Specifically Defining Big Data in Manufacturing

In the article What Is Big Data Analytics in Manufacturing?, the author defines Big Data in a manufacturing sense and also examines how big data has evolved in a manufacturing environment.  Interestingly, some of us think of Big Data as exactly as it sounds, a very large amount of data, say a petabyte of data as collected from sensors on an engine.  Big Data Analytics, in this mind frame, would then be the analyzation of this data using mathematical and statistical techniques.  But the author makes a key point – running reports on large data sets does not qualify as Big Data analytics in manufacturing.  If what I’ve just explained does not qualify as Big Data Analytics, then what does?

The article defines Big Data as follows, and I quote,

“Big Data Analytics in manufacturing is about using a common data model to combine structured business system data like inventory transactions and financial transactions with structured operational system data like alarms, process parameters, and quality events, with unstructured internal and external data like customer, supplier, Web, and machine data to uncover new insights through advanced analytical tools.”  This definition is certainly covers all the bases one could think of when it comes to understanding Big Data Analytics in manufacturing.

The transition of older technologies to a Big Data platform is happening right now.  One previous technology for collecting manufacturing data that is currently transforming to Big Data is enterprise manufacturing intelligence (EMI).  The author notes that two of the three ways this transition can happen for EMI is the ability to use structured and unstructured data as well as new analytical tools such as image, video, and geospatial data.

As big data usage in manufacturing continues to mature, it will become part of the IIoT Platform for delivering both legacy applications and Next-Gen systems.  Data will eventually be able to be taken from anywhere and delivered to anywhere while is usability will be simplified so that floor personal can use it.  In a connected, smart manufacturing environment, there is the possibility that any data collected can become useful to the process, personal, and ultimately, the bottom line.

 

Are most readers familiar with older technologies like EMI?

What do you think of the definition of Big Data as presented by the author?

Has anyone seen the industrial IoTs at work and if so, does this article portray a realistic picture of how manufacturing is changing?

http://blog.lnsresearch.com/what-is-big-data-analytics-in-manufacturing

Big Data is Not The Magic Bullet for Smart Manufacturing Improvement

In the article My New Year Wish – Less Hype for Big Data Analytics, More Buzz for Smart Manufacturing, the author examines how smart manufacturing is utilizing big data, but just not to the extent we all thought.  In fact, vast amounts of data are being collected in many new manufacturing processes, but very little of it actually gets used.  The value in new smart manufacturing processes isn’t all the data, it’s the connectivity between systems.  That is, no value is found by trying to mine a stream of sensor data emanating from machines in the plant in the hope of finding some pearl of wisdom.  The real value is streamlining business processes from desktops to machines, across department walls, across tiers of manufacturing operations management, and across tiers of suppliers.

Interestingly, the author cites an article that states that 70% of the data collected during manufacturing processes goes unused.  If this big data was so important, why is so much of being discarded?  One has to believe if there was usage to be found, it would be found by experts in these processes.  Instead of mindless and useless streams of data, emphasis needs to be placed on manufacturing process improvement enabled by integration standards that connect machines, processes, and systems.  Of course some data collection and analyzation is part of this improvement, but gigantic amounts of data are not necessary.

One has to wonder if Big Data is more a buzzword then a useful concept.  There is no denying that manufacturing processes can become more efficient through a more thorough understanding of the process via data collection, but perhaps we’ve overstated how much data we need.  Properly prioritizing the importance of big data usage within innovation is key, and we need to stop looking at the technology itself as the innovation.

 

Do you think the author has a minority point when it comes to Big Data?

Will there come a time in the near future where we actually begin to collect less data, or will the reasoning of better safe than sorry prevail?

Do you think there is actually any use to all the data that gets discarded?

http://www.manufacturing-operations-management.com/manufacturing/2015/12/less-hype-for-big-data-analytics-more-buzz-for-smart-manufacturing.html

The IoT will Give us the Big Data of The Future

In the article Big Data Analytics: The Force Behind the Next Internet of Things Wave, the author delves into how the cutting edge of the IoT is giving us the Big Data that will shape our future.  With more devises in more place, more data is being collected and thus more data must be analyzed.  The true value in this Big Data is our ability to make sense of it consequently create value.  The author examines a number of case where cutting edge sensors are allowing the IoTs to collect data that drives increased performance, predictive ability, and cost savings.

One of the first places the IoTs really took off was in our own homes.  Smart devices collecting data from meters, data on demographics, and data on energy consumption have helped customers save energy and money.  One smart meter company actually saved its customers over $500 million in energy spending.  With data analytics, forward-thinking energy management companies are able to run analyses on consumer thermostat data to better understand energy usage patterns.

Another case presented is that of an energy company using sensors in its oil and gas wells. The company collected data about the average production of oil, gas, and water from each of its wells.  It combined it with historical well performance and geospatial data to look at efficiencies and deficiencies based on location and equipment.  Based on the combination of this data and the sharing of this data with its operations, the company experienced 126 million per year in incremental revenue.

 

What are everyones thoughts on when we will begin to be fully connected, that is, when almost every device and thing we use is connected to each other?

Do you think we are collecting too much data?

As a customer, at what point do you believe collecting your data is an infringement on your privacy?  Is there any real way to track such invasions of privacy?

http://www.datameer.com/company/datameer-blog/big-data-analytics-the-force-behind-the-next-internet-of-things-wave/

A Structured Usage of Big Data Adds Big Value

In the article Overcoming Challenges To Make Big Data Profitable, the author goes over the immensity of the data collected every day and begins to break down how to analyze it and how to monetize it.  Big data analytics, though exciting, will become most useful when it adds monetary value to a business or individual.

Big data comes from a Machine 2 Machine (M2M), Internet of Things (IoT), Mobile, Cloud, Data storage and networks.  Because the amount of Big Data is so vast, the author has broken down the analysis of such into 5 V’s: value, velocity & veracity, volume, and variety.  Variety encompasses structured and unstructured data, volume is the actual memory amount of data, velocity is the speed of processing while veracity is the uncertainty vs the reliability of the data, and finally value is how the data becomes profitable.  Once can see that big data analytics, and its uses to create ecosystems, is becoming more complex every moment of everyday.

The key is to make the data manageable and able to be monetized.  The three silos that need to be connected to be able to monetize big data and thus create the ecosystems of the future are business, technology and regulatory.  On the business side, the big data analytics need to constantly be utilized to make sure it’s working and who it’s working for.  This utilization leads to accurate forecasting.  The technology side involves a competent and cutting edge workforce that is able to work in a multi-system, yet simple, infrastructure.  The technology at work creates the ecosystem for the future.  Lastly, regulatory requirements are constantly changing and the use, storage, and collection of certain types of data needs to be monitored.  As a business, working within the means of the law is a must.

In conclusion, big data and the analytics of it present big challenges.  Overcoming these challenges means analyzing the data correctly and efficiently while bridging the gaps between the three silos of business, technology, and regulatory.  With a plan in place, big data can lead to big money.

 

 

Have we already started collecting too much Big Data?

What new laws could come about as more and more data is collected?  Is the collection of some customer’s personal data with regard to habits legal?  Ethical?

Will there come a point where businesses rely too much on Big Data, thus negating the human elements of business?

http://www.excelacom.com/resources/blog/overcoming-challenges-to-make-big-data-profitable

One Smart Ecosystem: Using the IoT and Big Data Analytics

 

One Smart Ecosystem: Using the IoT and Big Data Analytics

In the article The Shape of Things to Come: IoT and Big Data, the author examines how smart ecosystems are being created and the prospect of IoT intercompatiblity issues.  As the IoT becomes more popular and mainstream, it is being touted as a life-changing interactive environment.  The connection of everything we do, and the collection of data from devices as we do it, is supposed to simply our lives.  Furthermore, this collection of data could even begin to predict our next moves and save us time in the process.  These conclusions based on the aggregate of data is the essence of Big Data.

The issues begin to arise when one philosophically thinks of Big Data.  Big Data, and the analyzation of this data, is only working in the past, or at best, the immediate present.  Additionally, using Big Data to predict the future relies on the heavy assumption that the future will mirror the past.  One cannot help but wonder, is this assumption valid?  No matter how one views the scope of utility of Big Data Analytics, companies like Netflix have already began to use it.  The issue of quality of data, based on amount of sensors and what the sensors are actually recording, further complicates the usage of Big Data.

Finally, the author brings up a very interesting point.  The creation of smart ecosystems based on the IoT and the Big Data collected has immense potential but only if there is only one compatible system.  What if there were multiple IoT that were not compatible with each other?  The data collected would be useless across systems thus creating no value whatsoever.  Thankfully, there is already movements in the technology industry to create unity of the IoT.  As the author quoted Sparks founder in the article, “Today, there aren’t enough things on the market to worry about intercompatibility, but there will be five years from now.”

 

Even with companies beginning to attempt to unify the IoTs, do you believe intercompatiblity will still be an issue?

How good will the predictive ability of Big Data get?

Supposing the IoT and the Big Data collected becomes so good at predicting our needs, what happens to our basic instincts?

 

http://unfiltered.groupsjr.com/the-shape-of-things-to-come-iot-and-big-data/

Potential Dangers to Mexico from the TPP

With Mexico being a member of the TPP and a Spanish version of the TPP now being available to the public, significant research has been done into what the implications will be. The article “The Dangers of the TPP to Mexican Legislation Regarding Intellectual Property” reviews some of these negative effects. The article starts off by noting that the TPP agreement will “promote negative changes on copyrights, access to culture or intermediary liability”. This forces local legislation to comply with TPP dispositions, which are projected to have a big impact on rights. Per the article, it will have especially detrimental implications with respect to the “matter of intellectual property” by “promoting a scheme based on restrictions and sanctions out of proportion”. Some of the topics that are specifically address in the TPP are frames to protect copyrights, technological protection measures, the Mexican Federal Penal Code, stronger sanctions against infractions to Copyright, and a ban on the use, production, modification, or selling of satellite signals. According to the article, it is “possible to detect that the TPP represents an imminent risk to public life” as it not only effects the ability to access culture on the internet, but “its administrative and penal sanctions may provoke an inhibitor effect that damages freedom of speech”. While the actual implications won’t be known until more time has passed, it is noteworthy to monitor these accusations and their potential harmful effects on Mexico.

New Zealand and the Meat Industry

The article “TPP to Deliver Removal of Tariffs” is an article specific to New Zealand, the meat industry, and the effects TPP has on both. As stated in the article, “in 2014, New Zealand exports of beef, sheep meat, and co-products to TPP countries total $2.4 billion USD. This equated to over one third of New Zealand’s total exports worldwide”. Tariff costs for exports to the TPP countries totaled $94.3 million. It is estimated that through the TPP, an” estimated $72 million in tariff costs” will be saved once fully implemented and operational. Specific to New Zealand, there are three main highlights. The first is the “removal of all tariffs on sheep meat in TPP countries within eight years or less when the agreement enters the force”. The second main requisite is the “removal of all tariffs on beef in TPP countries, except Japan, within 11 years or less from when the agreement enters into force”. Last is the “reduction of Japanese tariffs on beef from 38.5% to 9% over 16 years”. These changes obtained through the TPP will “secure market access and the red meat sector’s competitiveness not only into North Asia but will further integrate New Zealand into the Asia-Pacific regional supply chains”. Are there any potential side effects? Are any other countries in the TPP hurt by the adoption of the TPP?