Tag Archives: data

Reminded Again, Narrow Focus Leads To Failure Every Time. Why do Some Data Projects Never Make It?

In 1993, an incident occurred in the Toronto Dominion Bank Tower that caught national attention, enough so that it made the infamous “Darwin Awards”. A lawyer, in an attempt to demonstrate the safety and strength of the building’s windows to visiting law students, crashed through a pane of glass with his shoulder and fell 24 floors to his death. Maybe the Glass did not break but it pulled off the wall. 
The lawyer made a classic mistake, he had focused on one specific thing to the exclusion of the big picture. If he had taken a look at his hypothesis from a wider angle, he might have considered the numerous other factors  that may have contributed to his doomed demonstration – the bond between the glass and the frame,  the yielding effect of material after repeated tests, or simply the view of the courtyard below (the high risk should it fail) might have been enough to make him reconsider his “leap of logic”. He focused on a specific item and ignored the other factors. 
Such a narrowed focus is equally risky to an information management project, or any project really. Although we are getting better we often focus on one thing: technology implementation and ignore other aspects.
From my experience, many factors contribute to the success or failure of information management in Oil & Gas projects. People, technology, processes, legacy data, Integration, a company’s culture, operational model, infrastructure, time constraints, or external influences such as vendors and partners, just to name a few. Each has a degree of influence on the project, but rarely will they cause the demise of the project – unless they are ignored! The key to success in any project is the consideration of all aspects, and an assessment of the risks they impose, prior to spending millions.
As an example, let’s look at survey data. How would you manage that data?
Often, companies focus on two elements:
  • Finding the technology to host the data
  • Migration of the data to the new solution
Success is declared at the end of these two steps, but two years down the road, the business has not embraced the solution, or worse yet, they continue to see incomplete surveys, a problem the new technology was supposed to solve. Failure, in this case, is less abrupt than an appointment with the Toronto Dominion Courtyard, but it is failure nonetheless.
More often than not, projects like the one above fail to take into consideration the other aspects that will keep data quality intact.
Even more often, these projects fail to consider external factors such as data acquisition vendors. These external vendors have their own processes and formats. If your project ignores our increasingly integrated world, and cannot cooperate with the processes, technology, and data formats of key external vendors and business partners, your project will yield very limited results and will not be sustainable. 

To achieve sustainable success in data management projects or any projects for that matter, it is necessary to consider the context surrounding the project, not just the specifics. Without this context, like the unfortunate lawyer, your project too can look forward to a rather significant fall.

Juicy Data Aligned


Around the corner from my house is a local shop selling an excellent assortment of fresh vegetable and fruit juices. Having tried their product, I was hooked, and thought it would be a good addition to my diet on a daily basis. But I knew with my schedule that unless I made a financial commitment, and paid ahead of time, I would simply forget to return on a regular basis.  For this reason, I broached the subject of a subscription with the vendor. If the juice was already paid for, and all I had to do was drop in and pick it up, I’d save time, and have incentive to stop by (or waste money).

However, the owner of the shop did not have a subscription model, and had no set process for handling one. But as any great business person does when dealing with a potential long term loyal customer, the owner accommodated my proposition, and simply wrote the subscription terms on a piece of paper (my name, total number of juices owed and date of first purchase), and communicated the arrangement with her staff. This piece of paper, was tacked to the wall behind the counter. I could now walk in at any time, and ask for my juice. Yess!

Of course, this wasn’t a perfect system, but it aligned with business needs (more repeat business), and worked without fail, until, of course, it eventually failed. On my second to last visit, the clerk behind the counter could not find the paper. Whether or not I got the juice owed to me that day is irrelevant to the topic at hand…the business response, however, is not.

When I went in today, they had a bigger piece of paper, with a fluorescent tag on it and large fonts. More importantly, they had also added another data point, labeled ‘REMAINING DRINKS’. This simple addition to their data and slight change to the process made it easier and faster for the business to serve a client. Previously, the salesperson would have to count the number of drinks I had had to date, add the current order, then deduct from the total subscription. But now, at a glance a salesperson can tell if I have remaining drinks or not, and as you can imagine deducting the 2 juices I picked up today from the twelve remaining is far simpler. Not to mention the data and process adjustment, helped them avoid liability, and improved their margins (more time to serve other customers). To me, this is a perfect example of aligning data solutions to business needs.

There are several parallels in the above analogy to our business, the oil and gas industry, albeit with a great deal more complexity. The data needs of our petro professionals, land, geoscience and engineering have been proven to translate directly into financial gains, but are we doing enough listening to what the real needs of the business are? Reference our blog on Better Capital Allocation With A Rear-View Mirror – Look Back for an example on what it takes to align data to corporate needs.

There is real value to harvest inside an individual organization when data strategies are elevated to higher standards. Like the juice shop, oil and gas can reap benefits from improved data handling in terms of response time, reduction in overhead, and client (stakeholder) satisfaction, but on a far larger scale.  If the juice shop had not adapted their methodology in response to their failure of process (even if it wasn’t hugely likely to reoccur) the customer perception might be that they didn’t care to provide better service. Instead, they might just get unofficial advertising from readers asking where I get my juice. I’d suggest that the oil and gas industry could benefit from similar data-handling improvements. Most companies today align their data management strategies to departmental and functional needs.  Unless the data is also aligned to the corporate goals many companies will continue to leave money on the table.

What Impact Does Big Data Technology Bring To Oil and Gas?

Dealing with the massive influx of information gathered from exploration projects or real time gauges at the established fields is pushing the traditional data-management architecture to its limits in the oil and gas industry. More sensors, from 4-D seismic or from fiber optics in wells, crack the gap wider between data capture advancements and the traditional ways of managing and analyzing data. It is the challenge of managing the sheer volume of collected data and the need to sift through it in a timely fashion that Big Data technologies can promise to help us solve.  This was just one of the suggestions on the table at the recent Data Management Workshop I attended in Turkey earlier this month.

For me, one of the main issue with the whole Big Data concept within the oil and gas industry is that, while it sounds promising, it has yet to deliver tangible return that companies need to see in order to prove its worth.  To overcome this dilemma, Big Data vendors such as TeraData, Oracle, and IBM should consider demonstrating concrete new examples of real-life oil & gas wins. By new I mean challenges that are not possible to solve with traditional data architecture and tools. Vendors should also be able to offer Big Data technology at a price that makes it viable for the companies to “try” it and experiment.

The Oil and Gas industry is notoriously slow to adopt new software technology, particularly when it comes to anything that tries to take the place of traditional methods that have proven to work already, unless its value is apparent.  To quote my good friend ” we operate with fat margins we don’t feel the urgency”.  However, E&P companies should put their creative hats on to work alongside Big Data technology vendors. Big Data may just be the breakthrough that we need to make a tangible step-change in how we consume and analyse subsurface and surface data with agility.

If either side, vendors and E&P companies, fail to deliver, Big Data becomes a commercial white elephant and is doomed to very slow adoption.

At the workshop we had Oracle, Teradata, and IBM all showing interesting tools. However they showed examples from other industries and occasionally referred to examples that are possible to solve with the conventional data technology. They left the audience still wondering!

One Big Data example that is relevant and hits home was presented by CGG. CGG used pattern recognition (on Teradata technology) to find all logs that exhibit a specific pattern a petrophysicist may be interested in. This type of analysis require scanning through millions of log curves, not just meta-data which is what we had been bound to in traditional architecture. This opens up new horizons to serendipity and who knows maybe to new discoveries.