Ethics and Decisions

Ethics green highlighter

In life, we are all faced with difficult decisions. Whether they are personal or professional decisions has no bearing on the level of complexity the human mind sorts through to come to a decision. And when we factor ethics into the equation, determining what the “right” thing to do can be a daunting task.

Fortunately, there are simple questions we can ask ourselves when trying to reach the right decision. In the business world, all of us need to read, understand and consider the ethics policy of the company that we work for. When faced with a business decision, the ramifications can be huge and affect a lot of people, including coworkers and the company’s customers. Here’s a simple test that we can perform to see if our proposed decision is ethically correct. Share the decision with our company’s president! If we are willing to do this, then we can rest assured that the decision is the right decision and follows the corporate ethics policy.


In our personal lives, we have to compare a decision we are about to make with our own personal code of ethics. What are we comfortable with? How will our decision affect other members of our family? Are the consequences of our decision worth sticking with it? Well, as you may have guessed, there’s a similar ethics test for this one, too. Share the decision with our mothers! If Mom agrees with our decision, we know intuitively that our decision is the right one.

There’s one more test we can perform to test whether a decision is ethical. This one is a blend between our personal code of ethics and the corporate ethics policy. We must ask ourselves, are we willing to talk about this decision during our next job interview? If the answer is yes, we are about to make the right decision.

If we are willing to take part in all three of these ethics tests before making tough decisions, we will have a high level of confidence that our decisions are ethical and the right thing to do.

By Julian Yeo, Product Manager at United Electric Controls



At one point or another, you may have come across the buzzphrase – Big Data. What is it, why does it matter and how can instrumentation users position themselves to ride this trending phenomenon?

Big data is a term used to describe the tremendous volume of structured and unstructured data that a business receives. The ‘Big Data Universe’ is growing exponentially. We are producing and consuming data at an unprecedented rate. Computer Sciences Corporation predicts that data production will be 44 times greater in 2020 than it was in 2009 with individuals creating 70% of all data and enterprises storing and managing 80% of it.

It is worthwhile to note that big data is more than just volume (e.g. petabytes or exabytes). In addition to volume, SAS Institute lists 4 other aspects (velocity, variety, variability and complexity) associated with big data. Here is a simple example of how big data is contextualized in an industrial plant.

  • Volume – Big data in a process plant originates at the sensor level. There is tremendous amount of process information being captured across the plant’s labyrinth of sensors.
  • Velocity – Data that is fed into the control system (e.g. SCADA) should stream in at near-real time speeds and dealt with in a timely manner. Any latency in transmissions could pose a danger.
  • Variety – Data can come in from various sources and formats such as electronic data recorders, PLCs, HMIs and data historians (Dan Hebert, Four ways to Collect Process Plant Data).
  • Variability – Data flow can be inconsistent with periodic peaks (e.g. Peaking power plants where there could be daily, seasonal or event-triggered peak data loads).
  • Complexity – With a complex myriad of different types of information generated in a plant, it is necessary to connect the data dots and establish information hierarchies for effective plant monitoring and control


Data is only as useful as what you do with it and what you can make sense of it. In the words of Thomas Davenport an analytics thought-leader, “The sweeping changes in big data technologies and management approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation”. Big data when processed, analyzed and interpreted correctly is significant because past business performance can be evaluated and future performance can be quantitatively predicted. This arms decision makers with descriptive, diagnostic, predictive and prescriptive information to shape the best corporate strategy.

Moving forward

As an instrumentation user, here are some ways (adapted from Littlefield, M (2015, November/December).Big data analytics. InTech, 12-15)  to prepare yourself as your organization jumps on the Big Data bandwagon.

  1. Stay open and up to date with technological changes. According to InTech’s magazine cover story on big data analytics, “as legacy and next generation application build on top of IIoT platforms, new applications will be held to a higher standard than ever before in the industrial space” (Littlefield, M (2015, November/December).Big data analytics. InTech, 12-15) . Big data vendors need to create not only navigation friendly applications (think consumer apps like Uber) but also ones that weave social, collaborative, search tools and intuitive analytics into its architecture. Imagine, monitoring the state of your power plant’s instrumentation on a mobile platform interface. To some, that concept might be revolutionary while to others, it might be a precarious thought to have something so important hinged on an interface that appears to be skeletal. This will require a paradigm shift.


  1. Always refer back to the corporate strategy to maximise the value of big data analytics. Are you measuring the right process variables at the right places at the right frequency? Can the measurement data you are currently collecting be repurposed to indicate another aspect of operational or maintenance status? Can you expand the capability of your instrumentation to obtain additional measurement points? Also, as more companies adopt big data analytics, you can expect more cross functional collaboration between various departments (e.g. EHS, quality, asset performance management) in your organization to bring more structure to corporate goals (Littlefield, M (2015, November/December).Big data analytics. InTech, 12-15). If you are in a position to influence, be the voice that keeps everyone on track and gather only information that matters.


  1. Skills upgrade. Companies that embark on big data programs will invest in training their employees with the hope of ‘home-growing’ data science teams. In the long run, this is more cost effective from an organizational standpoint instead of outsourcing data services. Actively participate in these trainings, volunteer if need be, or even consider investing in a big data analytics course that is relevant to your area of expertise. Knowledge is power.


As previously stated it is clear that big data when processed, analyzed and interpreted correctly is significant because past business performance can be evaluated and future performance can be quantitatively predicted.


By Julian Yeo, Product Manager at United Electric Controls