Build ontologies, let your chatbots socialize!

It’s 2018 and companies need to improve their understanding of customers, vendors, employees and investors. Companies need many more ears and mouths to absorb and disseminate knowledge. Machines engaging with humans in productive conversations using Natural Laguage Processing is no more a dream as well demonstrated by Siri, Alexa and Google-Home! Why is this so important? Obviously because it would empower companies to do what they need to do this year. But before we get to it , let us pull back and take a look at what makes knowledge transfer hard.

Tacit and Explicit knowledge

Tacit knowledge is what we have internalized and that is what we really use when we make a number of decisions in our personal and professional life. A pilot uses his tacit knowledge and has no use for explicit knowledge such as an instruction manual while flying a large passenger aircraft. Doctors, lawyers, architects and soldiers owe their tacit knowledge to years of training alongside their seniors. This tacit knowledge transfer happens while socializing as per Alex Pentland in his famous book Social Physics

Explicit knowledge is what we externalize and express in English or some other language in articles, books, videos, podcasts, databases and spreadsheets etc.

Nonaka and Takeuchi have developed the SECI model for organizations to grow their knowledge-base by cyclically going through the following 4 steps that looks like an ever expanding spiral

  1. Socialization ( Tacit to Tacit)
  2. Externalization (Tacit to Explicit)
  3. Combination (Explicit to Explicit)
  4. Internalization (Explicit to Tacit)

 

  • SECI

    SECI Model of knowledge transfer

    Tacit knowledge is hard to externalize. No one can communicate all he/she knows by writing it down. On the contrary socialization is easy. It happens easily because of the social setting which provides the context. Human brain snacks on information that is in context. On the contrary most of the communication directed at you on the internet is sheer noise due to lack of context. Wouldn’t it be wonderful if chatbots storing a lot of explicit knowledge could use it in a virtually social setting to engage in conversations with us?

    Let chatbots do the hard work of externalization

    Imagine having a friend by your side who has the patience to answer all your questions that you need to be answered to understand and participate in a conversation. All of us were very inquisitive as children. We stopped asking questions when we saw adults around us losing patience.

    Chatbots can simultaneously engage in thousands of conversations. Socializing with chatbots makes it easy for us to weave bits and pieces of knowledge in the right context. This knowledge is provided accurately in the right context by a chatbot that is lurking the background while we are going about our daily routine of using our enterprise software or our favorite app. Explicit knowledge is internalized by a chatbot in a few seconds. But for chatbots to learn new pieces of knowledge , they need to ask questions and plug incoming pieces of information in the right place. Ontologies provide the underlying infrastructure for chatbots to organize knowledge – somewhat parallel to the way humans learn by organizing related facts in the same part of the brain.

    Building an ontology made easy!

    An ontology is a little more than a taxonomy which is a tree like structure that organizes entities in classes, subclasses and instances. In addition it also establishes connections between leaves of the tree by specifying one way, two way and transient relationships. Its best represented in a graph database such as neo4j. Organizing the knowledge specific to your company’s business domain in a graph database is a daunting task. Many companies have spent years building ontologies. No doubt that it’s an effort that more than pays for itself- but the sheer volume of work deters companies from investing in this project.

    Synerzip has developed a semi-automated way of building ontologies. The system takes documents relevant to your company’s domain – which may be catalogues, brochures, articles, emails, spreadsheets, resumes or handbooks and puts them in a corpus. Top few hundred the frequently occurring terms in the corpus are manually organized by an subject matter expert to form the seed ontology. This might take a few days or weeks at the most.

    Automation kicks in once you have a reasonably well populated seed ontology. The program computes similarity scores based on the context in which terms appear. Terms having high similarity are put in the same class. There are still some places where a human expert is consulted to resolve contention. But it makes the project more feasible and viable for companies planning to build their ontologies from scratch. For companies who already have built their ontologies , this system could be a good way to update and improve utility.

    Building chatbots has been made easy by providers like Facebook messenger, Slack and Skype. By the end of this year , we will see chatbots being preferred over mobile interfaces by many applications.

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The Startup Way- Take-aways from the Lean Startup Week 2017

The main highlight of the conference was the new book- “The Startup Way” authored by Eric Ries. It was released in the week preceding the conference and I was pleasantly surprised to be able to buy a copy from a bookshop in Hong Kong airport. I finished reading the book on the long flight to San Francisco and was all set to hear more about it from Eric himself. I was in for yet another surprise when as a part of the conference kit I got a hard bound copy of the book which many of us got signed by Eric.

The book significantly captures how a passionately motivated team trying to solve a problem under conditions of extreme uncertainty can apply the principles of “The Lean Startup” – even when the team is inside an institution such as a large corporation , Government or a social enterprise. The common idea is that any hypothesis can be validated by conducting a controlled experiment. These are low cost short iterations of few weeks each ending with success or failure. In either case the end result is that the team acquires some validated learning at the end of each iteration.

The Startup Way introduces some new concepts. Innovation boards are senior stake holders within the institution who act like VCs for the internal startups. They decide whether a proposed startup is worth investing in. Once the decision is made they hold the intrapreneurs accountable by reviewing progress on a regular basis in Pivot or Persevere meetings. The innovation board may decide to release the next round of funding or to kill the startup based on the value of validated learning by the startup. The board gives complete freedom to the startup in the way the funds are utilized. But the startup has the onus to use the funds judiciously so as to achieve demonstrable validated learning so that they can go back to the board for the next round. This way of funding is called as metered funding.

As one can see “accountability” is the foundation. The process is designed so that it builds the culture needed to motivate and empower people. All the words in italics have more detailed explanation in the book. The pyramid below shows the order of priority and significance associated with the four concepts.

Accountability

Accountability is the foundation

In this earlier article I had discussed the problems with hierarchies and various options being evaluated. The discussion was inconclusive and we remained admitting that no single structure can replace the hierarchies. “Horses for courses” as suggested by Tathagat Varma was the summary of our conclusion.

The startup way seems to have taken this thought one step further by suggesting a perpetually transforming organization that spins up startups to deal with the challenges as they come up. Some startups will succeed and others will pivot or die. This dynamic structure behaves more like a tree and less like a tower.The process of transformation itself is a series of lean startup style experiments . The diagram below describes the org structure in more detail.

TheStartupWay

Organization Structure for the Startup Way

Startups can be nurtured within the project teams with ambitious and passionate individuals hiring team members and “moonlighting” their way to launch. At some point this team would get blessed by the “innovation board” with a round of metered funding. This means the money comes with strings attached. Innovation board would ensure survival of the fittest by continuing to fund only the few deserving startups. Finally the successful startup would become an integral part of the organization by becoming a revenue earning , profit making project team or business unit.

The entrepreneurship function is just like any other staff function such as marketing or finance. The functional head is in charge of a knowledgebase of validated learning gathered from failed experiments. She will also ensure that the innovation boards do their job of guiding and nurturing the intrapreneurs.

Everything that is written in the book is supported by actual work done in several large conglomerates like GE and Citibank. Its very hard to judge the level of success given the size of these organizations. Most of the evidence is anecdotal. “The Startup Way” brings hope to the large hierarchical dinosaurs – may be they would metamorphose into nimble and vastly more intelligent forms.

The biggest take-away from the book is the accountability that lies at the foundation of the transformation. It brings some sense of control to the whole process of experimentation which is likely to run amok in endless iterations. Measuring validated learning by monitoring a leading metric which serves as a proxy for the net present value is at the center of innovation accounting. The proxy metric also needs to be corrected by accounting for the probability of it actually resulting into a certain net present value. This makes it easy for the innovation board to do apples to apples comparison between various opportunities competing for the next round of metered funding.

Execution Behavior change Customer Impact Financial Impact
Did we do what we said we were going to do? Are our people working differently? Do customers (internal and external) recognize an improvement? Are we unlocking new sources of growth as a company?
Project Team Is the founder and the team committed and stable? Has the team spent enough moonlighting hours on baking the idea? Does the idea pass the test of basic commonsense? Are they having a well defined business plan canvas? Do they know their LOFA? Do they hold P & P meetings? Do they run their experiments properly? Is there a recorded impact in terms of customer satisfaction? Shorter cycle time? Monetary savings? Customer referrals? NPV of business model. Productivity gain? Savings?
Business Unit What is the % of employees in startups? % of startup founders in this BU? What are the number of successful projects in the BU? Does the BU have mechanism of celebrating failures and capturing the learning? Employee morale? Per project cost? Average time taken by projects before first customer demo? Average cost incurred before first customer demo? Growth of BU revenue? Billable Head Count? Total Valuation of all projects incubated by the the BU
Company What is the % of employees in startups? % of startup founders in the company? Success rate? Is everyone talking about LOFA , Experiments and NPV? Number of pitches to the innovation board? Is the process simple? Are we able to attract better talent? Is it impacting Net promoter score? Are the number of red days zero? % of deep green accounts? New products launched? Value of phantom stock, Growth in revnue , billable head count and EBITDA, % of resources on bench

The cells in the table suggest some leading metrics for each stage from execution to behavior change to customer impact to financial impact.

In the end I would like to leave the readers think about a quote from Jack Welch. “ “If the rate of change on the outside exceeds the rate of change on the inside, then the end is near.”

 

Blockchain and Smart Contracts Will Change the Game From Big Upfront to Lean Experiments.

We often make big upfront commitments , decisions, investments and assume the risk of not achieving the desired outcome.

Screen Shot 2017-08-10 at 4.18.31 AM

We hire a software developer or a team of developers at the beginning of the project. Often we are required to define the technology stack and expected level of complexity up front. We wouldn’t be able to hire the team without doing so. Our choice of technology may turn out to be a bad one. Or we might end up committing to a higher budget by hiring a team with higher than the needed level of seniority. But we can’t go back on our commitment and switch to a better team of developers with appropriate level of seniority and expertise in a new technology.

Welcome to the world of smart contracts and blockchains. In not too distant a future it might be possible to enter into a short 2 week contract with the team. (Please read my earlier post) Each developer gets automatically paid in cryptocurrency as their code passes all the automated tests and is deployed in production. At the end of the sprint we have the freedom to suitably modify the team composition by entering into a new set of smart contracts. This gives us the freedom to experiment and evaluation a few options.

What changed is our ability to enter into smaller , more frequent transactions without the need of a central aggregator such as a Bank or the Corporate HR department. Aggregators can’t economically work with shorter contracts and smaller payments because there is a fixed overhead associated with each contract and payment.

The table below shows a few more examples where such a change can occur.

Big Upfront Lean Experimental
Buying a car Sharing a ride- No aggregator such as Uber or credit card needed.
Buying Servers Virtual servers on the cloud can be rented on pay as you use basis- going forward there will be no need to use credit cards. Cloud provider will automatically receive their payment in cryptocurrency.
Buying heavy machinery Renting made possible by more details about usage, wear and tear and damage if any will get communicated by data streamed by IOT sensors
Launching a company to enter a new business with all the associated overheads Assembling a team loosely bound by smart contracts. Blockchain ensures compliance without the protection of corporate law
Group health insurance- healthy members pay more and those susceptible to health issues pay less. Claiming insurance is fraught with legalese embedded in fine print. Wearable devices communicate the current health status frequently enough that makes it possible for a healthcare provider to directly enter into a smart contract with the individual .

This new way of working has become even more possible because of recent advances in IOT which will enable digitalization of lot of information about the physical world. Broader acceptance of “shared economy” is pushing individuals to “rent & try” instead of “buy & regret”. All this works very well for those who think and work in the lean and agile way. It gives them the freedom to conduct experiments to validate their hypotheses before making a large commitment. It lowers the barrier to entry into businesses which were a territory exclusively reserved for the big corporations.

Future of Work- impact of blockchain and smart contracts

Scope of this article is limited to innovative /learning organizations that are responding to constantly changing world. Traditional hierarchical organizations like the military or church are out of scope.

Distributed Autonomous Organizations (DAOs)will come into existence. These will have the advantages communities have over hierarchical structures. Individual team members will be bound by smart contracts and there won’t be any need for a corporate entity to govern their relationships.

blockchainforsmartcontracts

The risk of failure of a project will be assumed by an individual stakeholder who will be rewarded as the owner of the outcome if it succeeds.

These DAOs will be short lived – their lives will be limited to the project for the purpose of which the team is put together. Once the project is over the team will be disbanded.

Team members will be bound by a smart contract to the DAO. These smart contracts will ensure that no central authority is required to enforce the terms of the contract.

There will be no managerial overhead required to supervise individual team members and to ensure that they are delivering the work that they are being paid for. Smart contracts will automatically enforce accountability and discipline.

Work itself will be more interesting with one individual playing multiple roles – a leader in one project could be an intern in another project.

With smaller teams engaged in number of small short lived DAOs executing innovative projects the job of hiring team members will become more frequent. One can’t spend 12 weeks (as in the traditional recruitment process )searching for and evaluating candidates for a project which might last for 20 weeks.

More automated ways of recruitment using AI and Machine Learning will help recruiters and DAOs to reduce the time to hire to a few days – not weeks. Teams will get formed by like minded individuals finding each other. Trust relationships will be more easily forged by smart contracts.

Jobs will be replaced by assignments. An individual would be engaged in multiple assignments simultaneously. Each assignment will be customized to suit the needs of the project. Working hours, place of work, compensation, skills needed etc. will depend on the project. Designations will be replaced by multiple roles

Individuals will develop T shaped skills with depth in one area and interest and working knowledge of many other areas. In fact they will relish learning new skills by offering to work under a relatively younger team member. Reporting relationships will become more flexible- one can be reporting to a person who is a reportee on another project.

DAOs will pay individual team members in cryptocurrencies which will ensure that the economy in which the DAO is participates will thrive increasing the value of the cryptocurrency as compared to the fiat currency.

Monthly earning will depend on the value added and will reduce over the years if new skills are not acquired. There will be less politics as people will stop holding higher authority and getting paid higher merely because of their seniority. Authority and leadership will be short lived and based only on merit.

There will be a wider acceptance of the fact that intellectual property grows by sharing it openly , by maximizing its use and inclusion than by restricting its use and exclusion. Open source culture will gain ground. IP rights, patents and litigations will fade and become history.

Smart contracts will ensure that royalty, license fees and commissions will be automatically paid directly to the creator triggered by use of the digital asset. Intermediaries aggregating , distributing and collecting payments for digital assets will go out of business – the way music publishers like HMV and Nickelodeon went. Creative talent will be richly rewarded leading to an age of ultra modern renaissance.

Individuals will stop taking comfort in “getting hired” as the project to which they are assigned may not last more than a few weeks. Lifetime / Long Term employment with retention bonus etc. will become things of the past.

Smart contracts won’t need corporate law to ensure implementation. Cryptocurrencies would isolate these contracts from vagaries of fiat currency fluctuations. People working across national borders will be able to enter into contracts and get paid for their work without bothering about corporate laws of any particular nation. Geographical proximity would become less relevant. Teams will be formed based on mental proximity than physical proximity.

Dos and Don’ts of Lean Startup- Top Takeaways from The Lean Startup Conference 2014

Lean Startup Conference 2014

Lean Startup Conference 2014

Main Takeaway- Continuous Experimentation Well Beyond The Startup Stage

Contrary to the generally held belief that lean startup principles advice experiments in early stages of a startup; many speakers at the conference showed how they are experimenting continuously at all stages of their ventures.

Eric Ries in said that “Product market fit and experimentation is not a one time activity. It’s a continuous flow of activities. There are no discrete big jumps! Think of these steps in continuous flow that lend themselves to go back if an experiment fails”

Hiten Shah of Kissmetrics reiterated that a meaningful metric leads to a hypothesis and then to an experiment to validate it. Startups should always be A/B testing. Empirically 1 out of 5 tests succeed. Strive to win 1.67 out of 5.

A/B testing can help not only at different stages of a startup; but also for various activities including website traffic, app installs, welcome emails, Web/Mobile onboarding, E-mail digests, Triggered notifications, dormant/churned users.

Des Traynor also said that having continuous feedback is more valuable than one time event driven feedback.

Experiments helped even established brands like Rally, Google and Vox Media to validate hypotheses at later stages of their product lifecycle

  • Rally launched a dummy brand waffle.io targeted towards developers to protect the parent brand from the impressions created by the experiments. Finally Rally decided to have both brands.
  • Google Adsenses team validated Partner Problems using Lean Startup Principles. Blair Beverly said that they faced problem with new projects scaling too early and failing as they lacked historical data to go by. He got coworkers at Google ad senses team to use the Lean Startup. They scheduled office time to read the book, being helpful and not pushy. They also gave them a reading guide with questions. In the end they identified three hypotheses; put together templates like the partner problem hypothesis. People felt good about invalidating their own hypotheses as it saved them work that would’ve been wasted.
  •  Vox Media launched Vox.com in 9 weeks using analytics to guide customer validation. Melissa Bell got her co founders and others from Vox media in the same room to get everyone on the same page about her vision. Many editorial staff came from Washington Post whereas Vox was an agile technology company. They used card stacks for flexibility. They had problems with the way editors used card stacks, as it was difficult to navigate-hence they analytics were used to solve the problem. Now Vox.com has 22m users. Delivering content to users where they are-on social channels such as Facebook or YouTube instead of own URL.

Lean Startup- Dos & Don’ts

Max Ventilla

  • Pivoting statistics- 80% of failures didn’t pivot, 65% of successes Pivoted but 85% of Huge Successes (>$1B exit) didn’t pivot. Those didn’t pivot felt that evolution is safer than betting on intelligent design.
  • You need to eat your own dog food. Use your product to solve your own problems. If not you are at an enormous disadvantage.
  • Invert the org chart :customers & customer facing team should be on top. They should be heard and not told what to do.
  • Force yourself to pretend at the earliest possible moment what you want to be- to learn whether its worth being what you want to be. Landing pages, Concierge or Wizard of Oz are ways to pretend.
  • Don’t speed up for the sake of it. For startups not going fast enough is not the main risk. False summit is the reality. Journey of a startup is slow like that of a mountaineer. A new goal appears once you have reached what seemed like the ultimate goal.

Grace Ng

  • According to Grace Ng success criterion for any experiment is the weakest outcome that will give you enough confidence to move forward.
  • Testing the riskiest assumption on buy side in a two-sided market place could be deceptive in a sellers’ market. Sellers may not automatically follow even if you find many buyers.
  • Validated hypothesis doesn’t necessarily lead to a viable business. Grace Ng tested a hypothesis whether birdwatchers will post photos to ask questions. The Hypothesis was valid but the problem turned out to be too small – not a big pain-point.
  • Don’t validate the solution before validating the problem. As in the case above; the problem was not big enough though the solution was right.

Eric Ries

  • When it takes too long to learn as end results take time, use proxy metric like number of likes or start a cohort.
  • Don’t depend on one experiment to determine the product market fit. Keep testing and validating along the way as you grow. Growing too fast by taking product-market fit for granted is dangerous.
  • Don’t get misled by corporate America’s habit to underinvest or overinvest. “All Hands On Deck” sounds great but surely is a sign of overenthusiasm.
  • Avoid handing off innovation between silos. Handoffs kill innovation. What is learnt in one silo can’t be handed off to another silo.
  • Don’t add features for the sake of it. Its better to err on the side of being too minimal to get early feedback and learning. Its easy to add a missing feature later.
  • Pay more attention to paid users’ feedback than free users’ feedback. Free users ask for more; paid users ask for better.
  • Don’t use vanity metrics- Eric’s law: At any time no matter how badly you are doing there is at least one Google analytic graph that’s up into the right

Joanne Molesky

  • As you go through build-measure-learn cycles for product the same way you should be going thru build-measure-learn cycles for process compliance.
  • Beware of developers’ tendency to focus on how to do things than on outcomes. Developers tend to ignore security as they are dazzled by technology, so they focus on doing things faster-not safer. Security testing, threat model and risk metrics should be included right from the beginning and not at the end.

Dan Milstein

  • Don’t take idle pleasantries as positive feedback. People tend to be polite and cordial even though they are least interested.
  • Don’t choose to see what fits in a narrative that sounds good and makes you look awesome. That is self-deception. Realize that a startup is a series of unpleasant encounters with reality.
  • Don’t own a plan. Own questions. Plans will change.

Hiten Shah

  • Test small changes- Google sign on and changes to verbiage improved acquisition by 314% for KissMetrics.

Brant Cooper

  • Don’t as two questions that kill breakthrough innovation – what is the roi? When do we get it? In order to answer these questions we have to look at existing markets which kills innovation -innovator’s dilemma. We need to build cultures or safety net for innovators.

Conclusion

Most of the takeaways and dos and don’ts are common sense for any practicing entrepreneur. According to Eric Ries The Lean Startup process is more widely practiced than talked about. Most entrepreneurs are agents of long term change. They don’t think The Lean Startup is a big deal. As with most profound thoughts- it seems obvious after its well thought through, well organized and well presented.