Review: Digital Canterbury Tales
1) The Digital Canterbury Tales, Part one: The Secret of standing tall against a strong Wind: A tale about internal technical authority
The Digital Transformation or Digital Revolution of the Oil & Gas Industry can be a very exciting time, but how exciting, or how scary, depends on your point of view. Much like the classic tale by Geoffrey Chaucer in the late 14th century, The Canterbury Tales, the journey depends a lot of your perspective and your responsibilities. Chaucer leads us on the journey to Canterbury Cathedral through 24 stories and through the eyes of characters like: the wife of Bath, the Squire, the Parson, the Monk and the Sargent at Law. Those job positions aren’t really relevant for this story, but there are others that are.
Chaucer leads us on the journey to Canterbury Cathedral through 24 stories and through the eyes of characters like: the wife of Bath, the Squire, the Parson, the Monk and the Sargent at Law. Those job positions aren’t really relevant for this story, but there are others that are.
I thought it would be an interesting exercise to write about the digital transformation from the perspective of some folks that you might not ordinarily think about. The big management consultants talk a lot about the importance and the impact on the C-Suite so I will leave that audience to them. I want to take you through this transformation from the eyes of some of the “little people” that have to make projects successful but have smaller voices. People like data stewards, front line supervisors, domain experts from traditional disciplines. But I want to start this series by putting on the shoes of an internal technical expert from the IT department. Let’s call her Melanie.
One of the critical issues for Melanie is ‘how can internal experts compete with the marketing power of external tech vendor and influence executive decision making?’ These internal IT tech experts have quite a lot of experience working inside the company’s current IT environment and Melanie has built up a very good reputation, she has Influence but without authority.
One of the challenges is with emerging technologies: expertise is not measured by experience given fast moving digital technologies. For most companies the budget belongs to the managers not the technical professionals. The tech expert doesn’t even have an expense account to complete with the consultants and tech marketers to take them out to a fancy lunch and try to persuade them. How can Melanie get management to listen to her point of view?
To use another metaphor from English literature: “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair, we had everything before us, we had nothing before us, we were … (Charles Dickens, A Tale of Two Cities)
Why do I use that metaphor for the internal IT Tech Expert? Let me try to explain. In many respects “It was the Best of Times.”
• Many companies have started a Corporate Digital Transformation and Innovation program so the C-Suite is engaged and is expecting results.
• IT departments now have enterprise agreement with Cloud Service Providers (Microsoft Azure, Google Cloud and Amazon AWS) so their proprietary computing platforms are due for an upgrade.
• Accelerating pace of digital technology has everyone focused on how to adopt new technology enabled solutions.
• New centers of excellence in data analytics and IIoT (Industrial Internet of Things) have been set up to bring in new data scientist and digital tech experts into the company and get them working.
• Industry embracing Digital Oilfield 2.0 and Manufacturing 4.0 or at least it have a strategy to try to.
But in other ways “It is the worst of Times.”
• There is quite of bit of uncertainty in the internal tech expert community if and Oil & Gas company management considers IT to be a critical technology or just a back-office service provider. Where does digital belong?
• That uncertainty includes if even IT leaders in your company value technical expertise in IT versus going to external providers who seem like they have kept up with the latest developments while the internal staff focused on keeping things running.
• There seems to be a growing conversation gap between IT leaders and tech experts (do they understand each other?) Does my manager understand me?
• Everyone wants results immediately despite the obvious challenges of integration with existing infrastructure. Each side is seeking a common language, trying to identify the business benefit of technical expertise. Melanie wonders “Isn’t is intuitive obvious that I am right?”
• Management still asking ‘What is the business case for adopting new digital technology?’ ‘Won’t agile gives us benefits quickly?’
• The unclear career direction for technical career path (or a low ceiling as compared with management or traditional disciplines in petroleum engineering or earth science) and as compared with tech companies signal that management places a lower value on internal expertise.
All of these worries can drive internal IT Tech experts towards the lower loop. Are we our worst enemy? Do we enter into conversations thinking no one understands us or values our expertise? Do we think we know the answer even before we really understand the questions? Would you be influenced by this approach if you were looking for help?
So, from this perspective, Melanie and her peers look at digital transformation with a bit of concern and frustration. Of course, the new digital technology can be very exciting. They have been following most of these trends so several years now. Melanie rightly focuses on the challenges as well as the opportunities.
The sales pitch stories of tech vendors ignore the difficult issues of legacy environments (this critical application only works on Windows 2008), data quality and data access problems. Melanie and her peers are the ones who end up being responsible for making all this new kit work along side the existing applications and infrastructure. Why won’t someone listen up front before new deals are signed and overhyped expectations are set. Melanie always seems to be the one bringing the bad news to management that projects will cost more and take longer due to the concerns she has identified all along.
So, everyone is not as excited as you thought along the digital journey of oil and gas. There are some voices that remind us of practical challenges and past project failures. These voices may not be the life of the party but they do need their place at the decision-making table. Slow down before you sign that next purchase order or launch that next transformation project and give Melanie a chance to contribute in the decision. You might just find out that she can help make those investments pay out a little faster and are sustainable.
2) The Digital Canterbury Tales, Part two: Feeling invisible (and vulnerable) in the middle of a busy intersection: A tale about data stewards
The Digital Transformation of the Oil & Gas Industry can be a very exciting time, but how exciting, or how scary, depends on your point of view. In the first part of this series, following the model of the classic tale by Geoffrey Chaucer in the late 14th century, The Canterbury Tales, I told the story of Melanie an internal technical domain expert from the IT department. In this part, I want to take up the story of digital transformation from the perspectives to two data managers or data stewards, Let’s call them Gayle and Carrie.
It can be said the emerging digital technology is the engine of the digital oilfield, but I would add that data is the fuel needed for that engine. Technology seems to get all the attention (better marketing budgets I guess) while data seems to be the invisible partner. Executives, consultants and especially tech vendors just assume that good information across the corporation is easily available, in a standard format with high quality. But most of the postmortems on failed or troubled projects point to the fantasy of this assumption.
Gayle and Carrie live in the trenches of the data foundation (of sorts) of their company. Data management solutions have been available for some time but were developed and adopted by functional need and local investment. Carrie is the data steward of a critical system of record for well related information. For the purposes that the data management solution was created it works reasonably well. Data quality is always something to keep an eye on as data flows from a number of sources into her system of record. She works hard, with few resources, to keep the data of good quality for her business users and to respond to their requests. The volume of data is rising and the variety of data types is growing as her interpreters strive to create more accurate models for identifying new drilling locations.
Gayle works with a different data challenge. Her responsibility lies in bringing the master (or header attributes) from a variety of data sources together into a company data hub. Since there is no single trusted source for all well, facility, equipment and asset data, she has searched far and wide to find the best sources and integrate master data into a virtual “get my data” hub. The different data standards for each application she finds creates a real vocabulary translation challenge. Building a common language goes above and beyond the traditional data quality issues of accuracy, completeness, timeliness and consistency. She has to present a very simple data visualization (and standard dashboards) from a real rat’s nest of systems behind the scenes.
Now that is the reality before digital transformation. What changes when the new reality hits? Expectations grow, from the C-suite to the bench engineer. Every talk to you listen to or article you read suggests that the way to reduce costs, improve profitability and create competitive advantage comes from the application of advanced analytics, artificial intelligence and machine learning. You don’t read much about data management under these new assumptions. Cross-functional models and lifecycle analysis are the new requirements but has anyone stopped to understand what these new demands mean to folks like Gayle and Carrie?
One metaphor that comes to me is the story of Alice in Wonderland by Lewis Carroll. Most everyone knows this children’s favorite but the scene when Alice meets the Red Queen has a particularly important lesson for our data managers.
“Now! Now! Cried the Queen. “Faster! Faster!” And they went so fast that at last they seemed to skim through the air, hardly touching the ground with their feet, till suddenly just as Alice was getting quite exhausted, they stopped, and she found herself sitting on the ground, breathless and giddy.
The Queen propped her up against a tree, and said kindly, “You may rest a little now.” Alice looked round her in great surprise. “Why, I do believe we’ve been under this tree the whole time! Everything’s just as it was!”
“… in our country,” said Alice, still panting a little, “you’d generally get to somewhere else – if you ran very fast for a long time as we’ve been doing.”
“A slow sort of country!” said the Queen. “Now here, you see, it takes all the running you can do to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that.” (from Lewis Carroll, “Through the Looking Glass”, 1871)
Gayle and Carrie represent a data management community that have been running as fast as they can to keep up with traditional data management requirements. Their staff numbers and their budgets have been reduced in the low oil price tough times but not reflated when the prices go back up and the drilling rigs get back to work. They have to run as fast as they can just to stay in place. But when the digital transformation starts to reach their senior executives, they are expected to run faster, at least twice as fast to meet the new requests of the data scientists their company just hired.
When looking for the right quote from the Lewis Carrol classic, I came across another version of the Red Queen’s lesson. The Red Queen hypothesis, also referred to as Red Queen’s, Red Queen’s race or the Red Queen effect, is an evolutionary hypothesis which proposes that organisms must constantly adapt, evolve, and proliferate not merely to gain reproductive advantage, but also simply to survive while pitted against ever-evolving opposing organisms in a constantly changing environment.
I think most folks would agree that data management practices need to evolve in the age of Big Data (increasing volumes, variety and velocity). Staying put with traditional solutions is not keeping up. But how to run twice as fast as your previous top performance is not an easy challenge to meet. It is going to take a different approach, more investment and more management attention if Gayle and Carrie are going to win the new race.
3) The Digital Canterbury Tales, Part Three: I am their leader, which way did they go: A tale of leadership in the digital age.
The Digital Transformation of the Oil & Gas Industry can be a very exciting time, but how exciting, or how scary, depends on your point of view. In trying to think about all the different perspectives in our industry I had the idea to follow the model of the classic tale by Geoffrey Chaucer in the late 14th century, The Canterbury Tales. So, in the first part of this series I told the story of Melanie an internal technical domain expert from the IT department. In the second article, I told the story of two data managers, Gail and Cary. In this third part, I shift to the perspective of management and their challenges adapting to the new reality of data-driven transformation. Let’s call our manager, Amy.
The increased emphasis on digital transformation requires a rethink about the digital literacy of the workforce (in almost every job). The competence model must now include attention given to statistical analysis, Artificial Intelligence, Machine Learning and all the new technology accompanying this trend. The traditional role of career management brings a new challenge to the role of a supervisor/manager. This maybe a first where a significant new skill/competency is thrust on an organization from the outside-in and bottoms up rather than top down or from traditional industry technical training methods.
These skills are coming from consumer technology, self-learning, on-line courses and not from the corporate research or training departments. These are not skills the supervisor/manager developed in her earlier career assignments. So, the challenge facing Amy is one where she struggles to help or mentor her staff on what courses they should take, what technology is best in their career development discussions. Amy may not have even heard of some the new tools being deployed. When I was a new supervisor, Python was a snake, Amazon was a river in Brazil and Google wasn’t even a word.
While she is off at a staff meeting on next year’s budget, or a new reorganization, her staff are experimenting with data-driven, neural network forecasting/optimization models. When she gets back to her group, she has no clue how they developed the model or can even evaluate if the new production forecasts are reasonable. Can she take these plans to her boss without understanding how they were developed? Does she step aside and let the young professionals take the reign and look clueless in the process?
In previous generations, while technology always moved forward, she had some idea of how to judge and direct the application of technology for her business unit. Now she has trouble even recognizing the words they are using. (I thought R was a letter following Q, neural networks had something to do with brain surgery, robots were used in car manufacturing, not in the oilfield, and what the hell is a chatbot or malware?) Where is the management 101 course for all this new technology? When does Amy have time to catch up with her staff on these new developments? Does she really need to learn to code to keep up with her new engineers?
A new category of jobs is opening up that involves bridging the growing gap between technologists and business leaders. These jobs will become more important as AI systems become increasingly opaque. Many executives have already become uneasy with the black-box nature of sophisticated machine learning algorithms, especially when those systems recommend actions that go against the intuition developed from years of experience.
A deep-learning system provides a high level of prediction accuracy, but companies may have difficulty explaining how those results were derived. The challenge shifts to understanding, transparency, reproducibility and auditability.
In writing this article I came across an article by Tom Davenport “Don’t Be an Analytical Jerk!” who among other sage advice recommended that “Don’t pretend that you understand something that you don’t. The analytics and AI profession is sufficiently complex that no one can understand everything about it. If you say, “I don’t understand that—can you explain it?” then people will respect you more, not less. It suggests that you are confident and secure in what you do understand.”
Mr. Davenport, who has written a great deal about the role of analytics concluded this article with this: “The most successful analytical leaders I have met or worked with are the most modest and self-effacing. They don’t need to be the smartest person in the room, or to impress you with their academic credentials. Their goal is to make a difference within their organizations, and for the most part they succeed.”
Amy is a good leader. She recognizes that she can’t be an expert in everything despite her position on the organization chart. She listens and learns. She allows her group to experiment and pilot new approaches but when there is a big decision to make, she asks enough questions to understand. She challenges the data that is used to be sure is all comes from a trusted source. She makes sure the analytics is constrained by good physics, engineering and financial common sense.
The organization has put Amy in a leadership role, but in this brave new digital world, Amy also needs to be a good follower and a good learner as well. Gone are the days when the boss is supposed to know everything. Amy may have days where the blood pressure rises a bit, when the anxiety kicks in that she isn’t in control anymore and that her feet aren’t on solid ground. Grab an antacid pill and trust your people, trust the data and give it a try. You just may like it.
4) The Digital Canterbury Tales: Part Four: Correlation is not Causation: A tale of the discipline domain expert
The Digital Transformation of the Oil & Gas Industry can be a very exciting time, but how exciting, or how scary, depends on your point of view. In thinking about all the different perspectives in our industry, the classic 14th century story by Geoffrey Chaucer, ‘The Canterbury Tales’ came to mind in explaining the different views of what digital transformation brings to the industry. In the first part of this series I told the story of Melanie an internal technical domain expert from the IT department. In the second article, I told the story of two data managers, Gail and Cary. In the third part, we heard to story of Amy an experienced manager. In this fourth part, I shift to the perspective of a functional domain expert and the challenges comparing and contrasting new data-driven models to traditional physics and engineering simulations. Let’s call our domain expert: Ayush.
For many experienced domain experts, from earth science, to drilling, to production, to reservoir management, the right way to understand the processes of oil and gas production is through physics-based approaches. They spent their universities days learning all about the physical processes underpinning oil & gas production. From the classic Darcy’s Law or fluid flow through permeable material, to the Ideal Gas Equation, to mass balance relationships and many others, technical disciples from geology, to geophysics, to reservoir engineering, drilling, completions and production engineering, the gospel was physics and anything else was either lazy or wrong. Most people don’t want to remember that many of the physical laws we take as the truth today were developed through years of experimental lab work by under-appreciated graduate students.
Then comes along problems that we haven’t yet figured out the physical equations for. Shale is not a permeable rock and fluid flow is determined mainly by fractures not pore space. Sorry, you can’t use Darcy’s Law simulation routines, you have to use ‘type curves.’ The convergence of operational technology and information technology has brought field instrumentation and process control data to corporate domain experts. But we don’t have the physical understanding of how a compressor and a pump works. But we do have a lot of data.
All this “Big Data” opens the door for the statisticians to get involved. The term ‘empirical’ which used to be disrespected by physics-centered domain experts is now the rage. Computer scientists have gone further with data-driven models involving the newer fields of artificial intelligence and machine learning. But Ayush and his colleagues are still skeptical. They can’t get over their deep desire to understand how processes work. They can’t quite trust the results of a model when they can’t really understand how it works. Black box models just don’t cut it for this group of experienced experts.
“Correlation does not imply causation” is a phrase used in statistics to emphasize that a correlation between two variables does not imply that one causes the other. Many statistical tests calculate correlation between variables. That is their “goodness of fit” criteria (i.e. r squared or p-value tests) help to let us know how well the model predictions match the input data. But taken too far correlations taken for root causes can mislead with advanced statistics rather than enlighten.
I once sat in a talk at an SPE conference where a bright graduate student was giving a paper on the ‘stuck pipe’ problem in drilling where the drill bit and drill pipe can be trapped against the well bore by a pressure differential. This is a major problem in some formations and can costs a lot of time and money to recover from. The paper was all about using data to model the ‘stuck pipe’ problem using an AI technique called SVM (support vector machine).
The researcher concluded his very complex and sometimes difficult to follow presentation and opened the floor for questions. One of the old drillers in the room asked the simple question of how to use the results of the study as all of the variables used to identify the stuck pipe condition did not have a physical meaning (like mud weight, ROP or weight on bit). The student confessed he had never been on a drill rig or experienced a stuck pipe condition so he had no idea how to use the results on a drill floor, or in the design of a new well bore, so he couldn’t answer the expert’s question. That is not how we will move the needle on the application of advanced analytics.
So, while Ayush and his colleagues are full of doubt about the results of advanced analytics, and they are frustrated that they are losing their position of respect in companies, as the wizards of the physics world are being pushed aside in the rush to worship the new ‘data scientists.’ The better answer is to bring together the domain experts with the data scientists to study the complex problems facing the industry. In machine learning vocabulary this means ‘supervised learning’.
It wouldn’t be a bad idea to include Cary or Gail to help find the best, more trusted data. You also may want to invite Melanie along to see how the new algorithm is going to fit into the existing computing infrastructure. Then when you have everything ready to go, you can meet with your supervisor, Amy and try to explain all this to her.
If it takes a village to rise a child, it probably takes a team to solve today’s complex operations problems regardless how much data you have. The old saying of trust but verify is probably still true.
5) The Digital Canterbury Tales: Part Five: It was the best of time, it was the worst of times: A tale of the consequences of data democratization
The Digital Transformation of the Oil & Gas Industry can be a very exciting time, but how exciting, or how scary, depends on your point of view. In trying to think about all the different perspectives in our industry I had the idea to follow the model of the classic 14th Century tale, The Canterbury Tales by Geoffrey Chaucer. In this series so far, I have told the story of Melanie an IT technical domain expert, the story of two data managers, Gail and Cary, the story of Amy an experienced manager and the story of Ayush, the traditional domain physics expert. In part five, I want to look at the challenges to the existing corporate data foundation given the new trends in data democratization. The two lead actors in this debate are the CIO (Kim) and the data analytics lead (Grant).
Most corporate data management solutions grew up around functional work processes. From today’s perspective, this history has led us to “silos” or “islands” of information that can be difficult to integrate. It is still easier, given the way oil and gas operators are organized, to manage data this way. The industry has mature data base technology (for structured data) and commercial applications are available and in wide spread use. It is not easy to cope with all the increased in data volumes, concerns for standards and data quality and the difficulty establishing effective data governance even with current solutions in place and staff trained to operate them.
So, Kim and her data management staff have their work cut out just to keep the current environment working. A majority of Kim’s staff have a lot of experience (a good thing) but are thinking about their retirement dates (not a good thing). These days, everyone wants to be a data scientist, but not that many are really thinking about becoming a data steward.
The company has just formed a new group of the newly recruited data scientists and Grant has the privilege to manage this team of data experts. Of course, they need data, lot of data, data from different disciplines, different types of data and they need it now. So, Grant and Kim are getting to be good friends. Before the new artificial intelligence (AI) and machine learning (ML) methods being developed by Grant’s data scientists can be used, data must be collected and aggregated in one place. This data integration effort is the top priority for Kim and Grant and they are making some progress, but it seems like each request/project requires special handling so it takes a lot of time and effort by a lot of people to get ready for the magic.
But the trend towards digital transformation doesn’t stop there. Everyone wants to be a data scientist, so the requests for new and complex data collections are starting to come from just about anywhere in the company. This worries both Kim and Grant. Kim is responsible for information protection, individual privacy and protection of intellectual property as well as good data quality and efficient data access. But everyone seems to want a Google like data button, from an intuitive data hub, so they can do their data mining and model building, on their own with the help of IT.
While supporting corporate objectives, Kim would like this demand to slow down a little, have some discipline and involvement from senior management, to make sure the right people have access to the best data. Her data foundation isn’t really ready for this data democratization yet and her budget and skills of her data management group don’t yet stretch to get to the desired state anytime soon.
Grant also has some concerns about data democratization. With everyone thinking that they can program new algorithms with their newly earned certificates from online course in R, Python or Deep Learning, who is looking after the quality and effectiveness of these new techniques? What is the role of his new center of analytics excellence when everyone is writing their own R scripts?
Isaac Asimov, in his 1942, short story “Runaround” introduced his “Three Laws of Robotics”:
• A robot may not injure a human being or, through inaction, allow a human being to come to harm
• A robot must obey the orders given it by human beings except where such orders conflict with the First Law
• A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws
Shouldn’t Kim and Grant’s company have their laws for artificial intelligence and machine learning solutions? They have gone to Amy and Ayush (see previous articles) for help and while they understand and agree with the dilemma it seems like they can’t stop the rush to analytics either. The governance process around all this new technology is too immature to manage the desire to become a data-driven organization. But what if the company is driven by poor quality data or unvalidated algorithms? Every company wants to move faster and become more agile. But moving too fast has its consequences as well.
Recently the world (not oil and gas) saw two problems, one tragic and the other just inconvenient, that fuel the debate about automation vs. human control. In the case of the Ethiopian Airlines accident, it appears that the changed location of the jet engines required a new automated system to control the plane’s pitch, but that pilots may not have been sufficiently trained to react (or not react) properly to it.
In the case of the Facebook day long outage of its messaging functions, it appears that the cause was one bad server configuration change made by a system administrator. Are these both examples of why humans should be taken out of the loop? Not so fast, say some analysts. First, in the Boeing case, the investigation is in progress, so we don’t know the root cause for certain. Secondly, someone still needs to program the software that will be used to fly the plane or to manage configurations. If there is an error in that software, and there is no human ready to take over, will the results be worse (and widespread, since the faulty software will have been widely replicated)? This is of course the debate that’s been raging about autonomous vehicles. If humans can occasionally avoid accidents but more frequently cause them, then we should embrace fully autonomous systems, but we seem to have a strong reluctance to this. Is it a cultural issue, a generational issue, a maturity issue, or what?
So, what is our advice for Kim and Grant? Governance is good but so is serving the many want-to-be data scientists who are responding to management’s call for innovation. Behind the scenes efforts in improving data quality, deploying appropriate data standards, developing an enterprise information architecture view and the right technology for data integration are all needed. Some informal or formal (depending on the culture of the company) governance process involving data stewards (and management if possible) can help place from discipline and prioritization on all the activity.
Supporting a data sandbox for Grant’s official data scientists would help the productivity and agile delivery of solutions from that expert group, as well as some sort of expert review of the new algorithms before putting them into production. But a key step is finding some executive champion or steering group who along with promoting the new direction also helps to manage the pace and quality of the solution. Yes, experimentation and fail-fast techniques are part of the new world, but some executive air-cover for the work that Kim and Grant are doing would really help.
Rule #1: Embrace good data quality practices; they lead to trusted data and faster analytics. Every house needs a foundation and data quality is the foundation for any analytics process. Knowing that your data can be trusted and meets the criteria of data quality standards allows the analyst to work on the analysis instead of spending time scrubbing data sets or worse, not being able to use certain data due to the lack of data validity.
Rule #2: Develop standards that act as guiderails to ensure team effectiveness As a team begins this journey, it is easy to develop a lot of one-off metrics, data definitions, and reporting user interfaces (UI). We highly recommend developing a set of standards that the team adheres to. This will encourage reuse of best practices and avoid duplication.
For example, one analyst might create a metric called “average basket sale” that represents the average number of items purchased in the basket. At the same time, another analyst creates the same metric, but this version refers to the total dollar amount in the basket. The end user of the report now has two different definitions for the same metric, creating confusion and lack of trust.
To avoid these types of issues, create standards. Data definitions and lineage standards can be documented in a data dictionary that the team members use to define each data element or metric. It should include information about where each data element was sourced and any transformations that were applied. User interface standards refer to the look and feel of the reports the analysts develop. A standard look and feel will help users quickly interpret multiple reports rather than having to relearn the UI for each new report.
Rule #3: Create a report certification process to engender trust in the analytics : As more teams generate reports that are consumed by broader audiences, the standards and practices leveraged by the different teams will vary. One team may not use a source of trusted data; another team may not use the company’s data dictionary to create their metrics. As reports proliferate, the report consumers will not know which ones they can trust. A certification process aligned to standards can help a report consumer know the level of scrutiny and rigor that was applied to the report.
Rule #4: Embed data science in your reporting solutions: Many business analytics teams employ a full-time or part-time data scientist. This person’s skills allow the team members to generate deeper insights from the data to aid in making better business decisions. One gap that we often notice in these teams is the insights generated from the data scientist are usually a one-time study or only repeated on request. Many of these valuable insights can instead be leveraged by your team’s analytics solutions, enabling consistently better decisions for a broader audience.
Rule #5: Develop security practices early to avoid data breaches: We often read about data breaches in the news when a company is hacked and their customers’ credit card information is exposed to the world. Although most analytics do not involve personal consumer information, there is proprietary business information that you would not want your competition to discover. Developing security standards now can help avoid mistakes in the future. The simplest security control is to password protect sensitive reports or analytical studies. This can often be accomplished via the reporting tool.
If you have a more comprehensive tool, you can get very specific with user security — down to which data elements a particular user can access. You should encrypt your data, especially if it is in the cloud. When sending information via email, especially outside of your company, a simple encryption program can secure data assets against many hackers.
6) The Digital Canterbury Tales: Part Six: You mean I can’t just Google it?: A tale of Young Professionals in the digital oilfield
The Digital Transformation of the Oil & Gas Industry can be a very exciting time, but how exciting, or how scary, depends on your point of view. In trying to think about all the different perspectives in our industry I had the idea to follow the model of the classic 14th Century tale, The Canterbury Tales by Geoffrey Chaucer. Chaucer’s famous tale included 24 different stories presented as part of a story-telling contest by a group of pilgrims as they travel together from London to Canterbury to visit the shrine of Saint Thomas Becket at Canterbury Cathedral. The prize for this contest is a free meal at the Tabard Inn at Southwark on their return.
In this series so far, I am only on tale number 6 and I am not quite sure what the prize is going to be but if the metric is number of views of each article then Amy the experienced manager is in the lead. You can vote for your favorite story just by reading the article and I will let LinkedIn keep score.
I have told the story of Melanie an IT technical domain expert, the story of two data managers, Gail and Cary, the story of Amy an experienced manager, the story of Ayush, the traditional domain physics expert, the CIO (Kim) and the data analytics lead (Grant). In part six, I want to look at the challenges that young professionals face as they join the industry workforce and face a traditional but changing industry. My young professional digital engineers are Jessica and Antonio.
The digital transformation of the Oil and Gas industry has its challenges. Gigantic oil companies have been around for decades and have become set in their ways and processes. Decades of success, punctuated by the occasional low commodity price cycle, have reinforced “best practices” and the thought of being “disrupted” by digital technology is not something executives really want to happen. Some of the existing practices and decision-making approaches are developed to avoid accidents that involve the highly volatile and flammable products they work with or to make sure that the company avoid mistakes that could cost them a lot of money or reputation points in the marketplace. This contrasts to Silicon Valley, on the other hand, is said to live by the mantra “move fast and break things.” You are not taught to break things in the oil and gas industry.
I had an interesting conversation recently (and my eyes opened) regarding the millennials that are entering the workforce, like Jessica and Antonio. Besides the impact of emerging digital technology from Silicon Valley, they will also have an impact on the Oil & Gas industry due to their low tolerance to long term projects and lack of desire to do the “work abroad” in less than desirable places (only nice vacation spots). The old advice of never turn down a transfer is a hard pill to swallow for young professionals with a lot of options.
Many young professionals have the keen idea of getting the big established company logo on the resume and then go off on their own to start their own start-up firms. I am not sure their pampering “helicopter” parents have prepared them for this workforce and their paradigms and perceptions; however, time will tell. Their demand for the latest technology and reported short attention spans with desire for the good life and meaningful work will impact the industry too, just not sure how.
Jessica and Antonio are the real deal, digital natives. They were born after the smart phone arrived and the switch from desktop computers to laptops and tablets was well underway. Their world outside of work is enables by their phones, social media and a very connected world. Why wouldn’t they want those same tools at work?
So does your company have a version of:
Facebook to find their network connections, to answer their questions, and to collaborate and share their work.
YouTube to go to for training and references when they want, not to schedule class room versions of training classes when the trainer wants.
Smartphone and tablet, preferably the latest version on the market. Forget your company PC, no one wants them anymore. This device will be more than a phone, it will be their life. They want to take IT with them, so they are ready to work whenever and wherever you need them to, but they want their life as well.
Twitter, because they want to stay in touch with their network wherever they are. Email is dead so don’t expect an answer to your voice mail or repeated attempts at sending them email, send a link or a text instead.
Skype or FaceTime, when we meet virtually, which will be most meetings, why can’t they see you on their screen?
Google for their ‘get my data button’.
Programming was for the IT department geeks in my generation. But ‘Coding boot camps’ have become an accepted source of talent for some of the world’s most prestigious tech companies. After two to four months of intensive work, participants have proved themselves to be job ready. Jessica and Antonio are already accomplished programmers in several languages in addition to their petroleum engineering degrees. Some languages were taught in school and others they picked up on their own from online open university courses. I read where Silicon Valley Tech giants are shifting away from degrees all together. They think competence, not college – Silicon Valley companies no longer require a degree but look for experience and demonstrated skills. Show me your code not your diploma.
In 2017, 80 percent of coding boot camp graduates found a job that used their skills, with an average salary of $70,698—well ahead of that of recent US college graduates ($49,785)—and there are similar efforts in Europe and Asia. At present, individuals typically pay for their own boot camps, but the model is successful enough that there could be room for public–private partnerships or for companies to adapt the concept to their own needs. (BPX in Denver pays for 20 employees a year to go thru a tailored version of the Galvanize coding boot camp and then go back to work on business related projects).
Amy, Jessica and Antonio’s supervisor, will have an interesting challenge on her hands with the two bright recruits. Amy has a lot to pass along to her young employees about how to be successful at their company but if she is clever, and we have already established that Amy is clever, they can teach her a lot as well about the new technology.
When I joined the industry, the thought was a long apprenticeship was the best way to teach me the things I needed to know before I was set loose to make my own decisions and spend the company’s money. I went from the classroom at Colorado School of Mines to the classrooms of Chevron. It is a different balance today. Jessica and Antonio will be expected to deliver results on a meaningful project from day one. And that is the way they want it. The Oil & Gas industry is having trouble (at least in North America and western Europe but maybe not in the rest of the world) attracting the best and brightest. Jessica and Antonio know they have options and they even have friends their age that have already started their own companies.
Will this be a perfect match for the talent challenge brought on by digital transformation or a rocky relationship where two cultures collide. Stay tuned. It is going to be an interesting journey.