#4句话一篇文-bigdata analysitc for oil & gas industrial

this is a note of Management: Tapping the Value From Big Data Analytics by Amit Mehta, form Moblize
Moblize is a Data Analytics company in Oil & Gas, they build up a oftware platform named Enterprise.

4 sentences 1 paper

1: Many executicve believes big data analytics is a corporate priorities under such market conditions
2: Data analysis facing harsh realities,
3: there are two ways to find out :Workflow-Based Analytics and Optimization-Based Analytics
4: **Conclusion: **Many people belives data is a vital commodity , but now , we still need to answwer one questions “How do we apply the big data platform quickly to generate value and enable the ability to find and analyze information to make better decisions and insights at a reasonable investment?”

1 Many executicve believes big data analytics is a corporate priorities under such market conditionsCurrent market conditions needs efficient in exploration and production .

A recent GE/Accenture report shows that 81% of senior executives believe that big data analytics is one of the top three corporate priorities for the oil and gas industry through 2018. Decision makers are convinced that if other industries such as airlines and consumer Internet players such as Amazon and Expedia can leverage big data to drive efficiency and growth, the same should and can apply to the oil and gas industry.

If their assumptions are correct, leveraging hidden insights from mining data can help enterprise users make better, smarter decisions and reduce operational costs.

2 data analysis facing harsh realities, list 3 below

NO. 1 big data being uncharted territory for IT guys. 接触度不高 Big data being uncharted territory for information technology (IT) and a company’s business side. Further complicating the data analytics issue,

**NO.2 most IT organizations are traditionally more familiar with process automation **原始的方法水土不服。projects where business needs are known and stable. In contrast, data needs are context-dependent, dynamic, and may be unarticulated or even unknown sometimes. Solving this challenge requires anthropological skills that are in short supply in today’s IT world. Unfortunately, traditional requirements gathering fails when assessing data needs since the needs are fast-changing and diverse. Additionally, today’s machine data quality (especially on historical data) lacks accuracy, precision, completeness, and consistency for real-time analytics. As a practical matter, less than 50% of today’s enterprise users find information from corporate sources to be in a usable format. Also,

NO. 3 IT does not have a sufficiently deep understanding of how, when, and why information will be used by specific user segments. At the same time, enterprise users do not fully trust data from others or their functions and current tools in the organization todayIT行业和工业行业的人配合度不高，IT行业的人难以理解工业实际应用情况，而行业从业人员不相信外行业的人能提供建设性的意见.

Realistically, the time is now for data analytics champions within oil and gas companies to consider adopting radical thinking while practicing “lessons learned” and avoiding faulty actions from the past.

3 Two ways to find way for big data analysis

No. 1 Workflow-Based Analytics
These analytics are targeted toward answering the question: “How do we make an enterprise user’s work life better as consumer products do in people’s personal lives?” It involves developing an understanding of their daily pain points, segmenting their information usage patterns, and their stance toward technology adoption (e.g., visualization, delivery of business insight expectations). This differs from the traditional approach toward deep customer intimacy, i.e., gathering user requirements in RFP and ensuring that platform providers can satisfy them.

For successful adoption of this approach, consider the following:

1. Decision-based questions—Identify the universe of decisions that enterprise users are required to make daily.
2. Data architecture—Enable flexible, on-the-fly analysis capabilities through state-of-the-art architecture organized around key daily decision questions.
3. Contextualized information access—Provide enterprise users with access to information organized to address their top daily business questions.
4. Data quality transparency—Provide transparency into cleaning, filtering, and assembling all data sources to help the enterprise user gain trust in the data that will be used for decision making.

**No. 2 Optimization-Based Analytics **
In contrast with workflow-based analytics, optimization-based analytics are targeted toward answering the question whether reservoirs and downhole tools can be optimized to preempt failures and ensure that timely actions can be taken beforehand.

Timelines to realize value from this approach are relatively longer than the previous approach for some interesting reasons:

1. It requires a lot of heavy lifting to map/configure/assemble the data from disparate sources, and additionally the disengagement of actual operational enterprise users, primarily the central group team, is involved.2) Since it is focused on solving very complex problems, the volumes and types of disparate data requirements to create optimization algorithms are cumbersome because legacy data lakes are fraught with bad quality data.3) The designed solution may solve problems in a region/geography but is usually not scalable and repeatable easily to others (due to complexity of reservoirs, formations, and inconsistency of standardization of downhole tools).4) The complexity of models requires a team of experts to vet the results 24/7, which is a huge upfront investment, not to mention change management and new processes introduction that are never easy to get implemented and adopted in the enterprises. Underscoring what management faces, an enterprise user survey revealed high dissatisfaction within the enterprise user community today around current IT. They voice the opinion that solutions being piloted are barely meeting their needs, complex to use, and require extensive heavy lifting, i.e., requiring business experts from vendor teams to extract value from them. One senior executive at an oil and gas enterprise said: “If you give a Lamborghini to a 12-year-old, will he have a clue how to get high performance?” He expected a negative response.

**Conclusion****

For entirely too many years, oil and gas companies have possessed a virtual gold mine, That vital commodity is data and its value is now being viewed in a new “bankable” perspective through the power of big data analytics.

No matter which approach oil and gas management takes, the crux boils down to: “How do we apply the big data platform quickly to generate value and enable the ability to find and analyze information to make better decisions and insights at a reasonable investment?”