[Jim Crompton](https://www.linkedin.com/in/ACoAAAEpmjgBzbtCiH1ojzyCmWadYRf3EbkqAQc/?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3Bgz3HmErwTZq%2BEaUUIGTE9A%3D%3D{:target=”_blank”}{:rel=”noopener noreferrer”} is back to discuss Lauren becoming a citizen data scientist and how she overcomes the various challenges in her path by utilizing the power of a platform.
Let’s go back to the story of Lauren the young production engineer. Lauren wants to do more with data. Her current job requires her to focus on rig work (well workovers) and pumping schedules (artificial lift). Today the tools she has access to are limited to trends and alarms. Lauren has learned to use Excel, Spotfire and a few other reporting tools. She is not a strong programmer, but that doesn’t mean she doesn’t aspire to be a great analyst someday.
Lauren doesn’t necessarily want to become a programmer (in Python or R) as a full-time job, or a full-fledged data scientist (dealing with advanced machine learning and artificial intelligence techniques). She does however want to grow her analytics skill set beyond Excel macros and Power B,I or Spotfire data visualization tools. Not only does Lauren want to be a better production engineer and help her asset team meet their production and profit objectives – she also wants the opportunity to enhance her career. Is there something else for her to aspire to?
Lauren’s company is motivated to grow the “digital literacy” of their workforce. They want to close the digital talent gap by improving the awareness and sophistication of all professionals, especially with an aim to grow their “citizen data scientist” population. Lauren is a perfect choice to become a citizen data scientist.
Gartner Group defines a “citizen data scientist” as a person who creates or generates models that leverage predictive or prescriptive analytics, but whose primary job function is outside the field of statistics and analytics (in Lauren’s case, production engineering). The person is not typically a member of an analytics team (Lauren is a member of a field asset team) and does not necessarily have a job description that lists analytics as her primary role (Lauren’s primary role is to maintain production from her field).
A citizen data scientist shares some characteristics with any good data scientist (or good production engineer for that matter) including: significant domain expertise, familiarity with core business issues, clear aptitude for quantitative work and a decent understanding or the IT and data requirements or analytics. So Lauren has the basic qualifications for the program.
Lauren’s company has found out that developing strong citizen data scientists (like Jason) requires more than just developing technical skills. Some of the most challenging aspects of predictive model development are those that require creativity and interpersonal skills. Communication and collaboration are critical to the work of the citizen data scientists, who often play the role of moderator / translator / coordinator between the highly technical data scientists and the engineering and field stakeholders.
Building trust in the model results and recommendations often involves trying to explain how the model reached that conclusion. The model either has to be trusted or has to explain how it came to the conclusions. The citizen data scientist is right in the middle of that conversation. These skills cannot be taught in a digital camp or an accelerated coding course, or a hackathon. They are part of the person’s character and Lauren excels in these attributes.
The good news is that there are a lot of places to go to increase Lauren’s digital literacy. The bad news is that there are a lot of places to go and it is difficult to discover the best ones. Formal training can be a course, boot camp or tool training program. Lauren could also decide to learn on her own time with free study courses offered by Coursera, DataCamp, edX, Udacity and Udemy as well as numerous tutorials available on YouTube. The free study approach is the easiest one to start but is more difficult to track progress to tell if Lauren is learning what her company wants her to. Lauren is very motivated and that is a good thing as success on these online courses and webinars rest entirely on the discipline and time management skills of the individual. Many courses are started but never finished. Many ideas come up in brainstorming sessions but few make it to execution. Lauren needs to work with her supervisor and with the head of the analytics center of excellence to develop clear objectives and milestones in order to get ahead.
There is a new piece of technology that can help Lauren. That new technology is called a platform. But what is a digital platform? The Enterprisers Project defines a digital platform as follows: “Multiple components comprise a digital platform (typically a data-ingestion engine, a machine-learning transactional engine to perform tasks or rules-based activities, an analytical engine, and increasingly, an AI engine, APIs, or tools that allow digital platforms to talk to other software, and tools monitoring regulatory compliance). These components must be aligned and integrated to create better experiences for users. Digital platforms enable a data-driven world rather than a process-driven world. The digital platform handles an end-to-end business process necessary to achieve the improved experience for customers, employees, and partners. Digital platforms cut across traditional organizational structures, silos, policies, and technology investments to enable the new operating model. They force a different organization, a different talent model, a different mindset, and a different set of policies and processes.”
Some of these platforms are very complex and often expensive, but there are simpler versions that Lauren can easily master. Platforms have been developed for many purposes, so Lauren needs to find one that was developed with an industrial work flow in mind. An example of one of these is JourneyApps.
Lauren is looking for a solution that is field mobile-ready and integrates with all the data sources she needs. The platform has to be secure and it has to work with her company’s IT environment. It has to help Lauren with most of the coding of new applications so she can focus on the business challenge and new work processes they need. She needs to have support when she gets lost or is headed down a blind (digital) alley (yes, they exist). The right platform for her must be flexible and allow for rapid response to changing field conditions and for changing ideas from Lauren and her teammates. If her team comes up with a great idea, her company would like to deploy that innovation to other similar asset teams — so scalability is another key ingredient.
It looks like one of the quickest ways for Lauren to join the ranks of the new Citizen Data Scientist corps in her company is to find the right platform for her to launch her new career. Excel spreadsheets got her so far, but she needs more. Finding the right data wherever it is hiding, linking together the components of corporate work flows, and adding new insights and ways of working all on a tight time schedule is the solution Lauren is looking for. Solutions are out there. All Lauren needs now is a little training, permission from IT to get access to the data, finding the right platform for her, and the OK from her supervisor to start innovating. Life for Lauren and her asset team is looking up.
(reference: Leading Upskilling initiatives in Data Science and Machine Learning, Gartner Group, 19 July 2019 by Peter Krensky)
