The performance ideas blog

Category: Analytics

  • Is McGuyvering the right approach for your cloud data warehouse?

    Is McGuyvering the right approach for your cloud data warehouse?

    BOOM! I was on the final 15km of an extremely hot bike ride when things suddenly spiraled out of control. A grey piece of metal in the middle of the road had pierced my front tire and led to a forceful explosion. Luckily, I was able to avoid a crash. Here I was in the middle of a New Jersey forest on one of the hottest days of the year. Cell phone reception? Forget it. What to do? A new inner tube would have poked through and gotten pierced shortly after. Time to McGuyver! Two dollar bills neatly folded into the tire carcass helped keep the new inner tube somewhat protected. It wasn’t a pretty solution and the tire wobbled along. But it held up for 15km and got me home safely. 

    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.

    Agile Data Engine: A powerful & single platform

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

  • Ice Bathing Quantified

    Ice Bathing Quantified

    Shock!

    “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.

    No ice cubes required – a mountain river

    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:

    You can see stress levels decrease around 14:30h and the Body Battery % increase
    Whoop measures a significant drop in stress levels during the 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!

  • Industry 4.0 and the sensor data analytics problem

    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 & 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:

  • 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?

  • 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?

  • 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.

  • Big Data – Can’t ignore it?

    Big data

    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

    Pie Chart

    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.burj khalifa

    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