Identifying Decisions That You Can Automate

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Automated decisions are making the news. This year, Tesla cars in autopilot mode have experienced crashes in Florida, Montana and China. (The company contends drivers that were not using the autopilot mode properly). Meanwhile on another planet, the Mars Rover can now make its own decisions about what rocks to investigate. Keep your eyes open, because I believe these stories will become more prevalent quickly.

When analytics drives automated decisions, people are faced with another set of decisions about how that capability fits into the work, culture and mission of the organization. It’s akin to when you hire a new employee. What role does the new capability fill? How much autonomy is granted to the new capability? What review and verification processes are in place to ensure safe, productive and profitable work?

As stated in an earlier blog, it’s unlikely that automation will replace entire jobs. New job descriptions will be written, and existing ones likely rewritten, to specify how humans and automation will interact to fulfill a needed role.

The analytics entering organizational roles for the foreseeable future will be focused on specific tasks, if not built for specific purposes. Finance and investment companies are using analytics extensively for trading and portfolio composition, but those same analytics aren’t likely to make employee benefits decisions without modification.

Because they are purpose built, analytics need to specialize in predictable decisions that they perform repeatedly. This is exactly the sort of dull work that’s best left to automation, since people have a tendency to get tired, bored and distracted doing repetitive work.

At least initially, organizations will want to assign to analytics decisions that carry known and usually low-level consequences. Consequences can be measured by the amount of money at stake, the number of employees or customers affected, or the ease with which an automated decision can be reversed if need be. Analytics can help accurately define the type, scope and severity of consequences associated with decisions.

The metrics of predictability and consequence come together nicely in a video from The Harvard Business Review describing how to decide what decisions you can entrust to automation.

What about frequency of decisions? Some decisions, like short-term financial trading, happen so rapidly that humans can’t make every single call. In these situations, humans move from doing the work to maintaining, tuning and improving the automated systems that do the work. Other decisions, such as whether to acquire another company, happen so infrequently that automating the decision probably isn’t worth the effort. Between these two points lies a spectrum of decision frequency that organizations must also weigh in identifying decisions to automate.

The framework of predictability, consequence and frequency gives organizations the model they need to determine what role automated decisions will play. What decisions would you like to automated in your organization, and how would you score them for predictability, consequence and frequency?

(Image courtesy of forplayday / 123RF Stock Photo)

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Mobile Apps for Internet of Things Data Acquisition

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The Internet of Things in many ways is a catchall phrase that is used to describe everything from types of devices, to communications gateways, to new service-oriented business models. IoT devices generally are capable of sensing and communicating. IoT devices in the consumer sector include thermostats, door locks, garage door openers, etc. In the industrial sector there are many sensors used in manufacturing processes, vehicles, heavy equipment, and so on. Sensing and communicating data has traditionally been referred to as data acquisitions – a common IoT use case. What is often overlooked is the use of smartphones and tablets for data acquisition. These devices include several sensors such as for audio, video and motion.

The following story highlights how the mobile devices that we use every day are becoming integral to the IoT ecosystem.

Recently I was at a cafe with a friend. A former coworker of my friend whose name is Craig walked in, so my friend invited him to join us. My friend asked Craig “where you currently working?” Craig answered “I am working as an independent contractor developing a unique mobile app.”

With the Apple and Google app stores full of apps, and in many cases offering multiple apps that essentially do the same thing, I wondered what app could be new and unique. I quickly found out as Craig described how the app would help mining companies improve how they determine where mineral deposits most likely exist. Easier identification of mineral deposits will accelerate, optimize and lower the cost of mining – a definite game changer.

Determining places to explore and excavate is a combination of manual labor and trial and error. Miners typically pick and scrape away at surfaces in a mine to collect sample material to be examined to determine if the sample contains mineral deposits. If mineral deposits are detected then further exploration at that area would be initiated.

Craig then explained how the app works. Each mineral has a unique signature that can be identified by a spectrometer (from how the mineral reflects light). Photos and videos taken with smartphones and tablets use compression so the signature cannot be detected using standard photo and video apps. The app he developed interfaces directly to the video sensor so it can analyze the reflected light with the needed fidelity to recognize spectral signatures that identify specific areas where desired mineral deposits can likely be found. The locations identified are marked and uploaded to an operations center for further review and for planning.

Learning about this app shows how ingenuity and software running on commercial off-the-shelf smartphones and tablets makes them applicable for data acquisition use cases. More use cases that integrate people and mobile apps into IoT use cases will surely ensue.

So the next time you pick up a smartphone or tablet think of the myriad of uses it can be programmed to perform, especially when connected to other devices, systems and people. If you know of clever uses of mobile apps for IoT use cases, please comment.

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Augmented Reality For The Enterprise: A Use Case In Electrical Substation Field Service

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Augmented reality can make a real impact in field service workers in almost any industry. For a specific example, let’s look at a field technician visiting a problematic transformer at a substation.

Currently field technicians download test plans and view them on a laptop or iPad. They have to remain near their car as they conduct spatial reasoning with regards to electrical circuits and their locations. They have to hold some of they learned and deduced in memory, meaning they are eating up more bandwidth of the part of their brain that holds memory, conducts planning and spatial reasoning or navigation. This forces them to continually reference back and forth between the computer screen, supporting documents, tools and the work site. Many of the artifacts they will use are situated at angles and distances where they must turn away and effectively interrupt the mental process of fixing their eyes on the object they plan to use to do their work so that they can do the reasoning. They are basically switching between numerous cognitive tasks.

Below is a screen grab from a video of one technician conducting such procedures.

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The problem

Field technicians show up to substations and other work sites without knowing how to navigate the site. They also need to find the proper tools and documentation for completing maintenance, tests and repairs to assets.

They continually circulate their eyes between consulting site plans, asking site staff where assets are located, monitoring asset performance and completing their task checklist. Their hands are likely juggling multiple items that they need to set down in particular locations and keep track of in order to do the job.

Solution

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With augmented reality, field service workers don glasses at the job site. The glasses give them an internal map of the site they are at. Locations of tools and supplies are flagged or highlighted. Digital documents needed to complete a task appear in their field of vision. They can see any chart for an asset by shifting their attention to the asset. Field workers won’t need to remember anything or fumble with tools and documentation simultaneously. They can just focus on performing the work.

Here’s an example of how a substation needing maintenance might appear with augmented reality.

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  1. The menu helps decrease the total space filled by UI elements. Only the current UI element is up. Others appear wilted until workers turn gaze at it or access it with their hand or voice.
  2. This check list helps the user complete their work.
  3. The radar helps them navigate the station. The orange square shows the location of the nitrogen they will need.
  4. A capacity indicator of the transformer cylinder is off in the distance. Relative size is used to indicate importance. It isn’t up close because it isn’t exigent and spatial relationship is the primary representation in use.
  5. The chart shows year-over-year performance for the transformer. The orange year shows the current performance which fluctuates outside the range it should be in.
  6. The problematic area of the transformer is highlighted to draw attention to the user

This is not science fiction. It is reality. All the technologies and features listed in this use case are completely feasible with many of the headsets coming on market in 2016-2017. One example I love is the Meta headset which has gesture tracking among other awesome features.

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Analytics And Transporting Crowds Of Olympics Fans

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With the European Football Championships having just come to a close and the Olympics due to start, the Summer of 2016 will have seen two major events that only happen once every four years on the sporting calendar. These are in addition to the regular annual sporting events such as Wimbledon, the British Grand Prix and the Rugby League Challenge Cup Final. With events such as these, a lot of people travel whether it be locally or internationally. Such spikes in travel can have implications on the travel networks and cause problems with people getting around.

Despite the fact that the football championship was in France and the Olympics Brazil, back at home in the UK it is likely that a huge number of people will be watching these events live whether that be in a pub, a sporting establishment such as a club or at home. A huge number of people would have traveled to Wimbledon and also to Silverstone as well as those who made a trip to France and the more adventurous who might descend on Brazil.

Of course in the modern day world where we are able to watch all of our TV on demand it doesn’t really matter whether we miss one of our favorite programs. In the case of live sport however, it is extremely difficult to keep away from social media, news alerts and radio during a live game. So it is likely that a lot of people will watch sport live to stop the end result being spoiled for them.

Take the Olympics for example. Not only will a lot of people travel to Brazil from all over the world, they then need to travel inside the country to see various events. Local Brazilians also need to travel around the country to see the various events plus conduct their usual business. This will cause an increase in people traveling around the country over the period that the Olympics is taking place.

How can analytics help in these cases?

Using data to predict spikes in demand for transportation could be paramount to the success of a large sporting event such as the Olympics. For example, how many tickets have been sold for an event in one of the satellite locations in Brazil could indicate a lot of people traveling from Rio at the same time. Using IoT and data analytics could mean looking forward to one of these events to predict who might be traveling and what effects this could have. By enriching the data further with the city or postal code of ticket purchaser could tell planners where people are traveling from.

Of course it is difficult to predict as a lot of the locations are new and Brazil hasn’t hosted the Olympics before, but by pulling together data from previous transport networks and large events, planners might be able to predict where blockages or problems might occur. Predicting potential problems offers the opportunity of preventing problems from occurring in the first place.

The main aim would be to look at passenger info for the main transport hubs and see where the potential problems might occur normally, then predict what could happen when these places are busier due to huge numbers of people. Brazil wants to make a good impression during the Olympics for people who are visiting but also for people from the country to be proud that it did a good job. By predicting how the transport networks could be affected it will mean that the travelers will be happy and safe whilst visiting the country but also the networks will remain reliable and thus the country will see overall economic benefits from hosting a large sporting event.

(Image courtesy paha_l / 123RF Stock Photo )

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