Augmented Reality as a Service

Crime Predictive Analytics
Predictive Analytics Visualization of Crime Patterns

The graphic above is a visualization of expected crime patterns in Santa Cruz, California from a Santa Clara University research project to help police improve crime prevention, decide staffing assignments, and anticipate where crimes might happen. This is not ‘1984’ or anything spooky, it is the next stage of augmented reality technology applied to solve practical problems. Get used to it, this is big!

In every aspect of our lives new technologies are changing the way we do things. Sometimes this can be scary. Will robots and AI take our jobs? Is the government or big business tracking my every move?

But increasingly we find ourselves wondering—how did I ever find my way around without Google maps and Siri directions? How did I get along before we had (fill in the blank) —-mobile phones, internet, digital cameras, online shopping.

You get the point. Disruptive technology innovation is both wonderful and challenging as we learn to make sense of the complexity that swirls around us.

  • It is not just data; it is the context in which stuff is happening that changes the insight we get from the data.
  • It is not just volatility; it is the rapid “what if” trade-off analysis we make even in the face of fear of making wrong choices.
  • It is not a lack of new software, devices, or more apps we crave; it is the convenience that better user experience brings in putting it all together fast enough, visualize options, and make split second choices that makes the difference.

The best balance between completeness and simplicity is a solution that integrates good tools, good data, good analytics methods for fast, easy, transparent, consistent actionable decisions. The miracle of turning water into wine is in the judgment to inform, shape, and target our decisions to fit the circumstance—-and the courage to say DO IT!

The reason that artificial intelligence, machine learning and augmented reality solutions are making fast inroads into our mainstream activities is our need to quickly, seamlessly and consistently integrate vast streams of data with the context, choice and convenience filters used to make sense of it.

The 5 V’s we face:

  1. Volume-how much data and information is involved and where are you getting it?
  2. Velocity-how fast is it changing?
  3. Veracity-do you believe the data? How frequently is it updating? Is that sufficient?
  4. Visualization-can you put this stuff together and assess it to gain actionable insight?
  5. Value-is the insight you gain useful, sufficient, timely and credible enough to make informed decisions—does it empower your judgment, experience decision?

Enterprise-scale business intelligence and predictive analytics solutions help us deal with the first three V filters in the list above by slurping in big data and quickly organizing it in data structures that are searchable with other user friendly software algorithms. These solutions rely on a common data framework, consistent analytics methods, scenarios or stochastic risk analysis to help create a fast, efficient, repeatable, integrated basis for decision analysis.

These solutions are huge improvements over previous generations or business analysis done in spreadsheets and pivot tables by people. But automating and integrating it into enterprise systems has been hugely expensive, time consuming and makes the business unreasonably dependent upon the enterprise software vendor and system integrator for mission critical operations success. From the customers’ perspective, the problem is the process can easily overwhelm and defer the value. The business pain point is that to get to value you seek you must find a way around complexities that prevent you from seeing the big picture.

‘Everything as a Service’ is the emerging business model of choice enabling startups and others to avoid expensive time consuming enterprise software projects that stand in the way of going to market faster. The promise of the tech transformation requires finding a solution. This is the ‘where’s the beef?’ question in every discussion of value.

Energy Industry Example of Disruptive Change. There is a tsunami of data washing over us. Instead of monthly energy meter readings of static start/finish consumption data, for example, we have 15 minute pulses of data 24/7 enabling pattern analysis, data feeds from customer side of the meter sensors, and the meter may spin both ways as net metered customers both buy and sell energy produced to the grid at dynamic prices.

Smart grid is almost always meant smart meter deployment. But smart meters are only one factor in getting to the promised benefits of smart grid. The other components are dynamic pricing, transmission access for renewable energy projects, and peer-to-peer clean energy transactions from customer choice aggregation and direct sales ahead.

Most end use energy customers are still billed on an average cost basis for energy use. States are slow to implement dynamic pricing because it scares utilities, regulators and politicians who fear customers backlash over price volatility. Like surge pricing with Uber, demand pricing is becoming more common in the electric power sector. But residential customers hate utility bill price volatility and most utilities still have “budget plans” which offer level billing to avoid rate spikes. This completely defeats the policy and economic intent of dynamic pricing.

And let’s face it, introducing dynamic energy pricing is a BIG change not likely to win many friends or votes. And then there is the problem of the utility business model. Only a few states have “decoupled rates” so that utility earnings are not dependent upon selling more kWh of energy. But unless rates are decoupled the incentives are in conflict and few will see many benefits worth the hassle.

A goal of getting more of our energy supply from clean and renewable sources such as wind and solar is a popular. Many states have renewable portfolio standards (RPS) requiring utilities to buy ever larger percentages of their energy consumed from renewable sources. But shifting power to customers to choose energy options also brings risk and potentially reliability issues.

The business challenge ahead as many states and their utilities near their RPS goals is will the state ‘declare victory’ or do as California has done and raise the target. The rate consequence of higher targets is substantial and when combined with rate increase pressures from smart meter deployment, emissions reduction and other regulatory demands make declaring victory an attractive but not politically correct decision.

Renewable energy cost more than traditional ‘least cost, best fit’ power supply options. Those higher costs have been offset by subsidies, tax credits and leasing options. But the future of subsidies long term is uncertain.

The political reality is solar photo-voltaic (PV) panels prices have fallen rapidly as the market is flooded with supply and energy demand growth is flat. Have wind and solar prices dropped enough that we don’t still need subsidies? This prospect of lost subsidies terrifies the renewable energy industry causing consolidation of the players. Meanwhile, competition ruthlessly and efficiently forces every player to get to grid parity pricing to be sustainable as prospect for energy industry business model and regulatory changes usher in competitive, peer-to-peer energy services markets to replace the traditional utility average cost business model. Think: like from a Ma Bell land line to multi-carrier iPhone.

So what about the value and benefits to customers?

Everything as a Service. From regulated monopolies and government control the disruptive technology revolution is giving us choice but getting to the value we seek from putting all that data, analysis and user-friendly apps together requires that we MAKE CHOICES! To put it all together involves all the complexities of V1-V3, but so far it has been hard to organize and visualize all this data and get to insight sufficient to make decisions.

The energy industry like many others faces the triple threats of rapidly changing technology, highly volatile, competitive business conditions, and the systemic loss of talent and expertise when it can least afford it. It needs new solutions that help bridge the gap and enable energy choice consumers to make informed, prudent decisions.

Augmented reality technology helps us quickly assemble, organize, analyze and choose between options recommended by algorithms, automatically comparing, updating with new data and experiences, evaluating probabilities and presenting it visually so it looks easy. The result is good stuff happens, bad stuff is avoided and both are made actionable by solutions that put all these moving parts together to enable us to just DO IT!

Our world is full of disruptive technology that is replacing old ways of doing business with new tools and capabilities we could scarcely imagine only yesterday. This continuous process of change empowers us. Augmented reality and other disruptive tech solutions harvest the data, knowledge and our experiences and apply it so we can visualize it all together and quickly analyze our options and trade-offs.


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