McGuyvering

The classic 80s TV show MacGuyver was an international sensation and it helped coin the term McGuyvering. Its main character MacGuyver always managed to get out of hopeless situations by building ingeniuous contraptions out of ordinary things. According to the Oxford Dictionary, McGuyvering is to make or repair something “in an improvised or inventive way, making use of whatever items are at hand”. Think about the band-aid holding your broken glasses together. Think a combo of plastic bag and duct tape to fix a whole in your tent. While resourceful, McGuyvered solution are usually short-lived and not intended for long-term use.

The modern data stack?

What does the have to do with data? Over the course of my career I have witnessed many cases where organizations McGuyver solutions: combining different software solutions and methodologies to achieve a specific goal. It usually goes ok for some time but sooner or later something will spin out of control. The same thing is currently happening in the data warehousing space. There are a ton of spot solutions on the market that take care of a subset of tasks that need to be taken care of during the whole lifecycle of a cloud data warehouse. Some people call the set of these tools the ‘modern data stack’. No doubt that these solutions do a great job for their respective space. But when you use a concoction of disparate instruments that were never designed to work together, you might end up with not with a modern data stack but with a modern case of McGuyvering, instead.

Sure enough, macros, scripts and work instructions then help keep things in working order for some time. But as soon as complexities increase, critical team members leave or upgrades kill fragile integration points, things can easily get out of control. You quickly end up losing valuable time, making compromises along the way and spending time fixing instead of building. And we haven’t even started talking about training people in all these different tools…. Is it really worth cutting corners in these volatile & exciting times?

Enter: Agile Data Engine

I recently joined Helsinki based Agile Data Engine. Back in 2016, our fabulous team had the vision to build a single platform to fully automate the design, build & operations processes for data warehouses. No McGuyvering required – just a single product. Fast forward to 2023 and over 40 customers in Scandinavia happily rely on the Agile Data Engine platform to help them deliver ever evolving and stable cloud data warehouses. Customers love the fact that they can do most of their work in a single environment that just works. A full array of functionality allows them to move towards a truly agile development & operation process. Does it work? Our customers enthusiastically say ‘yes!’. Some of them are now able to make up to 250 deployments per months while dramatically reducing the number of errors.

Zero hero

And best of all: the reliance on the MacGuyvers on your team – those smart people who can’t ever go on vacation because the stability of the processes is reliant on their domain knowledge has greatly been reduced. And what do the MacGuyvers think about this? They love it! No more wake-up calls in the middle of the night or the weekend when workflows fail. Instead, they can focus on what they do best: engineer and deliver great data products to their stakeholders. We call this the ‘Zero Hero’ approach.

Are you McGuyvering your cloud data warehouse? More about Agile Data Engine and what we do in later blog posts.

Is McGuyvering the right approach for your cloud data warehouse?, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

“OMG, OMG” – The discomfort I experienced was almost unbearable. My entire body was begging me to get out of the cold swimming pool on a dark & rainy afternoon in November of 2021. It was getting difficult to regulate my breathing. Two hotel guests walked by and shook their heads in disbelief that somebody would be in the pool that day. “Just another 30 seconds….”, I was telling myself. Getting out of the water in less than a minute would have felt like surrender. But I managed to activate every bit of willpower and spent 2 minutes and 30s in the 12C water that day. It was my first official ice bath.

Why?

Let’s back up for a second – why would I even entertain the idea of jumping into a cold pool? There has been a lot of talk about the benefits of ice bathing recently and a lot of research is starting to go into this area. As an avid endurance athlete, I am always curious to learn new things that can help me perform and recover better. And with that, it was just a matter of time before I decided to give it a try. It is claimed that ice baths not only help you build a stronger immune system but that they also lower stress levels, improve metabolic function and sleep. But research is still in the early stages and you can rather gather all kinds of anecdotes on the internet. All the more surprising since we have a ton of personal devices at our finger tips to test & quantify some of the claims. Wearable companies like Oura, Whoop & Garmin provide neat algorithms and features that should be able to shed some light. And I set out to do just that.

After the ice bath

Following that painful inaugural bath, I raced back to my hotel room to warm up. The shivering & discomfort continued for a few minutes. Once that had subsided, an incredible sense of calmness began spreading through my body. It was a strange sensation. Indeed, a look at my Garmin watch revealed that my resting heart rate had dropped to under 40 beats per minute. That was a huge surprise as I had done a very hard 100km bike ride earlier in the day. After these kinds of efforts, my heart rate typically stays elevated for many hours. And this sense of calmness continued for a few hours. But do wearables like Garmin & Whoop register this?

Quantifying the benefits

There are two tools specifically that come in handy: Garmin’s Body Battery & stress algorithms and Whoop’s Stress Monitor. I won’t go into details of this – the names are pretty self explanatory. So what do the algorithms say? Here is a typical response. After an initial shock of getting into the cold water, stress levels go down and the body battery recharges. Whoop records a similar reduction in stress even during the actual ice bath:

Implications

Does an ice bath solicit this type of response all the time? Surprisingly, in 80% of the cases it does. Below are some further examples. There are days where the effects are more pronounced than on others. The same is true for the subjective positive feeling in the hours after. But what about the long term effect? Most wearables measure heart rate variability (HRV) which is an important indicator of your general well-being. The higher the value, the better. Over the past two years, my average HRV has increased by 10%. Can I tie this back to ice baths? No, I can’t. There are just too many variables I have worked on. I do think though that it plays a role.

The Elephant in the Room

Again, why ice baths? The benefits seem cool (no pun intended) but the associated discomfort must offset that? The surprising thing is, that your body adapts very quickly. After just 5-10 sessions, it gets a lot easier. And about 1 month in, I started to tremendously enjoy it. There is no pain anymore. It just feels really really good. You have got to experience it to believe it. And as an added benefit, it keeps my Garmin & Whoop data looking good!

Ice Bathing Quantified, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

In the last two blog posts, I spoke about Industry 4.0 and the challenges around working with industrial sensor data. Let me attempt to quickyl summarize the outlined problems: Industry 4.0 initiatives require a ton of time-series data. Acquiring, managing and analyzing this can be extremely challenging.

This is where the OSIsoft PI System comes into play. It’s been around for almost 35 years and has helped thousands of operations & IT professional manage their industrial sensor data. Today, I want to provide a high-level overview of PI for those people who are new to Industry 4.0 and the sensor data analytics space. (Please take a few minutes to read the prior blog post for a description of the basic business problems).

It’s a data jungle

As discussed in the last blog entry, most organizations have massive struggles with capturing and managing data from their assets. If you happen to work in such an environment, you will know this scene:

The OSIsoft PI System is all about simplifying this picture. It takes care of the full process for acquiring, archiving, managing and analyzing massive amounts of sensor data. Think of the PI System as a tool that takes care of getting data from the sensors to the users and applications that need it. This is basically a three step process:

Data capture/ Collect

It starts with collecting data. As outlined before, this can be quite difficult. The PI System therefore offers a library of 450+ interfaces for virtually any kind of industrial communication standard or specific assets. There is no custom coding and no sherlock-holmesing of ancient APIs. This not only saves a tremendous amount of time but also significantly lowers risk (bad data, etc.). Further, the interfaces are smart: there is essential stuff like data buffering, filtering of bad data, etc.. along with auto-discovery of data sources. This ensures clean and reliable data.

Arrival in the OSIsoft PI System

Once the data has been collected, it needs to be stored & prepared for analysis. This is a big job that most databases are not made for. Keep in mind: Industrial sensor data is fast. 1-100hz data are not unusual and asset operators require timely information (i.e. NOW). The PI System stores the data immediately upon arrival and provides it to the users or applications in real-time. Yes, it’s a real-time system and that’s why you see PI in so many control centers around the globe. But just providing data in real-time would not be enough to satisfy the requirements of Industry 4.0. The PI System therefore does some really cool stuff such as calculations (simple KPIs and very complex mathematical formulas), unit of measure conversions, tagging of events, sending notifications etc…..

In case you want to perform historical analysis, you can also query data from 10-20 years ago in mere seconds. All data is hot and available – no more complicated archiving and waiting. This is cool stuff – think about the massive data volumes that we are dealing with here. The OSIsoft PI System does all this without any complaints – it is optimized to provide industry strength performance and reliability.

Context

How do you want to make sense of this much data? Keep in mind that sensor stream naming conventions are weird and funky (e.g. TI37.109-CP-TK9PV). A white paper by OSIsoft sums up this problem:

Typically, only a few initial users responsible for control system naming convention can fully benefit from the value built into the semantic namespace. Others spend valuable time trying to find and integrate the “right” operational data for analysis, roll ups. As a result, operational data often remain “dark” –untouched, underutilized or forgotten.

What if we could attach those weird technical names to a metadata model (like a hierarchy)? That’s exactly what the PI System does: Tag names are attached to real-world assets such as transformers, pumps & reactors. You can then navigate the tremendous amount of data through a business view and you can also create asset templates for easy system configuration. Each template can not only contain standards such as calculations, units of measure & other useful stuff (read this case study for a nice example). In effect, working with the data has suddenly become a whole lot easier. Comparing one pump with another is possible just like standardizing the sensor data models across equal assets. This is very powerful stuff in a world that drowns in data but is starving for information.

Name those events

Making sense of big data requires automated structuring of it. This is especially true for sensor data. In an industrial environment, we are always interested in analyzing specific events such as batch durations, start-ups, downtimes, etc.. These periods of time contain stories and insights that help you to improve processes. But they are notoriously difficult to find and compare when left unstructured. The OSIsoft PI System automatically bookmarking these events. You can then easily compare various batches or simply analyze what led to a downtime. This is really powerful stuff.

The last mile

Now we have captured, archived and prepard that sensor data. But data is only useful if you really use it. That requires the timely and effective delivery to users and business applications. Rest assured that the OSIsoft PI System knows how to do that as well. It starts with real-time visualization clients, it includes a powerful SDK and also a really neat BIG Data integration tool. Discussing this in detail would be too much for this post, however.

Big Data & Industry 4.0

To summarize this longer than usual post: The OSIsoft PI System is your best friend when it comes to managing sensor data. Relational databases are not made for this type of data.

Without an appropriate data infrastructure, Industry 4.0/ Digitalization efforts can quickly come to a grinding and frustrating halt. Does it require a lot get this up and running? No. Installations are usually fairly quick (we are taking days not weeks) and the hardware requirements are also nothing to worry about (it runs on my laptop).

As always, thanks for reading and sharing!

What is the OSIsoft PI System?, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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.

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

2. Speedy data

3. Big data volumes

• 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?

4. Tricky time-series

5. Analytics ready data

That tricky sensor data

Industry 4.0 and the sensor data analytics problem, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

• Improve product quality
• Lower cost
• Reduce cycle time
• Improve margins
• Increase revenue

Industry 4.0 initiatives

• Energy management
• Predictive maintenance & asset health
• Process optimization
• Quality monitoring
• Remote monitoring & connected services
• Implementation of autonomous robots
• Advanced simulation

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

The value of industrial time series data

What’s next?

Industry 4.0 & Big Data, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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.

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.

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.

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.

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?

The Big Data Challenge of Activity Trackers, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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:

Keep natural gas flowing with analytics, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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.

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!

The Power of Data, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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

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..

• 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?

Data is only useful if you use it!, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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.

See you at the OSIsoft EMEA User Conference 2013, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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?

The Power of Data – Collaboration, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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?

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.

Catching up with David Axson, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

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).

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.

Naked Statistics – A book review, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

Wow. I cannot believe it. It’s been two months since the last post on this blog. Thanks to all of you who reached out to find out why it’s been so quiet here. There is a simple reason. Sometime around New Year, I realised that I was due for a creative break from writing. It’s a ton of fun to run a blog with so many awesome readers. Still, writing got a bit harder in the last quarter of 2012. I decided to take an inventory: 196 posts over 1.5 years. That’s a lot of ink on paper! Blogging should be fun and the decision to take a step back and focus on other things for a while was surprisingly easy. I have used the time to start a few different projects including a new photography blog along with a new posterbook publication.

Next steps

Is the Performance Ideas blog done? Nope. I will be back in a while. The focus will probably shift a little bit. In my new role at OSIsoft, I focus on real-time data and the according analytics. There are a ton of interesting stories and best practices.

Guest posts

In the meantime, I’d like to invite you to submit guest posts, ideas, inspirations and stories. Stay tuned for updates!

Updates and greetings, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights

2012 is almost over and I just realized that I have not yet posted a single entry about big data. Clearly a big mistake – right? Let’s see: Software vendors, media and industry analysts are all over the topic. If you listen to some of the messages, it seems that big data will create billions of jobs, solve all problems and will make us happier individuals. Really? Not really – at least in my humble opinion. It rather seems to me that big data fills a number of functions for a select group of people:

• It provides analysts with a fresh and fancy-sounding topic
• Media have something big to write about
• BI companies obtain a ‘fresh’ marketing message
• Professionals can have ‘smart’ discussions
• Consultants can sell new assessment projects

Big data – really?

I do apologize for sounding so negative. But I have a hard time finding big value in this big data discussion. Please don’t get me wrong – I would be the last person to deny that there is a tremendous amount of value in big data. But it does not deserve the hype. On the contrary, I personally find that the current discussions ignore the fact that most of us do not have the skills to do big data. We need to get the foundation right and make sure that we can tame the ‘small data lion’ before we tackle the big data Gozzilla. Don’t believe me? Consider the following:

• Spreadsheets are still the number one data analysis tool in most organizations.
• Managers still argue about whose revenue and unit numbers are correct.
• Knowledge workers have yet to learn how to make sense of even simple corporate data sets.
• 3D pie charts are floating around boardrooms.
• Companies spend over 6 months collecting and aggregating budgets only to find that a stupid formula mistake messed up the final report
• Hardly any professional has ever read a book or attended a course about proper data analysis

Here is the thing: Dealing with big data is a big challenge. It will require a lot more skills than most of us currently have (try finding meaning in gazillion TBs of data using a 3D pie chart!).

A big data problem

Earlier this year, I acquired a 36 megapixel camera. You can take some amazingly gorgeous photos with it. But it comes at a cost. Each photo consumes 65-75MB on my sad hard drive. Vacations now create a big data challenge for me. But guess what: this camera is anything but easy to handle. You have to really slow down and put 100% effort into each and every photo. 36MP have the ability to reveal every single flaw: The slightest camera shake is recorded & exposed. Minimal focus deviations that a small camera would not register, kill an otherwise solid photo. In other words: this big data camera requires big skills. And here is something else: The damn camera won’t help you create awesome photos. No, you still need to learn the basics such as composition, proper lighting etc.. That’s the hard stuff. But let me tell you this: If you know the basics, this big data camera certainly does some magic for you.

Big data – what’s next

Ok. That was my big data rant. I love data and analytics. No doubt – there is a tremendous amount of value we can gain from those new data sources. But let’s not forget that we need to learn the basics first. A Formula 1 driver learned his skills on the cart track. At the same time, there is a lot of information hidden in our ‘small data’ sources such as ERP, CRMs and historians. Let’s take a step back and put things into perspective. Big data is important but not THAT important.

With that: Thank your for following this blog. Happy holidays and see you next year!

Christoph

Big Data – Can’t ignore it?, The performance ideas blog - The Performance Ideas Blog | Christoph Papenfuss - Management & Leadership Insights