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Tc labs 3   What will predictive implementations look like
Tc labs 3   What will predictive implementations look like
Tc labs 3   What will predictive implementations look like
Tc labs 3   What will predictive implementations look like
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Tc labs 3 What will predictive implementations look like

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The future of Oil & Gas predictive analysis is heavily reliant upon efficient data capture from the source (wellhead, etc.) and the visualization of the resulting data federation.

The future of Oil & Gas predictive analysis is heavily reliant upon efficient data capture from the source (wellhead, etc.) and the visualization of the resulting data federation.

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  • 1. TCLABZ© TCLABZ 2013What will Predictive technologies LOOK like in Oil and Gas Extraction?If you are a production manager of fleets of physical assets, your Asset Dashboards could or should be apride-producing display of BOTHvisual-elegance and big data management horsepower. To keep techiesnobbery well in the back of the discussion line,Good Looks of a dashboard system is second place todata excellence in our view at TCLabs. We consult with energy firms (from extraction to midstreamprimarily) who have allowed paper based systems and spreadsheets to create repetitive work andinaccurate business management outcomes.We don’t have a lack of appreciation for how paper and spreadsheet systems provide value today, yetwe see improvements that most firms can now afford to upgrade the business performance of theirasset-grid, via anticipating visual analytics. New generations of analysts and seasoned veterans alike canmore easily master advanced analytics software and find business improvements much faster.We’ve noticed in our development practice that focusing first of ‘smart’ visual analytics, namely dataFilters that separate anomalies and poorly managed asset; takes our customers a long way towards thegoal of the most advanced, Predictive systems. Predictive algorithmic work has been pioneered by thefinancial asset trading markets and we believe the same sets of algorithms will be purposed much moreoften for Oil and Gas extraction procedures which are too complex for human capital and traditional BIsystems to maximize the growth of invested capital.Predictive Analytics is derived from a modular set of statistical techniques such as modeling, MachineLearning and others which analyze current and historical events to make predictions about futurebusiness or quality outcomes. Predictive Models exploit patterns found in historical and microseconddata to identify risks and opportunities. Models associate relationships among many factors and‘Entities’ to allow assessment of risk or profit-opportunities associated with a vast evaluation of asset-management details, ultimately focusing human effort on high-intensity actions for improvement
  • 2. TCLABZ© TCLABZ 2013TCLabz Predictive Insights for O&G TodayWhat are the keys to know today for this year’s seed-project you would like to make happen for yourasset management role from vast scale to tiny?We’ll start with the ‘Advanced’ nomenclature.Because of the volume of the data involved with a moving-asset grid, ‘smart’ visualization toolswill emerge. Visualization that is naturally predictive to the eye viewing millions of basictransaction flows as well as only hundreds of rows of information presented automatically whensmart-sensors are tuned to an immediate issue for analysis.1. A KEY today is to enhance your operation’s combination of legacy information systemswhile planning for the slick, next generation of tools ‘platformed’ on agile asset andknowledge management systems.2. Visualization must be interactive in your dashboard systems as a first go, no-go vendorselection3. Users can now explore scenarios with minimal training and huge time savings overstatic, Production Reporting or MS ExcelPredictive technologies will be a meta-analysis module on top of your visualization selectionsGeospecial is a visualization layer that won’t be as expensive as even six months ago, and is nowmust-have particularly for risk and cost managementMaximizing predictive business results can start today with founding your data layer andsoftware choices anticipating exceptional management dashboardsIn our experience developing and deploying analytics strategies and algorithmic softwares. The issue wemanage is not often a software platform selection or architecture, but how to get actionable intelligencefrom many forms of legacy data which effect given risks, costs and opportunities. Our first questions arealways:• Are we collecting and storing data with excellence? Accurate Time Stamps, Headers andEvents.• Today, what do employees read and chart to assist finding risks, costs and opportunities?Are you reading this imagining 5-day meetings pouring over spreadsheets and drinking cold coffee? Youshould be because that is what workgroups will do on your PM dime to develop Asset Optimizationplatforms.THE primary challenge to implement truly advanced predictive technology is not technology or vendorselection initially. The first order of business requires vision and a deep knowledge of highly-granulardata moving within a give group, firm and industry. The primarily challenge to brainstorm first is whatkind of problem will show the greatest return on an investment via predictive analytics. Where can theyapply predictive analytics and actually demonstrate? The best case result is a 5 minute demonstrationthat financial and technical specialists can both nod in agreement and envision deploying.
  • 3. TCLABZ© TCLABZ 2013Let’s assume that your company isn’t a Fortune 500 company and maybe you’ve struggled a bit withtechnology strategy and operations. Maybe you’re still struggling but you can’t wait another year for ITto complete the decade long SAP/Oracle/Cognos/Any ERP implementation that cost millions and has yetto show any benefit. Perhaps you’re not a math genius, not a finance person and not even really whatsome might consider tech-savvy. But, you know your business, you know your customers and you knowyour products. You also know you have to keep up with changes in your industry and you don’t want tobe left behind if Big Data is the next big thing. [And, I definitely think it will be, at least one of them.]You may be asking: where should I start?Our advice: make a map.Huh? Why would I start by making a map? Our company manufactures sophisticated enginecomponents. I need performance metrics, fancy algorithms and cutting-edge insights to drive strategyand profit. How is a simple map going to help me improve the bottom line? Sounds like a sillykindergarten activity with no possible ROI.Well, give me a chance to explain. Before you can turn some Nate Silver-like econometrics modelingguru or Physics PhD genius loose you need good data and some ideas about what specific problems youwant to try to address with analytics. Producing a map can be an excellent process for moving toward amore sophisticated Big Data program. So, how can making a map help start this process?Here are 6 benefits of making a map:1. Your company will be forced to take inventory of key data elements.2. Your IT team will be required to deliver data in a usable format.3. Any problems with customer data will become readily apparent in the geocoding process.4. Geographic representations of company data will reveal new patterns that spreadsheets may bedisguising.5. Producing a map will allow everyone to get involved, not just the same old digit heads.6. Seeing your company’s data on a map will generate new ideas.Most organizations stagnate in the process of identifying or collecting data before creating easy, basicvisual-awareness prototypes where internal contributors can point to and indicate their ‘pain’ is on themap. Geospacial consultants for example must develop an ear for this process and let the debates rollalong internally. Every question about the data and rendering on the map is a core concern of astakeholder deserving attention. Boiling all requests and specifications into a Top 3, KPI-dashboard isthen in range.‘But wait!’ Our clients say. ‘How do you ‘map’ output from multiple legacy systems, information lockedin (pricey) paper tracking and emailed spreadsheets where some real financial action is communicatedand lost immediately in the mesh of time and people. Our systems are everywhere’ they tell us, ‘Will we
  • 4. TCLABZ© TCLABZ 2013forever be paying for integration services for endless Dashboards of disparate data formats to get thegreat, visually predictive stuff for our engineers and financial decision makers?’That depends. Clearly automation of insights, at least managing the top KPI dashboards, is cost-relianton the deep foundations of the data coming in for analysis. Core asset databases must be 100%electronically collected in the field and paper must be abolished. Paper based data collection and Excelis not exactly the friend of world-class Dashboard software, yet there are MANY great firmsimplementing technologies to tap ‘arrays of spreadsheets’ tracking a vastly complex asset managementprocesses.Energy firms of all sizes are eager to develop flexible systems for big data insights. The combination ofLinguistic scoring, (workforce and customer chatter analysis) with statistical Asset Operations Data,creates predictive modeling across many functions of the global energy asset-grid. Subsets of thesecomponents can also work very well for workgroups with as few as 3 analysts in a starter analytical‘Sandbox’.

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