initial finding was a YouTube video about “Tractor Hacking” to allow farmers to make their own repairs. That seemed like an odd video to appear in my search results, but midway or so through the video it made sense. There is a discussion about not having access to software, replacement components not working because they are not registered with that tractor’s serial number and that the only alternative is costly transportation of the equipment to a Dealership to have a costly component installed.

Image of jail cell representing vendor lock-in
Image Copyright (c) gograph.com/VIPDesignUSA

My initial thoughts were that there had to be more to the story, as I found it hard to believe that a major vendor in any industry would intentionally do something like this. That led me to an article from nearly two years earlier that contained the following:

“IoT to completely transform their business model”   and

“John Deere was looking for ways to change their business model and extend their products and service offering, allowing for a more constant flow of revenue from a single customer. The IoT allows them to do just that.”

That article closed with the assertion:

“Moreover, only allowing John Deere products access to the ecosystem creates a buyer lock-in for the farmers. Once they own John Deere equipment and make use of their services, it will be very expensive to switch to another supplier, thus strengthening John Deere’s strategic position.”

While any technology – especially platforms, has the potential for vendor lock-in, the majority of vendors offer some form of openness, such as:

  • Supporting open standards, APIs and processes that support portability and third-party product access.
  • Providing simple ways to unload your own data in at least one of several commonly used non-proprietary formats.

Some buyers may deliberately make the decision to implement systems that support non-standard technology and extensions because they believe the long-term benefits of a tightly coupled system outweigh the risks of being locked-into a vendor’s proprietary stack. But, there are almost always several competitive options available so it is a fully informed decision.

Less technology-savvy buyers may never even consider asking questions like this when making a purchasing decision. Even technologically savvy people may not consider IoT as a key component of some everyday items, failing to recognize the implications of a closed system for their purchase. It will be interesting to see if this type of deliberate business strategy changes due to competitive pressure, social pressure, or legislation over the coming few years.

In the meantime, the principle of caveat emptor may be truer than ever in this age of connected everything and the Internet of Things.

 

Good Article on Why AI Projects Fail

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high angle photo of robot
Photo by Alex Knight on Pexels.com

Today I ran across this article that was very good as it focused on lessons learned, which potentially helps everyone interested in these topics. It contained a good mix of problems at a non-technical level.

Below is the link to the article, as well as commentary on the Top 3 items listed from my perspective.

https://www.cio.com/article/3429177/6-reasons-why-ai-projects-fail.html

Item #1: 

The article starts by discussing how the “problem” being evaluated was misstated using technical terms. It led me to believe that at least some of these efforts are conducted “in a vacuum.” That was a surprise given the cost and strategic importance of getting these early-adopter AI projects right.

In Sales and Marketing you start the question, “What problem are we trying to solve?” and evolve that to, “How would customers or prospects describe this problem in their own words?” Without that understanding, you can neither initially vet the solution nor quickly qualify the need for your solution when speaking with those customers or prospects. That leaves a lot of room for error when transitioning from strategy to execution.

Increased collaboration with Business would likely have helped. This was touched on at the end of the article under “Cultural challenges,” but the importance seemed to be downplayed. Lessons learned are valuable – especially when you are able to learn from the mistakes of others. To me, this should have been called out early as a major lesson learned.

Item #2: 

This second area had to do with the perspective of the data, whether that was the angle of the subject in photographs (overhead from a drone vs horizontal from the shoreline) or the type of customer data evaluated (such as from a single source) used to train the ML algorithm.

That was interesting because it appears that assumptions may have played a part in overlooking other aspects of the problem, or that the teams may have been overly confident about obtaining the correct results using the data available. In the examples cited those teams did figure those problems out and took corrective action. A follow-on article describing the process used to make their root cause determination in each case would be very interesting.

As an aside, from my perspective, this is why Explainable AI is so important. There are times that you just don’t know what you don’t know (the unknown unknowns). Being able to understand why and on what the AI is basing its decisions should help with providing better quality curated data up-front, as well as being able to identify potential drifts in the wrong direction while it is still early enough to make corrections without impacting deadlines or deliverables.

Item #3: 

This didn’t surprise me but should be a cause for concern as advances are made at faster rates and potentially less validation is made as organizations race to be first to market with some AI-based competitive advantage. The last paragraph under ‘Training data bias’ stated that based on a PWC survey, “only 25 percent of respondents said they would prioritize the ethical implications of an AI solution before implementing it.

Bonus Item:

The discussion about the value of unstructured data was very interesting, especially when you consider:

  1. The potential for NLU (natural language understanding) products in conjunction with ML and AI.
  2. The importance of semantic data analysis relative to any ML effort.
  3. The incredible value that products like MarkLogic’s database or Franz’s AllegroGraph provide over standard Analytics Database products.
    • I personally believe that the biggest exception to assertion this will be from GPU databases (like OmniSci) that easily handle streaming data, can accomplish extreme computational feats well beyond those of traditional CPU based products, and have geospatial capabilities that provide an additional dimension of insight to the problem being solved.

 

Update: This is a link to a related article that discusses trends in areas of implementation, important considerations, and the potential ROI of AI projects: https://www.fastcompany.com/90387050/reduce-the-hype-and-find-a-plan-how-to-adopt-an-ai-strategy

This is definitely an exciting space that will experience significant growth over the next 3-5 years. The more information, experiences, and lessons learned shared the better it will be for everyone.