Industry 4.0 and the sensor data analytics problem

That sensor data problem

A few weeks ago, I met with a number of IT consultants who had been hired to provide data science knowledge for an Industry 4.0 project at a large German industrial company. The day I saw them they looked frazzled and frustrated. At the beginning of our meeting they spoke about the source of their frustration: ‘Grabbing a bunch of sensor data’ from a turbine had turned out to be a pretty daunting task. It had looked so simple on the surface. But it wasn’t.

Industrial time series data

Data hungry Industry 4.0

In my last blog post, I looked at the Industry 4.0 movement. It’s an exciting and worthy cause but it requires a ton of data if executed well. Sensor data (aka industrial time-series data) from various assets and control systems is key. But acquiring this type of data, processing it in real-time, archiving and managing it for further analysis turns out to be extremely problematic if you use the wrong tools. So, what’s so difficult? Here are the common problems people encounter.

1. The asset jungle

When we look at a typical industrial environment such as a packaging line, a transmission network or a chemical plant, we will find a plethora of equipment from different manufacturers, assets of different ages (it’s not unusual for industrial equipment to operate for decades), control and automation systems from different vendors (E.g. Rockwell, Emerson, Siemens, etc.). To make things worse, there is also a multitude of different communication standards and protocols such as OPC DA, IEEE C37.118 & Modbus just to name a few. As a result, it’s not easy to communicate with industrial equipment. There is no single standard. Instead, you typically need to develop and operate a multitude of interfaces. Just ‘grabbing’ a bunch of sensor data suddenly turned difficult. There is no one-size fits all.
 Asset Jungle

2. Speedy data

Once you have started communicating with an asset, you will find that its data can be quite fast. It’s not unusual for an asset to send data in the milisecond or second range. Capturing and processing something this fast requires special technology. Also, we do want to capture data at this resolution as it could potentially provide critical insights. And how about analyzing and monitoring that data in real-time? This is often a requirement for Industry 4.0 scenarios.
high speed data

High speed data vs slow: what could you be missing?

3. Big data volumes

Not only is data super fast, it’s also big. Modern assets can easily send around 500 -10000 distinct signals or tags (e.g. bearing vibration, temperature, etc.). A modern wind turbine has 1000 plus important signals. A complex packaging machine  for the pharmaceutical industry captures 300-1000 signals.
The sheer volume creates a number of problems:
  • Storage: Think about the volume of data that is being generated in a day, week or month: 10k signals per second can easily grow to a significant amount of data. Storing this in a relational database can be very tricky and slow. You are looking at massive amounts of TB.
  • Context: Sensors usually have a signal/ tag name that can be quite confusing. The local engineer might know the context, but what about the data scientist? How would she know that tag AC03.Air_Flow is related to turbine A in Italy and not pump B in Denmark?
sensor structure

Signal/ tag names can be extremely confusing

4. Tricky time-series

Last but not least, managing and analyzing industrial time series data is not that easy. Performing time-based calculations such as averages require specific functions that are not readily available in common tools such as Hadoop, SQL Server and Excel.  To make things worse, units of measure are also tricky when it comes to industrial data. This can especially be a huge problems when you work across different regions (think about degree C vs F). You really have to make sure that you are comparing apples to apples.

5. Analytics ready data

An often overlooked problem is that sensor data is not necessarily clean. Data is usually sent at uneven points in time. There might be a sensor failure or a value just doesn’t change very often. As a result you always end up with unevenly spaced data which is really hard to manage in a relational database (just google the problem). Data scientists usually require equidistant data for their analytics projects. Getting the data in the right shape can be immensely time-consuming (think about interpolations etc.).
Uneven Time-Series data

Unevenly spaced sensor data

That tricky sensor data

To summarize this: ‘grabbing a bunch of sensor data’ is anything but easy. Industry 4.0 initiatives require a solid data foundation as discussed in my last post. Without it you run the risk of wasting a ton of time & resources. Also, chances are that the results will be disappointing. Imagine a data scientist attempting to train a predictive maintenance model with just a small set of noisy and incomplete data.
To do this properly, you need special tools such as the OSIsoft PI System. The PI System provides a unique real-time data infrastructure for all your Industry 4.0 projects. In my next post, I will describe how this works.
What are your experiences with industrial time-series data?

Industry 4.0 & Big Data

Industry 4.0

If you work in a manufacturing related industry, it’s difficult to escape the ideas and concepts of Industry 4.0. A brainchild of the German government, Industry 4.0 is a framework that is intended to revolutionize the manufacturing world. Similar to what the steam engine did for us earlier in the last century, smart usage of modern technology will allow manufacturers to significantly increase effectiveness.
While there is a general framework that describes what Industry 4.0 should be, I have noticed that most companies have developed their own definitions. As a matter of fact, most of my clients lump the terms Industry 4.0, Digitalization and IoT together. Also, the desired objectives have a wide range and include items such as:
  • Improve product quality
  • Lower cost
  • Reduce cycle time
  • Improve margins
  • Increase revenue

Industry 4.0 initiatives

Industry 4.0 initiatives

With a wide definition of Industry 4.0/ Digitalization comes an equally wide interpretation of what type of tactics and initiatives should be undertaken to achieve the desired outcomes. Based on my own experience, I see companies look at a variety of activities that include:

When you think about it, each one of these programs requires a ton of data. How else would you go about it? Consider the easiest example: energy management. Reducing the amount of money spent on energy throughout a large plant by gut-feel or experience is almost impossible. It is the smart use of data that allows you to identify energy usage patterns, and hot spots of consumption. Data must therefore be the foundation of every Industry 4.0 undertaking.

Big Data & Industry 4.0

What type of data does Industry 4.0 require? It depends. Typical scenarios could include relational data about industrial equipment (such as maintenance intervals, critical component descriptions etc.), geospatial (e.g. Equipment location, routes, etc.) and most importantly sensor data (e.g. Temperatures, pressure, flow-rates, vibration etc.).
geospatial information

Sensor data enriched with geospatial information

Sensors and automation systems are the heart of your Industry 4.0 program: they pump a vast amount of highly critical time series data through your various initiatives. Just like the vital signals from a human being allow a doctor to diagnose a disease, industrial time series data allows us to learn more about our operations and to diagnose problems with our assets & processes early on.
Screen Shot 2016-07-12 at 21.38.49

The value of industrial time series data

Assets such as turbines, reactors, tablet presses, pumps or trains are complex things. Each one of them has thousands of valves, screws, pipes etc.. Instead of relying on intuition, hard-earned experience and luck, we can collect data about their status through sensors. It’s not unusual for specific assets to produce upwards of 1000-5000 signals. Combine a number of assets for a specific production process and you end up with some really BIG DATA. This data, however, allows engineers and data scientists to monitor operations in real-time, to detect specific patterns, to learn new insights and to ultimately increase the effectiveness of their operations.

screen568x568

What’s next?

Industry 4.0/ Digitalization is an exciting opportunity for most companies. While many organizations have already done a bunch of stuff in the past, the hype around Industry 4.0 allows project teams to secure funds for value-add initiatives. It surely is an exciting time for that reason.
But is dealing with industrial time series data easy? Collecting, archiving and managing this type of data can be a huge problem if not done properly. In the next blog post, I will speak about the common challenges and ideas for making this easier.

The Big Data Challenge of Activity Trackers

The activity tracker revolution

Activity trackers such as the ubiquitous Fitbit, Jawbone and the Garmin Vivofit are extremely popular these days. You can frequently spot them on colleagues, friends and customers. Their popularity raises a question: Does the collected data add value to your personal life? As a data hungry endurance athlete who relies on various technologies such as heart rate monitors, accelerometers & powermeters to improve my training I could not resist finding the answer. For the past three months I have worn a Garmin Vivofit to collect and analyze data. Here are my experiences and a simple process for getting value out of your activity tracker.

The Data

What do activity trackers actually do? The devices count the number of steps that you take each day (they also estimate the distance you have covered). In addition, they also track data about your sleep. The Garmin Vivofit and the Polar Loop also allow you to measure your heart rate and the associated calories burned during workouts. Pretty basic stuff really, nothing too fancy. Once the data has been collected you can review it in an app. The reports are very easy to understand, but it’s easy to brush over them. As a matter of fact, many people I know don’t use the dashboards. Instead, they simply look at their total step number. I believe that you can do more. Last year I wrote a very popular post called “Data is only useful if you use it!“. The activity tracker is a prime example. Here is the process that I leverage.

The Garmin Vivofit Dashboard

The Garmin Vivofit Dashboard

Five easy steps

1. Collect a bunch of data.

Start using your activity tracker for a few weeks. Make sure to wear it every single day. Wear it all the time. Synchronize frequently to avoid losing data. Also, make sure to familiarize yourself with the reports that are available for your device.

2. Analyze your lifestyle.

Once you have collected data, spend some time to look at the reports. I discovered a few surprises:

  • Reaching the typical goal of 10k steps per days is not that hard for me. A typical morning run can easily get me above to 10000 steps before 8am.
  • A typical workday is a bit of a shocker: Conference calls, admin work and email create long periods of complete inactivity except for the occasional walk to the coffee machine or the bathroom. As a matter of fact, the morning runs often account for 80% of the activity for the entire day.
  • Weekends and vacation days usually show a high activity level. I typically move around a lot and it is spread evenly throughout the day.
  • No wonder that conferences and trade-shows are so exhausting: the five most active days (as measured in steps) are linked to conferences. You constantly move, hardly ever sit around and often walk long distances.

Check out the charts below. Pretty interesting stuff.

Vivofit Report

A typical workday: Run in the morning and then a lot of nothing. Not good!

Vivofit Report

Vacation day – constant movement

3. Identify weak spots.

Now that you have found some interesting patterns, identify your weak spots. I found three specific areas:

  • Not enough sleep
  • Too many periods of complete inactivity during working hours
  • Hardly any activity on workdays when I don’t work out (steps below 5000)

It’s fairly easy to get this information out of the reports.

4. Make changes to your lifestyle

It’s time to make some changes. In general, scientists recommend to stay active throughout the day to keep your metabolism engaged. And some of the activity trackers can help you with that. My Garmin Vivofit, for example, features a red bar on top of the display which displays inactivity. To clear this bar you basically have to move and do something.

Sitting on a plane...

Sitting on a plane…

In general, here are some of the things that I have changed:

  • Instead of taking mental breaks at my desk (surfing, reading the news, personal email), I now get up every 45-60 minutes and spend a few minutes doing an activity (walking, push-ups, stretching).
  • 3-4 short walks on rest days. It’s good to get out!
  • Focus on sleep

5. Use the activity tracker for daily motivation

Once you have some goals and objectives, you can also use the activity tracker to get motivated. First of all, there is the daily goal that all of these devices provide you with. Then some of them also have badges for certain achievements. It’d kind of fun to work on getting them. Last but not least, you can also participate in step challenges with friends and families.

Garmin Vivofit Badges

Collecting badges can be fun

Summary

Activity trackers can definitely provide you with some interesting insights. However, you do have to make an effort to analyze the data. Simply looking at the total number of steps is probably a waste of money. To do that you can purchase a cheap step tracker. It’s the analysis where you get the bang for the buck. Will I continue wearing the Garmin Vivofit? I certainly will. I am currently in the process of assessing how activity levels between really hard workouts affect my recovery. What are your experiences?

Keep natural gas flowing with analytics

The Power of Data

Last week, I had the honor to moderate the OSIsoft 2014 user conference in San Francisco. Over 2000 professionals came together to discuss the value and use of real-time data across different industries. There were a ton of really interesting and inspiring customer presentations. It’s just amazing to see how much companies rely on analytics these days to keep their operations running and/ or to improve their situation.

Combating the Polar Vortex

One of the keynote presentations of the conference really stuck out and I want to share the content with you. Columbia Pipeline Group (CPG) operate close to 16000 miles of natural gas pipelines in the US. Keeping the gas flowing reliably and safely is not easy to begin with. But doing that during the polar vortex that struck the East Cost of the US earlier this year is even harder. CPG turned to real-time data and analytics to keep their assets safe. The benefits of using data are tremendous as outlined in Emily Rawlings’ presentation:

  • Estimated $ 2.8M in savings from event (outages etc.) prevention
  • Increased customer confidence
  • Improved asset reliability
  • Expanded operational visibility.

If you have a few minutes to spare, take a look at Emily’s cool presentation:

 

The Power of Data

The Power of Data

Real-time data is all around us. Modern sensors allow us to capture enormous amounts of data at extremely high frequencies. Here is an example: grid operators nowadays utilize so-called syncrophasors (also called PMUs) to record over 40 different KPIs at 120hz. They use this information to keep our electric supply safe and stable. Shift managers use real-time data to keep production lines running and performing. However, managing this type of data requires a different type of technology. It’s not your typical big data problem. You can’t just stick this high-speed stuff into a simple relational database. That would be like driving around the desert with a Formula 1 car.

grid stability

Monitoring grid stability in real-time

OSIsoft

My new employer OSIsoft has been helping companies capture, archive and analyze real-time data for over 30 years. It’s quite an amazing success story. It all started with a brilliant idea to develop a high performance time-series database (the famous PI system). This has gradually developed into a true infrastructure for managing all kinds of real-time data across different industries. If you want to find out more about this, take a few minutes to watch the recent keynote from our EMEA User Conference 2013 in Paris. If you want to skip my opening words, you can safely forward to minute 10.

Enjoy!

Data is only useful if you use it!

The value factor

We have all become data collectors. This is true for corporations and individuals. Organizations store petabytes worth of customer transactions, social sentiment and machine data. SAP’s Timo Elliott recently wrote a nice blog post about the ‘datafication’ of our own private lives. Just to give you a personal example, I have over 2GB worth of exercise data (heart rate, running pace, cycling power, GPS info, etc.) ranging back to 2003. But there is a growing problem – too many people & organizations are just really good at collecting data. Not enough people are doing anything with it. Let’s face it – data is only valuable if we really use it!

The inertia problem

strava

There is a ton of data available

Leveraging data for your benefit can be a struggle: you have to process it, you have to look at it, you have to analyze it and you also have to think about it. Here is an example: let’s say I am a runner and I wear a heart rate monitor that is connected to my iPhone. I will only get value out of that data, if I am willing and qualified to analyze it after each run. Letting the data sit on my iPhone will not help me identify trends and patterns. And then there is also the step of developing and implementing specific actions: should I rest, do I need to run harder to improve my marathon time or do I actually need to slow down to accelerate recovery? The same thing is happening in organizations. Starting to trust your analytics is another whole big issue.

Take action

How can we prevent becoming masters in data collecting but rather champions in performing analytics? Based on my experience there are a number of actions we should all look at (personal & professional):

  • Examine your available data and make sure that you really understand what it all means. This includes knowledge of the data sources, meaning of KPIs, collection methods, etc..
Power data

Do you really understand your data?

  • Sit down and clearly identify why you are collecting that data. Identify goals such as increase sales, set a PR in the next marathon, increase machine performance.
  • Develop a habit of working with your data on a daily basis – practice makes perfect. Only cont
  • Acquire the right skills (attend training, read a book, meet a thought-leader etc.) – we all need to work on our skills
  • Invest in the right tools – not every piece of software makes it easy to perform analysis.
  • Collaborate with other people, i.e. share your data, discuss findings
  • Celebrate success when you are able to achieve your desired outcomes

What are your experiences? Are you really leveraging your data or are you just collecting it? What else can we do?

See you at the OSIsoft EMEA User Conference 2013

OSIsoft EMEA UC

Please allow me to do some advertising for my company today. Beginning this year, OSIsoft will host an annual Pan-EMEA Users Conference event to bring together the latest PI System information coupled with presentations from customers that demonstrate the business value they have achieved using the PI System.The user conference will take place in Paris from September 16th – 19th.

Why attend?

This event is the ideal place for existing users and prospective customers to share and learn more about the PI System and how it drives value in their businesses. It’s a great opportunity to network with industry peers as well as OSIsoft developers and executives. I personally love going to these type of events (even when I’m not presenting). It’s the easiest way to pick up a tremendous amount of knowledge within a very short period of time. And let’s face it – it’s fun as well.

What is OSIsoft PI?

Some of you might not know what the PI system does. Well, I will write about that in a few weeks from now. To keep it short and simple: The PI system allows you to collect, archive & analyze massive amounts of real-time data (we are talking milliseconds) generated by machines and sensors. Smart Grid & wind park operators, manufacturers and many others have been using the PI system for close to 32 years to make sense of all their machine signals. It’s fascinating stuff.

Click here to find out more about the OSIsoft EMEA user conference.

The Power of Data – Collaboration

It’s my data!

No doubt – there is tremendous value in data. I use data collected from a small sensor in my bike to improve my cycling performance. Factories leverage data to keep their machines humming as long and as efficiently as possible. Unfortunately, most companies have historically tried to keep data for themselves. Sharing was a foreign concept. Security concerns and cultural barriers (“It’s my data!”) have fostered this environment.

“Share your knowledge. It is a way to achieve immortality.”― Dalai Lama XIV

Collaboration

What if we could share critical data with relevant stakeholders in a secure and effective way? Would we be able to improve our performance? Take a look at this short video to see what can happen if you start sharing subsets of your data. It is a fascinating scenario.


OSIsoft will release this new technology later this year. Stay tuned for more updates.

How could your business benefit from collaboration? What type of data are you ‘hiding’ from your stakeholders?

Catching up with David Axson

Performance Management professionals around the globe know David Axson. David is an exceptional consultant, public speaker and author. His bestselling book Best Practices in Planning and Performance Management can be found on most bookshelves. In the past year David has gotten a bit quiet. We were able to catch up the other day.

Christoph: In the past, you used to jet around the planet, write books, blog and speak at a ton of conferences. But you have gotten a bit quiet lately. Where have you been hiding the past year?

David Axson: Good question – I joined Accenture in June 2011 and obviously spent a few months getting settled in, however things are getting interesting again with the Accenture engine behind me I am now leading thought leadership efforts for our finance consulting team globally.  From a market standpoint I am spending a lot of time communicating with CFO’s of large global companies about the real power of analytics, big data and perhaps most importantly how finance can be a true value generator within the business. 

Christoph: In your last book The Management Mythbuster you take a humorous look at popular management practices such as lean management, six sigma and budgeting. Most of them have not lived up to the hype that once surrounded them. Are there new management fads that we need to be aware of today?

David Axson: Well at the moment it seems like the solution to every problem is cloud, big data, analytics and mobility. We need to move beyond the broad topics to get very specifc about how these mega-trends can be applied practically to drive growth and profitability.  We need to explain to a CEO, CFO, CMO or general manager what these trends mean to them and their organizations otherwise the hype will remain unfulfilled. 

Christoph: Speaking of management fads, how do you feel about Big Data? If we trust the opinion of some industry analysts, big data is likely to create millions of jobs while also fixing a ton of problems. Do I need to worry about big data? Can big data feed my family?

David Axson

David in action. Professionals love his workshops and keynote speeches

Christoph: Without a doubt, analytics is an important discipline for most companies. Today, have the ability to collect more data than ever before and we also have the tools to put that data to good work. Where do you see the real opportunity for companies today? How can they leverage analytics for their advantage?

David Axson: Focus, focus, focus.  I have moved beyond analytics to the notion of applied enterprise performance analytics whereby an analytics strategy looks at the impact analytics can have on specific business decisions such as market selection, product and service portfolio management, customer profitability, operational excellence and the like.

Christoph: Analytics is a relatively young discipline. It did not appear in the curriculums of universities and colleges in the past. What type of new skills do managers need today and what can they do to acquire them?

David Axson: Well analytics embraces a number of disciplines such as statistics, operations research, portfolio management and financial analysis.  They key now is how these skillsets get applied through the analytic tools that are now becoming available.  Managers need to understand how to translate the potential of analytics into reality.  One technique I use is to explain how analytics can be applied to drive positive impact on specific line items in the P&L and balance sheet.  

Christoph: When we speak about analytics, we also need to speak about technology. What is the most popular analytics tool today?

David Axson: Not sure it is that simple, it is about applying the right tools for the right job. IT needs to help the business match tools to tasks.  It’s a bit like doing a DIY job, you don’t just a hammer for everything.  

Christoph: You not only write books, but you also love to read them. What’s on your Kindle today?

David Axson: Just finished The Signal and the Noise by Nate Silver – excellent read about statistics, analytics and forecasting in a real world context. 

Christoph: Can we expect another book from you in the future?

David Axson: Funny you should mention that.  Talking with my publisher about a new book focused on Enterprise Performance Analytics that takes a very pragmatic approach to applying analytics to decision making. Watch this space! 

Christoph: Thanks for the interview, David!

I had the pleasure to work with David for over five years and ended up delivering keynote speeches with him in over 20 countries. You can find out more about David on his Amazon.com page.

Naked Statistics – A book review

Scary Statistics

Amazon.com recently recommended the book Naked Statistics: Stripping Dread from the Data. Since I already knew the author Charles Wheelan from his awesome book Naked Economics: Undressing the Dismal Science (Fully Revised and Updated) I went ahead and bought this one for my Kindle. Great decision – it is one of those books that is fun to read while also adding (hopefully) long-lasting value. To make it short: Business Analytics professionals should read Naked Statistics. We work with data on a daily basis and there is an increasing emphasis on Predictive Analytics. Professionals therefore have a growing need for a decent working knowledge of statistics.

All Greek?

Many people have a hard time with statistics. College and university courses usually throw around a wild mix of scary looking formulas containing lot’s of Greek symbols. It certainly took me a while to make sense of my professor’s scribble. As a result, lot’s of people develop a fear of of this subject. Naked Statistics, however, demonstrates that it is possible to teach a seemingly complex topic in a simple manner. Charles Wheelan provides a journey through some of the most important statistical concepts and he makes it fun and easy to understand.

The content

Naked Statistics covers a broad range of the most fundamental statistical concepts such as median, standard deviation, probability, correlation, regression analysis, central limit theorem and hypothesis testing. Each concept is explained in simple terms. The author also uses a mix of fictitious stories (some of them are funny) and real-life examples to show how things work and why they are relevant. Math is kept to a bare minimum – you will only find a few formulas in the main text. Reading is easy and fun. I was surprised to find that I devoured many chapters late at night in bed (I don’t usually read business books that late).

NormalDistributionSD

The normal distribution – no need to be afraid

Naked Statistics

Naked Statistics is a great read. It provides you with a sound working knowledge of statistics and it actually motivates you to dig deeper (I pulled out one of old text books). For those people who know statistics, this book can help you brush up on some concepts. Analytics professionals might also want to recommend this read to colleagues who start working with predictive analytics and other advanced tools. Students should buy a copy before they attend statistics classes – they will certainly be able to grasp the more advanced subjects more easily. I wish I had had this book back at university. It would have saved me some sleepless nights. Two thumbs up – Charles Wheelan does strip the dread from the data.