July 22, 2018 by

Data before delivery: putting the cart before the horse?

I have now heard dozens of stories from friends and colleagues about failed BI and analytics initiatives. They range in costs from hundreds of thousands to tens of millions of dollars. It’s a problem that I see several companies at risk of repeating…and the motivation for today’s blog. A common thread to most of these stories is trying to “fix up our data before we select or implement an analytics tool”. How can anyone possibly understand the data needs, the use cases, and the possible data issues without providing a means to use the data, view the data and identify issues? It’s like trying to anticipate what part of a car a mechanic should fix without test driving it first. Consider starting with the data you have, take it for a test drive. See how the business wants to use it and evolve your data quality and architectural initiatives incrementally. You’ll realize value on the way and better focus your efforts. Why do they fail? Lack of focus: Hyped up terms like “big data”, “data lakes” and “cloud” distract us from the pragmatic task of delivering information ­­­— getting reliable, current information into the hands of business users in a form...

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May 7, 2018 by

Machine Learning: Is it really a Black Box?

Machine Learning isn’t the “black box” that many perceive it to be. On complex data sets, the use of Machine Learning with a rigorous process and supporting visualizations can yield far more transparency than other methods. What is a “Black Box”? Machine learning models are sometimes characterized as being Black Boxes due to their powerful ability to model complex relationships between inputs and outputs without being accompanied by a tidy, intuitive description of how exactly they do this. A “Black Box” is “a device, system or object which can be viewed in terms of inputs and outputs without any knowledge of its internal workings” (Source: Wikipedia). Black Boxes (and Machine Learning models) exist everywhere We tend to label things as “Black Boxes” when we don’t trust them more than when we don’t understand them. Machine Learning models aren’t unique in having an element of “mystery” in how they work – there are all sorts of things we trust all around us for which we don’t fully understand the inner workings. GPS, search engines, car engines, step counters, even the curve fitting algorithms in Excel are examples where we trust what’s happening inside because we’re able to see and, with experience,...

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March 2, 2018 by

Machine Learning: Finding the signal or fitting the noise?

Before machine learning came along, a typical approach to building a predictive model was to develop a model that best fit the data. But will a model that best fits your data provide a good prediction? Not necessarily. Fortunately, there are machine learning practices that can help us estimate and optimize the predictive performance of models. But before we delve into that, let’s illustrate the potential problem of “overfitting” your data. Fitting the Trend vs. Overfitting the Data For a given dataset, we could fit a simple model to the data (e.g., linear regression) and likely have a decent chance of representing the overall trend. We could alternatively apply a very complex model to the data (e.g. a high-degree polynomial) and likely “overfit” the data – rather than representing the trend, we’ll fit the noise. If we apply the polynomial model to new data, we can expect it to make poor predictions given it’s not really modeling the general trend. The example above illustrates the difference between modelling the trend (the red straight line) and overfitting the data (the blue line). The red line has a better chance of predicting values outside of the dataset presented. Due to the powerful...

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January 15, 2018 by

Innovation or Dishwashing Robot?

The start of a new year is often accompanied by commitments for change and improvement, both business and personal. We’re all striving to innovate, “to introduce something new; make changes in anything established”. In business, this often involves technology. How you approach the introduction of technology could be the difference between realizing true innovation or ending up with a “Dishwashing Robot”. Dishwashing Robot Image Source: Popular Science 2010 A friend introduced me to the expression “Dishwashing Robot” as a way of describing what happens when you apply new technology to an old way of doing things. It seems innovative on the surface, but it doesn’t bring true, impactful change to an organization. “Innovation” (e.g. a dishwasher) occurs when you leverage the full potential of new technology to change a process and realize optimal benefits. So my challenge to you, and myself, is to consider how we can change processes in our organizations to maximize the benefits of any new technologies we introduce. A common “Dishwashing Robot” in O&G producing companies A common focal point when introducing business intelligence solutions to an Oil and Gas producer is to replace the tedious task of assembling the Weekly Production Report – delivering it faster...

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November 13, 2017 by

Should you build or buy software for your company?

I have to admit right from the start: I am not what anyone would call a neutral observer on this topic. After all, I run a company that builds and sells visual analytics software. I’ve done so for over a decade. It’s in my interest that someone – you, perhaps – be interested in buying that software. I come to these biases honestly and I state them upfront. However. Like many senior leaders and executives out there, I’m also trying to do more with less. I want to achieve my business goals with only the most calculated investments. I’m trying to deliver value as quickly as possible and make sustainable choices that will serve my company, and my clients, well over the years to come. At Verdazo Analytics, we have a unique perspective on the question of build vs. buy, not just because we’re a software vendor but because we might actually have the capabilities to build some of the software products we need to run our business or some of the tools we might embed in our software offering. So, here are six questions I consider before deciding whether to build software or buy it. I hope they’ll be...

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November 6, 2017 by

Managing Uncertainty: the difference between Investing & Gambling

The last week of September had me doing three industry presentations that all shared a common theme, “uncertainty”. Today’s blog will focus on the presentation Uncertainty Considerations for Development Planning Type Curves. Uncertainty extends through the reservoir, drilling, completions & operations and is compounded by commodity prices. What is certain is that shareholders have little tolerance for production shortfalls. The following image show the reduction in stock price of 8 companies in 2017 that occurred following the announcement of production shortfalls. Figure 1: Stock price reactions to production shortfalls I could have easily showed you several companies who hit their production targets and maintained or increased their stock price. While it’s not important to know who these companies are, it is important to know that there are best practices that can help protect you from targeting statistically unachievable results and falling short of your production promises. Wait a minute, isn’t that called “sandbagging”? “Sandbag is a tactic used to hide or limit expectations… in order to produce greater than anticipated results” according to Investopedia. A disciplined approach to characterizing, understanding and managing uncertainty is a strategy to mitigate your downside and is consistent with the practices of “investing”. “Gambling” typically...

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June 13, 2017 by

IP90 and the Datasaurus: The dangers of summary statistics

I recently read the article Same Stats, Different Graphs which spoke about Anscome’s Quartet, four highly varied datasets that are identical when examined using summary statistics, then took it to the next level with the Datasaurus Dozen by demonstrating how highly varied datasets could produce the same summary statistics. Datasaurus Datasource: Alberta Cairo  The article states “It can be difficult to demonstrate the importance of data visualization. Some people are of the impression that charts are simply “pretty pictures”, while all of the important information can be divined through statistical analysis.” It also references Alberto Cairo who created the Datasaurus dataset to urge people to “never trust summary statistics alone; always visualize your data” because visualization can reveal valuable insights that could be otherwise missed. This inspired me to revisit my 2015 blog How useful are IP30, IP60, IP90 … initial production measures where I illustrated how two wells with identical IP90 production performance measures had very different production profiles.  To further illustrate the dangers of using near-term production performance measures like IP90, without visualizing the production, I’ve expanded the dataset from my previous blog to show just how different the production profiles are of 33 Montney Regional Heritage wells...

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November 14, 2016 by

How a producer started its analytics journey

At Verdazo Analytics, we are advocates of ‘Discovery Analytics.’ Discovery Analytics is an  approach by which a data-driven investigation shifts with new insights revealed during the process. It’s not something calculated from start to finish. It evolves as you go, often bringing you to a point you couldn’t have predicted when you started. The results and decisions made are more pertinent and powerful as a result. I think of this idea regularly when looking back on the work we’ve done with a producer here in Calgary. They initially used VERDAZO software as part of an initiative to improve their Well Review process. Their results went beyond their expectations: they were able to free up their engineers from days of manual Excel analysis in advance of a well review, saving time that exceeded $175,000 in annual costs. But that was just the start. That initial project spawned something the producer couldn’t have initially predicted: a commitment to analytics that eventually spread across their organization. New projects and efficiencies were created and an analytical culture took hold. It was exciting for our team at Verdazo Analytics to see our client discover how much value could be uncovered, in so many unexpected ways....

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October 27, 2016 by

How VERDAZO enabled an O&G startup to thrive during tough economic times

Our Oil & Gas clients continue to emphasize an increasingly acute need to do things smarter, simpler and more cost-effectively across their organizations. That’s one of the reasons why VERDAZO has been effective for them: it enhances their analytical capability and capacity no matter their economic constraints. This is important for any producer where the order of financial magnitude of a decision can be in the multi-millions of dollars. However, it may be even more critical for junior companies who often walk a thin financial line on the way to full capitalization and initial production. When Calgary-based Burgess Creek Exploration was founded in 2015, VERDAZO was its very first purchase.With 20 years of experience in the industry, President & CEO Kory Galbraith knew he could use our software to do everything from building a compelling case for initial investment capital to keeping his team lean (without sacrificing analytical insights) to creating data-driven consensus across his organization. We have always been deeply invested in our clients’ success and to see Burgess Creek thrive in such difficult economic times is a point of pride in our office. We’re very pleased at the contribution our software made to its growth. You can read...

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October 17, 2016 by

Multivariate Analysis: Completion Optimization’s Silver Bullet?

The term Multivariate Analysis has gained in popularity – and hype – in the oil and gas industry, particularly as it pertains to completion analysis. The goal of Multivariate Analysis is typically to understand the relationship of multiple input variables to one or more outcomes, attempting to isolate the effect each individual input variable has on a particular outcome. Given the many geological and completion parameters that influence production profiles, it is not surprising that the industry is embracing Multivariate Analysis in its search for optimal completion designs. The use of this term has evolved to include a broad range of tools and techniques, such as: Visual tools like parallel coordinates that visually communicate the relationships between inputs and outcomes Workflows that leverage mathematical, statistical and visual techniques to identify the most pertinent inputs and determine optimal design considerations (this is where VERDAZO excels) Regression analysis (often perceived as a “black box”) that aims to isolate the most pertinent inputs that influence optimal completion designs and arrive at a predictive equation Image source: Wikipedia Multivariate analysis encompasses a broad range of tools, techniques, technologies and workflows… but not without notable dangers. I sat down with Tyler Schlosser, Director of Commodities Research, GLJ Petroleum Consultants,...

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