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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The Benefits Of Automating Your Job With Analytics

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Automation and robotics are becoming increasingly capable of taking over at least aspects of many jobs. Some people are concerned that their entire job will replaced. However, there is good news if you’re among those concerned about losing their jobs to robot overlords – researchers at McKinsey found that only roughly five percent of current US jobs could be completely automated using existing technology.

If automation and robotics won’t replace your job, they will certainly change significant parts of your job, mostly for the better.

The McKinsey researchers examined the benefits to automating parts of a job by surveying 2,000 tasks commonly found in American jobs. They estimate that in 60 percent of current US jobs, 30 percent or more of the work activities could be automated using existing technology. That equals 1.5 days in your work week. Not only would this work be taken off your workload, it’s likely the work would get done more accurately or to a higher level of quality.

Unfortunately, this likely won’t be mean shorter work weeks anytime soon. But, it does mean that you can spend more time doing tasks that people do well, such as setting strategy, creating content, supervising and mentoring.

Take the example of an average marketing manager. Marketing managers are often highly skilled and highly compensated workers. The organization needs them doing the work for which they are best or uniquely suited. Roughly 10-15 percent of tasks in such a job could be automated, such as reviewing pricing decisions, estimating material costs for product production, and surveying industry literature for trends and competitive information. Automating these tasks could free up roughly half a day per week for a marketing manager.

An extra half-day per week equals more than 20 work days per year. That time could go to new product research, increased sales training, additional product promotions, and other activities that directly impact the bottom line, provide job satisfaction and benefit from a human touch.

Analytics provides the brains behind automating job tasks. First, analytics build lists of tasks through anomaly detection, failure analysis, root cause analysis and other techniques. Tasks might include maintenance work, repairs, corrective steps, scheduled activities, etc. Next, these tasks are then scored according to complexity, criticality, urgency, cost impact, risk of danger, likelihood of human error, and other characteristics.

Depending on these scores, tasks might be done automatically, done pending review of a human, initiated only after gaining permission from a human, or left to humans to do. The tasks needing human permission, review or labor are presented in a prioritized list to facilitate decision making and resource allocation.

By sorting work in this way, and then carrying out some of the work, analytics become a powerful assistant to workers striving to serve customers better while optimizing organizational performance.

 

(Image courtesy of nd3000 / 123RF Stock Photo)

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Using Analytics To Improve Workplace Safety: 3 Steps

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You likely have a bunch of different spreadsheets, reports, and databases related to safety scattered across your organization. These data sources are compiled by various people in different departments. For example, HR may have information about driver certifications, while customer support keeps records of complaints about drivers and fleet operations holds maintenance records. You might not even know all the sources of possible data pertaining to accidents and injuries, of them, even if your job title contains the word “safety”.

Your organization’s safety record is probably pretty respectable. After all, accidents on the job are decreasing, as described in this news release from the Department of Labor. Check out this chart from the news release:

Safety trend chart
Occupational injuries and illnesses have been declining for more than 10 years

Still, there is no room for complacency. The National Safety Council cites nearly $200,000 in direct and indirect costs associated with a workplace injury that results in a doctor or hospital visit.

How do you apply those disparate and scattered sources of data to reducing the risk of accident and injury in your organization? There are three simple steps to using your data to improve workplace safety.

The first step is bringing together your using existing safety-related data using visual analytics. Just seeing your spreadsheets and databases correlated into intuitive visualizations that everyone can share delivers significant gains in safety. Creating a picture of your data clarifies relationship between data, puts data into a familiar context, and identifies potential problems in data quality.

For instance, the historical safety data for a trucking company may show which drivers were involved in accidents in the last five years. You could find that information easily by sorting a table of data. Placing that same tabular data on a map and a timeline tells you much more about exactly where and when those accidents occurred.

Safety table map
Visualizing data correlations is more effective than hunting for correlations in a table of data

The next step is moving from viewing historical data to responding to today’s events by applying diagnostic analytics to alert users about current unsafe conditions. In the trucking example, adding alerts to a visualization of historical accident data can tell dispatchers when a driver is entering an area of frequent accidents. With that information, the dispatcher might caution the driver to reduce speed and drive carefully.

The third step in using data to improve workplace safety moves beyond the past and present to look into the future. Predictive and prescriptive analytics, based on your historical and real-time data, calculate the likelihood of future hazardous events and locations. The power to predict is the power to avoid or mitigate consequences.

Keeping with our trucking company scenario, predictive and prescriptive analytics may take the form of a prioritize list of tasks to perform to reduce risk and avoid accidents. For example, the dispatcher may change a driver’s rout to avoid areas with frequent accidents.

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Analysis of risks leads to recommended actions for lowering risk

The journey from scattered spreadsheets to ubiquitous prescriptive analytics requires dedication, funding, and a vision of a safer workplace. Traveling the full journey may not make sense for every organization. Even so, stepping beyond simple spreadsheets and databases offers value for your employees and the organization as a whole.

 

 

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Could ‘Pokémon Go’ Inspire Enterprise Productivity?

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Renewable

The world is going crazy for Pokémon Go. Nintendo’s stock value is making huge gains while masses of people are out hunting little ‘holographic’ critters. The technology isn’t new. Yelp has had a similar functionality out for 4 years called monocle. Yet this ‘Pokemon Go’ has made a ginormous splash. How? They used a technology to solve a pain point which is evidenced by their profitability so I don’t want validate that product vision here. Instead I’d like to answer another question which is “How can Augmented Reality help Enterprise on the same scale as the B2C market?”

The first part of the answer is that the motive to introduce AR to enterprise should not be about making a lot of money but rather helping a lot of brilliant people attain more.  Guy Kawasaki says, “The genesis of great companies is answering simple questions that change the world, not the desire to become rich.” The same applies within an organization eyeing new product offerings.

The second part of the answer is more involved so first I’ll discuss proof that Augmented Reality indeed helps brilliant people dive into a use case and then highlight some good User Experience design principles stemming from neuroscience that will make the use case come to life.

Augmented Reality (AR) is a technology that registers digital aspects onto the physical world around us. A stop light could be considered primitive Augmented Reality (AR). AR on Mobile Devices isn’t new but neither was the tablet before Apple made mountains of cash by designing it the right way. After Google Glass, there was a healthy dose of skepticism that everyday people would enjoy using Augmented Reality. Many would assume that if people don’t want it on their glasses with their hands free then why would they want to use a hand to hold the same information that they didn’t find useful?

Even as AR is in its technological infancy, Market guru Greg Babb explains how Augmented Reality reduced errors and time to complete task for wing assembly study presented by Paul Davies in conjunction with Iowa State and Boeing.

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The chart above shows that wing assembly took significantly less time than traditional methods of assembly. There tends to be a major drive in Enterprise AR use cases to build applications for field crews and those assembling things. Meta CEO Meron Gribetz is on the record of saying he expects to throw away all the monitors in his office by next year and just work with AR headsets. This is compelling because Meta consists of developers, designers and scientists. That means knowledge workers would be using AR at their desk. This is either crazy or prophetic.

So what would an enterprise application look like? Well lets look at it through the lens of situational intelligence. Let’s consider the following use case:

Wind Turbine Use Case

A scientist at an energy company needs to run some prescriptive analytics for wind turbines. Government compliance and regulations have just been reformed and costly repairs and updates to the machinery has to be implemented on an accelerated pace. Heavy fines will be instituted for safety violations. Our scientist wants to use Matlab to run simulations on existing wind turbines to predict which turbines have a greater risk of breaking down, overheating or malfunctioning. He is think she can get more longevity out of gearboxes for yearly use within certain confidence levels.

  • He first speaks out loud saying,”Show me wind farms in central California.”
  • He sees a 3D map of several wind farms that he spins with his hands
  • The length of time he gazes at a certain region makes it slowly expand
  • He sees temperature on 4 wind farms with a high failure prediction
  • He increases severity of wind velocity changes in his data set and sees gearbox 4 fail
  • His gaze on the fourth gearbox chart line causes a line to appear animating towards the holographic turbine
  • He picks up the turbine, swipes the outer shell away and sees the motor spinning
  • He dictates notes about the turbine and recommends replacing the gearbox sooner than others

Outcome

Our scientist is able to work faster in a more implicit manner without opening various files and programs with a mouse. He has more visual real estate and can now work with depth rather than just up and down dimensions. He is more productive, which makes his company more money.

Neural Interface Design

Pictorial Cues are an important aspect of good user experience that make a scene more realistic in AR. Some of these can be:

  • Occlusion – One object blocking part of another
  • Relative Size – Equal sized objects taking less area within our field of vision when their distances from us vary
  • Shadows – Objects that casts shadows seem more real
  • Accretion – Aspects of an object appear as a user moves in the physical world

Another aspect to design for in AR is binocular disparity as it assists with depth perception. Studies show that the neurons in our visual cortex fire optimally when there is a amount of specific disparity with a stimulus. So that is why you see one wind turbine moved to the right of another in the wireframe.

So maybe you won’t get to go hunting Pokémon at work if your company is smart enough to outfit you with one but you will definitely feel like a Jedi when manipulating the world around you with your senses. You will love sharing this world with your co-workers. Augmented Reality unleashes you imagination. The next couple years show some exciting times ahead for Reality Computing!

Special thanks to JulianHzg for making a great Wind Turbine in Blender at  that I recolored and used in my wireframe.

 

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Use Visual Analytics to Get Started with the IIoT

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Industrial IoT (IIoT) applications bring about many opportunities to increase operational efficiency by presenting personnel with timely insights into their operations. Visualizing IIoT data using visual analytics is a proven way to facilitate insight-driven decisions. So at the very least your IIoT initiative will start off by integrating IIoT connectivity, visual analytics and other system components. To best ensure early and ongoing success it is recommended that you follow the best practice of starting small, attaining quick wins and then increasing scope and/or scale.

The first step is to connect devices and systems and use visual analytics to create a simple visualization of your IIoT data. If the IIoT devices are mobile or geographically separated, then an appropriate visualization would be to display the location of the devices on a map such as shown above. This is an effective way to verify connections and validate successful integration.

The second step is to collect and intuitively visualize your IIoT data. At this point you can identify issues to make operational efficiency improvements.  As an example, a freight trucking business can see a map with the locations and times of where their trucks are moving at slower than expected speeds. This information is used to change the routes on the fly to maximize on-time deliveries. As this example highlights, connecting to IIoT data streams and visualizing the data facilitates operational efficiency improvements.

The third step is to correlate data from different systems and data sources, including time series data from devices at different locations. Visualizing data correlated by time and location makes it possible to create comprehensive big picture views that reveal details about what happened and is happening, where, when, why and how. Using the trucking example, areas where driving speeds are consistently slower than expected are highlight by the red lines on the map above. This information is used to refine future routes, schedules and delivery commitments.

The fourth step is to apply advanced analytics to the IIoT data to generate insights for inclusion into the visualizations. Returning to the trucking example, advanced analytics will recommend the optimal average truck speed to minimize fuel costs based on the weight of the load they are carrying. Visualizing each truck using color coding to highlight the biggest offenders makes the analytics results actionable at-a-glance so that operations managers and drivers can improve driving efficiency. In the image above it is easy to see the truck icons colored yellow and red that represent the trucks that are traveling outside of the optimal speed range.

Having completed these steps you are positioned to leverage your IIoT infrastructure and expand on your competency by combining visual analytics, data correlation and advanced analytics in innovative ways to address business problems and facilitate operational efficiencies that would not otherwise be possible. Future blog posts will cover such combinations and the corresponding operational efficiencies.

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Industrial Internet Of Things: End Up On The Winning Side

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Operations and technology executives take notice when experts such as McKinsey project $11.1 trillion in economic value by 2025 as a result of linking the physical and digital worlds.  That’s a tremendous amount of economic value in a very short time, even if the experts might be a little off in their estimates.

The impact of the internet over the last 25 years certainly supports predictions of disruption and promise as the Internet of things (IoT) and Industrial IoT (IIoT) continue to connect the physical and the digital.  Organizations that transform themselves using IIoT can become giants; those who lag or fail in their execution may become mere memories. How do you ensure you and your organization land on the right side of this disruption?

Operational data is not a new phenomenon

Mentions of IIoT pepper nearly every operations- or technology-related conference these days.  Many traditional control system vendors are relabeling their offerings as part of the IIoT movement.  While industrial control systems remain critical components of operations in many industries, simply rebranding existing systems is certainly not going to going to deliver the trillions in economic value that McKinsey and others predict.  That magnitude of value creation comes only from truly transformative changes to how companies and industries operate.

Inherent risks in embarking on transformative change

Any large organization that can greatly benefit from the promise of an IIoT world has a number of existing critical assets, control systems, IT systems, processes, and skilled people that are essential to their operation.  Many industries have equipment and systems that have been acquired over several decades. Displacing all of these existing operational assets with a sparkly new, end-to-end IIoT-enabled operation is risky and typically not economically practical. Mergers, acquisitions, large IT projects and other attempts at transformative change fail at an astonishing rate. Estimated failure rates range from 30% according to the optimists to 70% from the pessimists.

If you are trying to create transformative change while relying on existing systems, processes systems, and people, you inevitably will face execution risks related to:

  • Lack of interoperability and openness of existing control and IT systems
  • Poor data quality in dependent systems
  • Lack of scalability, both technically and economically, of these systems
  • Insufficient internal talent, expertise, and bandwidth to manage a large project that touches the operations and IT sides of the business
  • Security exposures as you open up systems that have traditionally been on a closed loop system
  • Poorly defined objectives and accountability
  • Striking the wrong balance between building versus buying IIoT systems, ending up with either a solution that isn’t fit for purpose or a solution that exceeds cost and timeline estimates and doesn’t scale.
  • Difficulty maintaining balance of schedule, cost, ROI and executive support

How to end up on the winning side of IIoT

The risks and complexity make getting started with an IIoT initiative seem daunting.  But with this sort of disruptive change, playing the laggard is not an option. How do improve your odds for success?  Here are a few recommendations:

Think big but start small – Think big about how your organization can use new data sources and analytics to improve their operations and service uptime, but start small by first tackling a discrete, well-defined problem. Deliver value quickly and then consider another problem to tackle or extending the first solution to solve other related problems.

Clarify the problem, solution and accountability – Ensure the problem, solution requirements and dependencies are clearly understood.  Appoint a clear, accountable owner for the project who has organizational support.

Prioritize vendors that have “skin in the game” – Many software, hardware and communications vendors will happily sell you the parts of an IIoT solution–platform access, software licenses, sensors, access points, gateways, network access, and servers–but leave you to sort how to assemble these parts into something that solves your problem and provides value. Prioritize vendors who provide ongoing service with lower up-front costs.  This enables you to ensure the service delivers on its promised value before you have committed too much funding.

Challenge traditional thinking in your organization – What got you here won’t get you there! Clearly for many industries existing levels of security, reliability and regulatory compliance must not be compromised. However, that shouldn’t mean that new approaches such as cloud computing, internet connectivity, open source software, and commoditized hardware should be dismissed.  These will be required in many cases to realize the potential value of IIoT solutions.  Many companies use these technology solutions successfully today while balancing the associated risks.

Get Started – Don’t get stuck in analysis paralysis – Obviously it is important to ensure a problem, the solution and potential value for solving it are well understood.  It is also critical to assess risks and get necessary organizational buy-in.  Once you have done that, get started, learn and improve.  The opportunity is immense and those who lead with successful IIoT solutions will have tremendous efficiency advantages in their respective industries.

Allan McNichol is the former CEO of GOFACTORY and Managing Director for Intelligent Energy

(Image courtesy zurijeta / 123RF Stock Photo )

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Are You Analytically Driven?

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“Ultimately, the analytically driven enterprise is one where analytics is seamlessly woven into the fabric of the entire business.”

This statement introduces the 2016 Dun & Bradstreet Enterprise Analytics Study. In the study, nearly three-quarters of respondents considered their organizations as “analytically driven.” Yet a majority of respondents:

  • Did not possess a corporate strategy for how analytics are used across the organization
  • Did not share analytics models and results outside of their departments
  • Did not consider their company skilled in visualization tools for analytics
  • Did not have the analytics resources in-house to identify new product or market opportunities

Clearly there’s a gap between how broadly people perceive analytics influencing their job and how sophisticated the role analytics actually plays in their job.

The majority of respondents in this study work in the manufacturing, financial services, and high tech industries. These industries are not unique in their slow adoption of analytics. Many industries are just starting on the analytics learning and maturity curves.

So, is your organization analytically driven? How would you answer these questions from the survey?

  • Does your organization have a strategy for using analytics across the enterprise, and not just within departments?
  • If your department generates and applies analytics models and results, are you sharing those with other departments?
  • What data visualization tools and skills do you use on a regular basis to communicate across your organization?
  • Can you point to new products or new markets that you identified and exploited using analytics?
  • What percentage of your organization’s analysis is performed by external researchers and consultants?

If you struggled to give specific, positive answers to these questions, know that you’re not alone. And, as they say, the first step is admitting you have a problem. With the amount of data and devices pouring into the world in 2016, you’ll quickly encounter problems if you don’t have an organizational analytics approach–especially if your competitors do.

This white paper contains an easy approach to evaluating your organization’s analytics maturity and models to consider for your organizational analytics strategy. It’s a good first step toward becoming truly analytics driven.

(Image: everythingpossible / 123RF Stock Photo )

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