Flown from across the world by pilots operating from a cockpit simulator with visuals representing the images being captured by the drone, the pilots control the aircraft using the visual and sensor cues. The pilot’s experience is almost the same as being in the aircraft except that the pilot is not in any physical danger. Modern sensor and control technology combine digital sensors for position, attitude, altitude, pitch, actual visuals, infra-red and electronic surveillance to create a fusion of data back at the pilot station.

One might be tempted to think that all one has to do is to present the information to the pilot and provide the pilot with controls that drive the actual flight controls in the drone and all is well. Not quite. The tough problem is that it takes a certain amount of time for the sensor information to reach the pilot station on the other side of the world, and the controls applied by the pilot take as much time to get back to the drone.

Imagine driving a car at 60 miles an hour, when a deer suddenly crosses your path 50 feet ahead.  There’s a 1/4^th second delay as your mind registers the need to act; there’s another 1/4^th of a second for your brain to send a signal to your foot; and one more 1/4^th of a second for the car to apply the brake to the wheels after you’ve hit the brake pedals. That represents a total of 66 feet traveled before the car even begins to slow  down  – and by then, it’s already too late. The reaction time of such critical, real-time decisions must be faster than the action to which it’s responding. The same is true for drones; by the time the pilot tries to reverse the bank of the drone – caused by a gust of wind – the drone may have already banked a lot more than it appeared to the pilot. The pilot ends up initially under correcting, then over correcting and quite soon the drone is uncontrollable.

But we know that drones fly well and very reliably. That is because the short term, quick reacting responses needed to keep the drone flying is handled by a smart controller or computer in the drone itself. A gust banks the aircraft to the left – no problem – the computer instantly commands the left wing aileron down and the right wing aileron up to correct the bank. The pilot does not need to worry about these kinds of low-level technicalities, leaving her free to focus on decisions that require her human judgment, like which heading to fly and what altitude to maintain.

Meanwhile, the mission director is making an even higher level of decision; he’s interested in completing the mission – whether it is surveillance or finding and destroying a designated target. He uses the same current location data and other sensor data from the remote drone to decide the safest route to the target, the best way to manage the total flight to it and back to the base.

Notice that there are three control loops at work here – a control loop being loosely defined as a combination of “observe-decide-act” by a human, automatic control system, or hybrid of both. The fast-responding controls within the aircraft; the tactical controls with the pilot in the loop; and the longer strategic controls with the mission director in the loop.  All three loops work in a cascading manner one above the other, and all three use the same sensor data. But each one samples the data differently and they use different logic to meet their specific ends. Together they execute the mission at each level smoothly, seamlessly, each control or decision loop extracting the kind of information it needs from the very same single source of sensory data.

This is not a unique case of cascading control loops. Every modern refinery, most modern automobiles, fly-by-wire aircraft like the Airbus or F-16, our human nervous systems, and many social organizations such as well run companies are all examples of such cascading control loops. In each case there are, broadly, three levels of control loops each one nested within the other. The innermost loop takes care of the fast reacting, operational responses. The middle layer samples the same sensory data and applies a higher level of smart feedback controls usually by inserting a human being into the control loop so that tactically significant decisions can be made. Typically these are medium term decision in terms or time and response. Outermost are the strategic decisions made with long range understanding of the sensory data, and carefully contemplated long term decisions whose impact will be also be felt in the longer term.  For the past decade or more there has now been a concerted effort to apply these concepts to the oil field for a number of reasons. 

Stay tuned for our next post where we will explore cascading control loops in E&P companies, and how ‘Smart’ Oilfields should apply these concepts.

If you’re a scientist working for a pharmaceutical company or a geophysicist working in an Oil & Gas exploration company, orienteering can mean much the same except that the wilderness is the unchartered landscape of not-yet-discovered knowledge and insights.  It can be the key to making important new discoveries and connections that you might never have otherwise known existed.

In the lingo of Orchestra™, our ontology-driven platform, orienteering is a technique that allows users to chart multiple foraging pathways through institutional data, to understand the context of their search results, and to explore new and often unforeseen relationships across assets, people, applications, and geographies, breaking down these traditional silos.  We call this area of search ‘orienteering’ because it has some things in common with the Boy Scout version:  there is the same guided navigation and sense of exploration and discovery. The metaphorical mud, if you will, is the metadata picked up in the contexts of the waypoints.

Of course, finding the destination is only part of the objective.  The path to reach the desired destination is part of the essence of orienteering – both the sport and the search technique.  Defining the best path to an insight or discovering interesting side paths (roads less traveled, as Robert Frost would say) is just as much the objective.  These paths defined by individual scientists and explorationists can eventually form well-traveled trails that can be retraced and eventually become codified knowledge on which institutional decisions can be made.

This exploration is the key to scientists, geophysicists, and business users connecting patterns across disparate, disconnected areas of knowledge, seeing new patterns that were previously hidden, and interacting with their peers for collaborative discovery.  For a scientist, these discoveries can lead to innovation and the next successful drug – or the early realization that a substance in the pipeline is likely to fail, thus reducing costly late-stage attrition.  For geophysicists, these discoveries can yield better exploration decisions.  And for corporate leadership, these discoveries can lead to better strategic decisions.

The term ‘orienteering’ was first used in 1886 and it meant “the crossing of unknown land with the aid of a map and a compass.”  In many ways, “unknown land” is a good description for the vast, untapped knowledge stores of the typical large corporation.  They’re both big, full of immensely rich resources, and impossible to traverse without the right tools and guided navigation.  In fact, if we were to pursue this analogy accurately, not only does Orchestra™ offer the map and compass, but it also offers the boat, bike and snowshoes to actually get across the diverse and siloed institutional knowledge landscape.  For pharmaceutical and E&P companies both, orienteering can offer the “guidance” they’ve been seeking to a better pipeline.

To learn more about orienteering for pharmaceutical companies, read our Search & Orienteering brief.

Learn more about Orchestra™ here, or read about why an ontology engine drives Orchestra™ in this white paper by Dr. Suresh Madhaven, CEO of PointCross.

That’s why, unlike most technology and SI firms who provide services on a time-and-materials basis, we fix-price our offerings.

There’s a reason this is so unusual in the enterprise technology market:  there is way too much (perceived) risk, making it a really bad idea for vendors who have co-opted IT buyers.  IT staff often have little direct experience in developing applications, and rely on vendors’ estimates and perceptions of risk.   Scope creep is another reason – both sides tie themselves down to firm specifications instead of working to meet intent and managing change at the lowest cost. All these risks and more can easily increase, if not double the initial estimated cost – often in the millions of dollars in a typical enterprise technology implementation.

And yet, we do fixed pricing anyway.  We do it because we feel that our customers should not have to shoulder risks that we as the solution provider are in the best position to mitigate. It is a matter of professional etiquette that we size the risk, the range of uncertainties, and work to achieve satisfaction with as many iterations as it might take.  We are driven by the beliefs that “customers are not always right, they always have rights” and “users may not always know what they want, but they always know what they don’t want.”

We are also more assured than most in taking on the risk ourselves because:

1. Orchestra™ is built on an ontology engine, which makes it a fundamentally agile platform.  All of our solutions built on top of Orchestra™ are equally flexible, which means that we can adapt to changing business requirements quickly; and
2. Orchestra™ is compatible with most other software systems because it sits on top of a company’s current infrastructure, provisioning knowledge resources as needed.  We don’t disrupt a thing, which makes implementation far easier and less risky.

We also do fixed pricing because it makes our covenant with our customers simple: we are incented to finish delivery quickly and to their satisfaction; in fact the earlier we finish it is more profitable for us, and better ROI for them. Our customers meanwhile do not have to stress about budget and late projects, or worry about making compromises on the specifications or changing their mind on requirements.  By aligning our goals with our customers’ goals, we’re able to serve both of us best.

So we think we’ve found one possible solution to the skyrocketing cost and high failure rate of enterprise software; now we think that companies should start demanding it from all their IT and SI vendors.

What do you think?  Leave us a comment below if you agree…

As we discussed in the last blog, the brain is a complex thing, and we are therefore not going to attempt to recreate it in all its glory. Instead, we seek to borrow a few key principles from the human brain to create institutional memory and a kind of higher-order ‘intelligence’ within the corporate body. This simple approach is effective because, after all, very intelligent, thoughtful people – your employees/colleagues – connect to this common institutional memory, and provide all the remaining cognitive and decision functions for the organization.

The first capability of this sought-after organizational intelligence is that it connects knowledge across the organization and reconciles both structured and unstructured data, regardless of sources, formats, applications, departments, and geographies.

The second, and perhaps most critical, capability is that of contextualizing all this data, reuniting far-flung pieces of knowledge with their contextual families. This contextualization of information – and clearing out noisy, unrelated information – is at the core of creating institutional memory in a similar way that the brain develops its long term memory. By associating pieces of knowledge as they are created – such as emails, reports, presentations, etc. – with organic, real-world contexts (which can be thought of as concepts that employees deal with on a daily basis, like ‘sales’ or ‘oncology’ or ‘project XYZ’), an organization creates and evolves its own, organized institutional memory in real-time. In doing so, it gains access to institutional knowledge in a way that all employees (not just a few power users or IT) can understand, benefit from, contribute to, and evolve. This ability is so critical because it is what gives meaning to data – transforming it into knowledge – and it is what gives employees access to and (secure) control over the knowledge that they need to do their jobs effectively.

The third requirement of this organizational brain is that it offers a variety of transportation choices through the institutional knowledge to help employees discover what they are looking for, including search (teleportation), orienteering (discovering a path through exploration), and faceted search (selecting qualities that lead to a knowledge destination).

The fourth capability is the ability to automate and integrate the daily workflow of the organization, including the data that makes up the inputs and outputs of those processes, with the rest of the organization’s knowledge capital and associated people. Connecting the people, processes and knowledge of an organization is analogous to connecting a person’s body to its brain – both are needed to work in synch for the body to function well.

The fifth capability is foolproof security based not only on the user and her role, but on an individual user’s relationship with individual contexts. This makes for a tremendously nuanced control over the way that knowledge is managed across the organization – something that is critical to industries in which knowledge is a precious asset.

These five capabilities form the basic structure of a powerful, useful, organically evolving institutional memory that is capable of serving small teams or large groups. Additional capabilities are available and desirable, as we’ll discuss later, but for now, we’re going to spend a few days discussing each of these capabilities in a bit more depth – what they entail, why they are important, and how, together, they will revolutionize the way companies think about knowledge.

With up to 33 billion neurons (depending on your gender and age), 10,000 synapses per neuron, and 200 decisions per interneuronal connection per second, your brain is theoretically capable of somewhere on the order of 66 million billion calculations, insights, and decisions every second.

When you touch a hot stove, the brain and nervous system carry that information at speeds of up to 268 miles per hour – from your fingers to your brain and back again, allowing you to remove your hand within milliseconds. If you have a brain like Einstein’s, you can map the universe on the back of an envelope; if your brain is like Shakespeare’s, you can string together words that speak to the fundamentals of human nature, regardless of culture, experience or epoch.

In other words, the human brain is capable of extraordinary things. So it would seem to take a great deal of hubris on the part of any individual or company to attempt to imitate it. But as we said in our last post, we are not so presumptuous as to think we can replicate the brain in all its glory. That’s not necessary. It is often the modest attempt, built off correct principles, that yields the most successful results. The airplane took only a few basic principles from its equivalent in nature – wings, tail, and the physics responsible for lift – and it successfully applied them to create the basic result that we sought: safe, comfortable, soaring flight – and the ability to get from New York to London in 7 hours instead of 7 days. Our goal in imitating the human brain is similarly modest – and similarly revolutionary: it has to do with lower-order thinking versus higher-order thinking.

Our brain performs billions of subconscious decisions every day: it keeps our heart beating, our lungs breathing, our liver and kidneys functioning, and a million other complex operations that we are not even aware of. At the same time, our brain is capable of conscious, but lower-order decisions, such as reciting the alphabet or describing a book we’ve just read or getting into a car and remembering the route to work and how to drive. These acts are conscious, but they are rote and often so familiar that we are barely aware of them. And then our brain is capable of higher-order thinking. This is the act of executing a complex mathematical formula, composing a sonnet, or learning to play an instrument.

In our analogy of a technology-enabled organizational brain, databases and the enterprise software they support offer organizations increasingly efficient and effective means of performing ‘lower-order’ operational functions that keep the organization going: payroll, supply chain, etc. They do an excellent job at sending billions of pieces of information flying around the corporate body, keeping it stable and in working order, and this is essential to all organizations, regardless of industry.

But what is missing from the myriad of enterprise software solutions on the market today is a capability equivalent to higher-order thinking – organizational creation, creativity, consciousness. Think of how important this capability is to knowledge-intensive organizations. Even the meanest of organizational intellects engage in lower-order thinking, but for knowledge-intensive industries such as Pharma, upstream Oil & Gas exploration, and others, higher-order thinking is crucial to their existence.

They are the Einsteins and Shakespeares of the organizational world – individuals who are defined not by their ability to drive to work, but by their ability to create these new and extraordinary gifts for mankind. So it is with knowledge-intensive industries that their raison d’être is defined not by their ability to crunch finance numbers or execute a Six-Sigma black-belt in Supply Chain, but by their ability to make complex, often intuitive, strategic decisions, and ultimately to create knowledge and products that did not exist in the world before. This is higher-order thinking.

Thus, our primary goal in creating a technology that mimics the human brain is to imitate specifically the organic, creative thought-center of the brain, the cortex, and in doing so enable ‘higher-order’ thinking and organizational consciousness.

Stay tuned for the next post where we tell you how…

I would like to share with you a few of our accomplishments of the past 10 years and some of our aspirations for the future.

We have crossed a threshold in our evolution as a company. We are no longer a start-up, and yet we have retained our entrepreneurial spirit and nimble adaptability. We have been self-funded since 2001 and profitable since 2004; we have no operating debt. We grew very well and profitably through the recent recession. We acquired a small company that allowed us successful entry into the health-sciences market. We don’t have VCs to contend with. Very few of the technology companies that started with us in 1999 remain in existence – most folded; the remaining were acquired by other entities and lost the promise of their original technology. Of the few that remain, most have changed their business model and elected to provide services and consulting to shore up revenues. Today, our technology runs in some of the top companies in the world including Shell, Hoffman La Roche, and Pemex among so many others.

PointCross came through the trials and successes of the past 10 years stronger, even more agile, and even more effective in delivering the most advanced technologies for enterprise search, institutional memory, decision support, secure communications and strategic business processes. We built a very strong culture of innovation and service with intellectual honesty that is uniform across our globally deployed virtual team. This culture is strong enough to sustain itself into the future. We are tuned in to the trends in the market and what our customers want and need. We are confident that our products, services and culture are right for the needs of this tough market and the future.

Here are some of the trends we see in the market:

• A strong demand for interactive, navigable search for authorized data and content across the enterprise. Unstructured search engines are great at finding a large number of documents from a huge mound of documents – but E&P professions need to know if there is a well log that suggests a particular lithology; and Pharma scientists want to know if a certain parameter in an animal’s blood shows a specific behavior compared to the changes in liver function in any study.

• A strong push to drive IT costs down by increasing the value from software while reducing the number of applications in the inventory, as well as the time and expense of implementing enterprise-wide solutions which now seem inevitably to stretch out for years and hundreds of millions of dollars. 7 days for one process; 20 weeks for 20 processes – no specs no quarrel – all fixed price; that is what they want.

• An integrated, seamless environment that customers can securely extend without fuss to external partners and vendors. There is no patience for siloed software that will not inter-operate and work as one.

User Forums and “I Wish…”

I Wish… has been very successful for us and for our customers. It has allowed us to hear the voice of the customer well and for us to react fast to those voices. We have failed at times but we use the metrics to learn from our mistakes and continually improve. But we also understand that there is a lot of value in having a forum where current and even prospective customers can interact and share their views of the future and their needs so we can accelerate our roadmap and delivery. To that end, we are requesting that customers and others in the industry join our Orchestra Conductor forum where you will have the power to influence our roadmap and direction. Please send us an email join us.

About a month ago, we launched this blog as a way to share some of our serious, and many of our not-so-serious views about the world of enterprise software, business, trends, and the latest products and breakthroughs that we’re working on. Please take a moment to look around our blog, share a comment, and come back now and then as we continue to share exciting new developments.

The reason we are introducing these new forums of discussion and collaboration with our customers is because this is the best way we know of evolving our technology and ourselves to better serve you. Let us know how you feel – your thoughts influence us.

In the last 10 years we have built a company that you and we can be proud of. We have developed relationships with our customers, partners, and with each other as an organization that are founded on strong, solid principles. In the next 10 years, we intend to accelerate, gain market share, change the way enterprise software is built and delivered. Many of you appreciated the advanced ideas we came out with ten years ago and the market is now getting it. But we are also ten years ahead again. The strange looks we got when we talked about contexts and ontologies in the early 2000s are now vigorous nods of agreement. We intend not only to ride the crest of this wave, but to define it.

For those of you who have been customers and partners, I thank you for your inspiring partnership over the years, and the ones to come; for those of you who are meeting us for the first time, we look forward to new vistas of possibilities and collaboration with you.

Suresh Madhavan, Ph.D.
CEO, PointCross Inc.

But there was another invention from about the same time that had been centuries in the making, and unlike the automobile, it took its inspiration directly from its counterpart in nature. We are talking about the airplane.

Human attempts at flight have been recorded as far back as Ancient Greece over 2,000 years ago. But throughout most of history these attempts have been few and far between – or at least the recorded ones were. We can only imagine that this was because (a) it was rather dangerous to throw oneself off a building or mountainside relying on an unproven technology to save your neck, and thus no one wanted to risk it, or (b) people did risk it, but they lost their necks in the process, and were therefore unable to tell anyone about it.

Among the most famous failed attempts that we know about was Leonardo da Vinci’s flop around 1505 at the top of Mount Ceceri. Luckily da Vinci did not break his neck (legend has it that he had a student do the test flight, who broke not his neck but his leg in the process) or else he never would have finished the Mona Lisa, among many other achievements.
The significance of this story is that da Vinci, like his Greek predecessors and many others who followed him, modeled his flying machine after birds.

As early as 1485, Leonardo da Vinci invented a mechanical wing, which he drew in his Codex on the Flight of Birds. He observed the movement of birds’ wings and tried to replicate their motion and mechanics, making careful study of the center of gravity of various fowl, wing structure, feather alignment and other anatomical study. As it turns out, none of da Vinci’s designs ever could have gotten off the ground (although he did learn a lot about birds). Da Vinci was, in this as in so many things, a man ahead of his time, and lacked both the necessary materials (canvas and wood were too heavy for his purposes) and knowledge of physics for the task.

It wasn’t until 1738 that the Dutch-Swiss mathematician Daniel Bernoulli told us exactly how an airplane could achieve lift. Put in simplistic terms, it is, he said, because air passes over an airfoil (a curved wing) faster than under it, causing increased pressure bellow the wing and less pressure above the wing. This created the necessary lift – and it gave the Wright brothers the necessary knowledge to transform da Vinci’s failed attempt at the top of Mount Ceceri into success at Kitty Hawk.
This, too, was a case of disruptive change in the transportation industry, but one that was based on nature, rather than a departure from it.

We tell this story because we believe that this example of technology derived from nature is a powerful and accurate parallel of how databases will be replaced (although it won’t take millennia to do so).
The reason that databases are bad at handling knowledge is because knowledge, as opposed to data, is an organic thing. Just as certain principles of physics needed to be understood before we could achieve flight, we cannot accurately model knowledge in technology without understanding the origins of human knowledge: the brain.

Godel would tell us that accurately modeling the brain is impossible – and perhaps it is, or perhaps it will take us millennia to do it right. Leonardo believed the same thing about flight: he said, “Human subtlety will never devise an invention more beautiful, more simple or more direct than does nature because in her inventions nothing is lacking, and nothing is superfluous.”

As in most things, Leonardo was probably right about this, too. But that doesn’t stop nearly 3 million people from boarding an airplane every day and soaring, safely, comfortably, tens of thousands of feet in the air and traveling thousands of miles to their destination in a fraction of the time that it would take using any other modern technology.

It may not be a perfect technology, but it’s certainly a positive step in the right direction. And, at least for now, we feel that this is a sufficiently modest goal when it comes to modeling the brain in information technology.

But as we discussed, the automobile didn’t actually kill the horse – it just made the horse moot. The car was a creation so far superior that the horse was no longer a desirable option for most people. The car was faster, more powerful, more comfortable, and easier to maintain than the horse. It was scalable (some of the larger earlier models rivaled small trams in size, and now of course we have the Hummer) and it was adaptive (the Ford Mustang evolved more in a decade than its namesake in the animal kingdom had evolved over the last 2,000 years). It also didn’t hurt that cars were less… messy than horses.

The birth of the automobile is an example of true transformation. It was not the next logical leap from the horse; it was not Horse 2.0. It was, if you’ll forgive the pun, a whole different animal. Henry Ford’s assembly line then helped put this new ‘animal’ in garages around the world, and ushered in the modern era.

Nowadays people have taken to calling this kind of change “disruptive.” Clayton Christensen, the coiner of the phrase, defines disruptive innovation as change that “makes things simpler and more affordable.” We would add to that definition that it should be something that changes the way we think about the aspect of our life that the innovation affects. Just as the car changed our paradigm about how we move from point a to point b, the successor to the database will change the way we think about knowledge: how we store it, connect it to other knowledge and to people, navigate through it, access it, and use it.

This is the kind of change that the enterprise software industry needs today – and by ‘enterprise software’ we mean those large-scale software packages intended to solve a variety of issues at the enterprise level (but that often create more problems than they solve). F or all the reasons that we discussed in the previous series on the Death of the Database, the enterprise software industry needs some serious disruption.

The IT industry as a whole got an enormous transformation with the Internet. But enterprise software, while it has evolved little-by-little every year, has not experienced true disruptive change for a very long time. Databases were born 50 years ago, and it’s been 40 years since Edgar F. Codd developed the relational model, which remains the standard today. Since then, only incremental improvements have been made to the way that databases, and the large-scale software they support, handle knowledge. This is an industry that is begging for a shake up.

It also begs the question – if the automobile was the Death of the Horse, what kind of disruptive technology will bring about the Death of the Database?

Stay tuned…

The problem is that for the last 50 years or so technology applications have burgeoned inside enterprise walls so that companies can no longer see the forest for the trees.

Every new application added to the jungle adds exponential complexity to a company’s architecture. Each application deals in data in its own way, storing it in proprietary formats, usually within in its own server, database or on employees’ desktops. The result is a fragmented, siloed data architecture that makes it impossible for companies to intelligently access their own knowledge. In other words: most companies don’t know what they know.

Even technologies that are intended to help employees communicate more effectively have the downside of creating yet another channel of information flow to be consumed and forgotten. Companies do not need another way to create more data; they need a way to make sense of the data they already have.
This Amazonian infrastructure is hurting companies more than they may think: every time knowledge is fragmented and relationships are hindered, companies lose opportunities for intelligent decision making and innovation.

The solution? Stay tuned for our next segment, the sequel to Death of the Database: “Long Live Ontologies!”

First of all, a company’s business processes should not be made to bend to a technology solution, and yet that is what many ERP solutions and other traditional database technologies ask of a company.

Not only does this interrupt and complicate a company’s already complex operations, but it often takes years to implement, there is a high risk of failure.

Even if the technology is implemented successfully, thousands of employees may need to change the way they work, which results in an even longer de facto implementation before the return on investment kicks in, as well as an additional risk of failure to adopt.

The best-run businesses may run certain ERP solutions, but next time you talk to one of them, ask to see their wallets and their war wounds.

The other day we posted a story about the MIT kids who launched their version of the Hubble telescope into space for a grand total of $150. Okay, so their version can’t measure redshift and plot the origins of the universe, but it was pretty impressive considering it cost about as much as one screw on the Hubble.

IT companies and system integrators need to find that entrepreneurial can-do spirit again. Industry companies need to start expecting more for their money from software companies and systems integrators. They need to start demanding technology solutions that actually deliver value within the first few weeks or months, not only after several years.