No data scientist? No analytics platform? No problem.

2

“Analytics” and “data scientist” aren’t new terms, but they are trending buzzwords. The popularity of these concepts has created a false impression: Analytics are mysterious abstractions that can only be decoded if you have a white lab coat and an advanced degree in computer science.

The reality couldn’t be more different. It’s true that data scientists are statistical whiz kids who skillfully manipulate massive amounts of data and generate intelligence with data mining, machine learning and predictive modeling. But with the right set of tools, non-data scientists can also reap the benefits of advanced analytics. A streamlined, automated data integration process and robust visual exploration tools can make analytics the most powerful collaborative tool available to your organization.

No data scientist?

Despite the misconceptions, advanced analytics can be made easier and more accessible at every level of the enterprise.

  • The first step is to streamline the data integration process.
  • Next, engage visual exploration tools that enable even those with rudimentary data science skills to build, deploy and collaborate on data mining and model-building using machine learning and other advanced processes.
  • Then, operationalize your analytical efforts to add tangible value to your business, and encourage collaboration by deploying these solutions on cloud and mobile platforms.

Companies with constrained budgets or limited manpower can still make the most of advanced analytics by adopting a results-as-a-service approach, such as SAS Results. This affordable outsourcing arrangement enables companies to provide their data and a specific brief to analytics experts who help you generate data insights for a specific project or recurring report without taking on new, ongoing internal costs.

No analytics platform?

What if you could spend your time on analysis, not lost in your data?

To begin, we must consider the truth: An under-used bounty of raw, siloed data is filling up every nook and cranny of your firm’s servers and hard drives. It’s imperative to analyze this data to fully assess the risk inherent in your operations and quantify uncertainty in the metrics you use for decision-making.

Companies that adopt low-priced – but under-powered – visualization tools to sift the value out of this untamed data often find them lacking. What’s missing? Robust workflow tools. To be successful, the analyst must have drag-and-drop, right-click and run tasks that form a continuous workflow in a visual environment. This lends itself to speed of build, execution, transparency and repeatability.

Think of an oil and gas producer that wants to monitor and optimize its production by running analytics by well, by basin and by field. The process involves incorporating real-time data into history matching, adding geological context on the formations being developed and running algorithms to infer missing data. The result can be a vivid, actionable view of operating conditions and the operationally controllable elements of your production.

Powerful workflow tools are needed to capture and automate these processes. Without them, performing data analysis at industrial scale becomes a Sisyphean task: Repeating your work over and over again, every day, to get the answers you want.

No problem

Sisyphus

Automation is the answer to this dilemma. It’s not necessary to increase data storage or add more staff if you can capture and seamlessly automate your workflow. Automating these tasks sets up a repeatable and auditable process that can be scaled up and applied to thousands of assets simultaneously. This has two immediate advantages:

  • Automated workflows shift the gather/analyze balance. Research shows that many analysts spend the majority of their time preparing data. An automated workflow reduces that data management time significantly, maximizing the time available to analyze the data for the patterns, trends and outputs that enable better business decisions.
  • Automated workflows enhance optimization efforts. To optimize oil production or any other industrial process, you have to be able to understand and measure uncertainty as it relates to your processes. Automation provides the level of consistency needed to make that understanding relevant. By ensuring the same process runs every time, regardless which asset you are evaluating, you enable multi-asset optimization that brings a higher level of efficiency and reliability to your operations.

We frequently encounter this lack of workflow automation – a reliance on continual customization and repetitive, time-consuming data preparation – throughout the oil and gas value chain. It’s an understandable workaround, one that has grown out of a traditional IT approach to analysis and a comfort with tools that aren’t integrated. But analytics doesn’t have to be that difficult, or that time-consuming.

Visual exploration tools make analytics easier. Once data is properly prepared via automated workflow tools, visual exploration expands the capabilities of everyone in your organization to access the full set of integrated data and harvest analytical insights. Visual exploration encourages collaboration regardless of the participants’ data science expertise.

Experience your new possible:

  • Download our whitepaper to learn how to Big Data integration enhances your operations.
  • Visit the SAS Results website to see how our proactive approach can ensure your team spends their valuable time analyzing, not managing, your data.
Share

About Author

Ian Jones

Senior Strategist, Energy Risk Management

Ian Jones is Senior Strategist for SAS' energy commodity risk management practice. Prior to joining SAS in 2009, he served as editor of the industry trade journal The Risk Desk.

2 Comments

  1. John Kershaw

    I still worry about organisations believing that tools will solve everything - beware of statistical packages and software baring gifts. There is still a huge requirement to have the right mix of skills and roles in an organisation (data scientists, statisticians, even "data journalists" to better communicate and interpret insights

    • I agree John, but even more important is to have these skills and roles to be championed by a key exec or else all attempts to implement change will fail

Leave A Reply

Back to Top