Assuring That the Actions Taken Into Account the Current Science and State of the Art Quizlet

Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and predictive analytics.^[one] ^[2]

Referred to equally the "concluding frontier of analytic capabilities,"^[iii] prescriptive analytics entails the application of mathematical and computational sciences and suggests decision options to take reward of the results of descriptive and predictive analytics. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today.^[four] Descriptive analytics looks at past functioning and understands that performance by mining historical information to look for the reasons behind by success or failure. Nearly management reporting – such as sales, marketing, operations, and finance – uses this type of post-mortem assay.

Prescriptive Analytics extends beyond predictive analytics by specifying both the actions necessary to achieve predicted outcomes, and the interrelated effects of each determination

The next stage is predictive analytics. Predictive analytics answers the question what is likely to happen. This is when historical data is combined with rules, algorithms, and occasionally external information to determine the probable future upshot of an event or the likelihood of a situation occurring. The concluding stage is prescriptive analytics,^[5] which goes beyond predicting futurity outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.^[six]

Prescriptive analytics not only anticipates what will happen and when it will happen, but also why information technology volition happen. Farther, prescriptive analytics suggests decision options on how to take advantage of a time to come opportunity or mitigate a futurity risk and shows the implication of each decision pick. Prescriptive analytics can continually have in new data to re-predict and re-prescribe, thus automatically improving prediction accuracy and prescribing better decision options. Prescriptive analytics ingests hybrid data, a combination of structured (numbers, categories) and unstructured information (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to have advantage of this predicted future without compromising other priorities.^[7]

All iii phases of analytics can be performed through professional services or technology or a combination. In society to scale, prescriptive analytics technologies need to exist adaptive to accept into account the growing volume, velocity, and variety of data that most mission critical processes and their environments may produce.

I criticism of prescriptive analytics is that its distinction from predictive analytics is ill-divers and therefore ill-conceived.^[8]

The scientific disciplines that incorporate Prescriptive Analytics

History [edit]

Prescriptive analytics incorporates both structured and unstructured data, and uses a combination of avant-garde analytic techniques and disciplines to predict, prescribe, and adapt. While the term prescriptive analytics was first coined past IBM^[two] and later trademarked by Ayata,^[9] the underlying concepts have been around for hundreds of years. The technology behind prescriptive analytics synergistically combines hybrid information, business rules with mathematical models and computational models. The information inputs to prescriptive analytics may come up from multiple sources: internal, such equally within a corporation; and external, also known as ecology data. The data may be structured, which includes numbers and categories, besides as unstructured data, such as texts, images, sounds, and videos. Unstructured data differs from structured data in that its format varies widely and cannot be stored in traditional relational databases without significant endeavor at data transformation.^[10] More than lxxx% of the globe's information today is unstructured, according to IBM.

In addition to this variety of data types and growing information volume, incoming data can also evolve with respect to velocity, that is, more data being generated at a faster or a variable pace. Business rules define the business organization process and include objectives constraints, preferences, policies, best practices, and boundaries. Mathematical models and computational models are techniques derived from mathematical sciences, computer science and related disciplines such as applied statistics, machine learning, operations research, natural language processing, computer vision, pattern recognition, image processing, speech recognition, and signal processing. The right awarding of all these methods and the verification of their results implies the need for resources on a massive scale including homo, computational and temporal for every Prescriptive Analytic project. In club to spare the expense of dozens of people, high operation machines and weeks of work one must consider the reduction of resources and therefore a reduction in the accuracy or reliability of the outcome. The preferable route is a reduction that produces a probabilistic effect inside acceptable limits.^{[ citation needed ]}

Applications in Oil and Gas [edit]

Key Questions Prescriptive Analytics software answers for oil and gas producers

Energy is the largest manufacture in the world ($vi trillion in size). The processes and decisions related to oil and natural gas exploration, development and production generate large amounts of data. Many types of captured data are used to create models and images of the Earth'due south structure and layers v,000 - 35,000 feet below the surface and to describe activities around the wells themselves, such as depositional characteristics, machinery performance, oil menstruation rates, reservoir temperatures and pressures.^[11] Prescriptive analytics software can assist with both locating and producing hydrocarbons^[12] by taking in seismic data, well log information, product data, and other related data sets to prescribe specific recipes for how and where to drill, complete, and produce wells in guild to optimize recovery, minimize cost, and reduce environmental footprint.^[xiii]

Unconventional Resource Development [edit]

Examples of structured and unstructured data sets generated and past the oil and gas companies and their ecosystem of service providers that can be analyzed together using Prescriptive Analytics software

With the value of the end production determined by global commodity economics, the basis of contest for operators in upstream Eastward&P is the power to effectively deploy uppercase to locate and extract resources more than efficiently, effectively, predictably, and safely than their peers. In unconventional resource plays, operational efficiency and effectiveness is diminished by reservoir inconsistencies, and decision-making impaired by high degrees of doubt. These challenges manifest themselves in the course of depression recovery factors and wide functioning variations.

Prescriptive Analytics software can accurately predict production and prescribe optimal configurations of controllable drilling, completion, and product variables by modeling numerous internal and external variables simultaneously, regardless of source, structure, size, or format.^[fourteen] Prescriptive analytics software tin also provide determination options and show the impact of each decision option and so the operations managers can proactively take appropriate actions, on time, to guarantee future exploration and production operation, and maximize the economic value of assets at every point over the form of their serviceable lifetimes.^[xv]

Oilfield Equipment Maintenance [edit]

In the realm of oilfield equipment maintenance, Prescriptive Analytics can optimize configuration, anticipate and prevent unplanned downtime, optimize field scheduling, and improve maintenance planning.^[sixteen] According to General Electric, there are more than 130,000 electric submersible pumps (ESP's) installed globally, bookkeeping for 60% of the world's oil production.^[17] Prescriptive Analytics has been deployed to predict when and why an ESP volition fail, and recommend the necessary actions to prevent the failure.^[18]

In the area of Health, Rubber and Environment, prescriptive analytics can predict and preempt incidents that tin lead to reputational and financial loss for oil and gas companies.

Pricing [edit]

Pricing is another area of focus. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geopolitics, and weather atmospheric condition. Gas producers, pipeline transmission companies and utility companies take a keen interest in more than accurately predicting gas prices so that they tin can lock in favorable terms while hedging downside run a risk. Prescriptive analytics software tin accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and evidence the impact of each decision option.^[19]

Applications in maritime industry [edit]

Common Structural Rules for Bulk Carriers and Oil Tankers ( managed by IACS organisation ) intensively utilizes term "prescriptive requirements" equally one of two chief classes of checkable calculations by dedicated numerical tools and algorithms for verifying safe of send hull structure.

Applications in healthcare [edit]

Multiple factors are driving healthcare providers to dramatically improve business processes and operations as the United States healthcare industry embarks on the necessary migration from a largely fee-for service, volume-based arrangement to a fee-for-functioning, value-based system. Prescriptive analytics is playing a primal role to assist meliorate the operation in a number of areas involving various stakeholders: payers, providers and pharmaceutical companies.

Prescriptive analytics tin help providers amend effectiveness of their clinical intendance delivery to the population they manage and in the procedure attain better patient satisfaction and retention. Providers can do better population health direction by identifying advisable intervention models for risk stratified population combining data from the in-facility care episodes and home based telehealth.

Prescriptive analytics can also do good healthcare providers in their capacity planning past using analytics to leverage operational and usage data combined with data of external factors such equally economical data, population demographic trends and population health trends, to more accurately program for futurity majuscule investments such equally new facilities and equipment utilization besides as empathise the trade-offs between adding boosted beds and expanding an existing facility versus building a new one.^[20]

Prescriptive analytics can assistance pharmaceutical companies to expedite their drug development past identifying patient cohorts that are most suitable for the clinical trials worldwide - patients who are expected to exist compliant and will not driblet out of the trial due to complications. Analytics can tell companies how much time and money they tin save if they choose 1 patient cohort in a specific country vs. another.

In provider-payer negotiations, providers can amend their negotiating position with health insurers by developing a robust understanding of future service utilization. By accurately predicting utilization, providers can as well better classify personnel.

References [edit]

^ Evans, James R. & Lindner, Carl H. (March 2012). "Business organisation Analytics: The Next Frontier for Decision Sciences". Decision Line. 43 (ii).
^ ^a ^b http://www.analytics-mag.org/nov-december-2010/54-the-analytics-journeyingLustig, Irv, Dietrich, Brenda, Johnson, Christer, and Dziekan, Christopher (Nov–Dec 2010). "The Analytics Journey". Analytics. {{cite journal}}: CS1 maint: multiple names: authors list (link)
^ "Archived re-create". Archived from the original on 2016-04-02. Retrieved 2014-10-29 . {{cite web}}: CS1 maint: archived re-create as title (link)
^ Davenport, Tom (November 2012). "The 3 '..tives' of business organisation analytics; predictive, prescriptive and descriptive". CIO Enterprise Forum.
^ Haas, Peter J.; Maglio, Paul P.; Selinger, Patricia G.; Tan, Wang-Chie (2011). "Data is Dead…Without What-If Models". Proceedings of the VLDB Endowment. 4 (12): 1486–1489. doi:10.14778/3402755.3402802. S2CID 6239043.
^ Stewart, Thomas. R. & McMillan, Claude, Jr. (1987). "Descriptive and Prescriptive Models for Judgment and Decision Making: Implications for Knowledge Engineering science". NATO AS1 Senes, Adept Judgment and Good Systems. F35: 314–318. {{cite journal}}: CS1 maint: multiple names: authors list (link)
^ Riabacke, Mona; Danielson, Mats; Ekenberg, Honey (xxx Dec 2012). "State-of-the-Art Prescriptive Criteria Weight Elicitation". Advances in Determination Sciences. 2012: i–24. doi:10.1155/2012/276584.
^ Bill Vorhies (Nov 2014). "Prescriptive versus Predictive Analytics – A Distinction without a Difference?". Predictive Analytics Times.
^ "PRESCRIPTIVE ANALYTICS Trademark - Registration Number 4032907 - Series Number 85206495 :: Justia Trademarks".
^ Inmon, Bill; Nesavich, Anthony (2007). Tapping Into Unstructured Data. Prentice-Hall. ISBN978-0-13-236029-6.
^ Basu, Atanu (November 2012). "How Prescriptive Analytics Can Reshape Fracking in Oil and Gas Fields". Data-Informed.
^ Basu, Atanu (December 2013). "How Information Analytics Can Help Frackers Observe Oil". Datanami.
^ Mohan, Daniel (August 2014). "Machines Prescribing Recipes from 'Things,' World, and People". Oil & Gas Investor.
^ Basu, Mohan, Marshall, & McColpin (December 23, 2014). "The Journey to Designer Wells". Oil & Gas Investor. {{cite journal}}: CS1 maint: multiple names: authors listing (link)
^ Mohan, Daniel (September 2014). "Your Information Already Know What You Don't". E&P Mag.
^ Presley, Jennifer (July 1, 2013). "ESP for ESPs". Exploration & Production.
^ {http://www.ge-energy.com/products_and_services/products/electric_submersible_pumping_systems/}
^ Wheatley, Malcolm (May 29, 2013). "Undercover Analytics". DataInformed.
^ Watson, Michael (November 13, 2012). "Advanced Analytics in Supply Chain - What is it, and is it Meliorate than Non-Advanced Analytics?". SupplyChainDigest.
^ Foster, Roger (May 2012). "Big data and public wellness, part 2: Reducing Unwarranted Services". Government Wellness Information technology.

External links [edit]

INFORMS' bi-monthly, digital magazine on the analytics profession
Menon, Jai "Why Information Matters: Moving Beyond Prediction" IBM

caudillhationers.blogspot.com

Source: https://en.wikipedia.org/wiki/Prescriptive_analytics

Assuring That the Actions Taken Into Account the Current Science and State of the Art Quizlet

History [edit]

Applications in Oil and Gas [edit]

Unconventional Resource Development [edit]

Oilfield Equipment Maintenance [edit]

Pricing [edit]

Applications in maritime industry [edit]

Applications in healthcare [edit]

See also [edit]

References [edit]

Further reading [edit]

External links [edit]

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