In today’s connected world, there are business terms that come and go and those that change form but still have the same meaning. So how do you go about keeping up with the ever-changing digital landscape terminology while knowing exactly what to do once you’ve deciphered it? It isn’t easy, is it? But don’t worry, because by reading this post, you’ve taken the first step in making sure your business can operate intelligently by actively seeking the newest information and trends about the most important topics in your industry. So let’s get started…
Four Industry Technical Terms and What They All Mean
Cognitive Computing, Industry 4.0, Predictive Maintenance, and Data Conditioning… these are some of the most common terms being used in tech today. Let’s go over them in detail so you can make educated decisions about how to use them as they relate to your particular business interests.
What is Cognitive Computing?
Have you ever heard of artificial intelligence? Well, cognitive
computing is directly related to artificial intelligence in that it
simulates human thought processes using a computerized model to identify
patterns. In other words, cognitive computing uses machine learning
algorithms to recognize and process compounded data over time. This
process is designed to provide the user with a series of related results
based on what the learned data determines to be the most likely
intended request. As closely as possible it mimics how the human brain
works, and in many cases even performs tasks faster and more accurately
than the human brain can. What does this mean for you? Well, going
forward, cognitive computing could be applied to other areas of your
business as well. For example, consumer behavior analysis, personal
shopping bots, customer service bots, travel agents, educational tutors,
security, and just about any type of diagnostics. If you think about
it, this is the next natural step as we already have digital personal
assistants on our mobile devices and computers.
What is Industry 4.0?
This term refers to the transformation in the way we currently produce
products due to the digitization of manufacturing. The number four
represents the fourth transition that has occurred in manufacturing over
time. The first was the industrial revolution, which used water and
steam as its main mechanism. The second was assembly lines and mass
production, which used electricity. The third which utilized computers
and automation. The fourth, also known as Industry 4.0, is an expansion
of the third revolution, which refers to how it will enhance computers
and automation with smart, self-thinking systems that use data and
machine learning.
What is Predictive Maintenance?
The purpose of predictive maintenance is to predict when a piece of
equipment might fail and to prevent it from failing by proactively
replacing it or performing the necessary maintenance to it. This helps
avoid unplanned reactive maintenance, while saving money on the cost of
performing too much preventive maintenance. The way predictive
maintenance works is by using condition-monitoring equipment to assess
any given asset’s performance in real-time. The main component in making
this process work is the Internet of Things (IoT). The IoT process
enables different assets and networks to communicate and work together
to share, analyze data, and other similar actions. This is important
because these systems depend on the predictive maintenance sensors to
gather information and analyze it to find areas of concern so it can
proactively perform the recommended maintenance before the equipment
actually encounters a problem.
What is Data Conditioning?
Data conditioning, also referred to as data cleaning or data cleansing,
is the detection and removal of errors of corrupt or inaccurate
information on a database. Once identified, the errant or erroneous data
is modified, replaced or deleted. Failure to perform proper data
conditioningcan lead to any number of problems, including the
compilation of false data results. However, data conditioning itself
doesn’t come without problems. Improper data conditioning can sometimes
result in losing vital information or other important data. Fortunately,
there are plenty of tools that can be used for data conditioning. Some
statistical programs have the data validation built in – actually
catching some pre-conceived data errors, such as non-valid variable
codes, automatically as they are entered.
The Bottom Line
Time marches on, and so does the evolution of technology. It looks like
artificial intelligence, machine learning, and the way we used to do
business is going to continue changing. That means staying current with
the ever-changing trends is no longer optional…it’s mandatory – if you
want your business to succeed and grow.
What’s Next?
If you would like to discuss business, software, engineering solutions, or need XML consulting services, please Contact Us today. JANA
is an experienced technical services company that specializes in
authoring, managing and publishing technical documentation. We would
love the opportunity to help you take your business to the next level.
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