Prescriptive analytics

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Prescriptive analytics is the third and final phase of business analytics (BA) which includes descriptive, predictive and prescriptive analytics.[1][2]

Prescriptive analytics automatically synthesizes big data, multiple disciplines of mathematical sciences and computational sciences, and business rules, to make predictions and then suggests decision options to take advantage of the predictions. The first stage of business analytics is descriptive analytics, which still accounts for the majority of all business analytics today.[3] Descriptive analytics answers the questions what happened and why did it happen. Descriptive analytics looks at past performance and understands that performance by mining historical data to look for the reasons behind past success or failure. Most management reporting - such as sales, marketing, operations, and finance - uses this type of post-mortem analysis.

The next phase is predictive analytics. Predictive analytics answers the question what will happen. This is when historical performance data is combined with rules, algorithms, and occasionally external data to determine the probable future outcome of an event or the likelihood of a situation occurring. The final phase is prescriptive analytics,[4] which goes beyond predicting future outcomes by also suggesting actions to benefit from the predictions and showing the implications of each decision option.[5]

Prescriptive analytics not only anticipates what will happen and when it will happen, but also why it will happen. Further, prescriptive analytics suggests decision options on how to take advantage of a future opportunity or mitigate a future risk and shows the implication of each decision option. Prescriptive analytics can continually take 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 data (videos, images, sounds, texts), and business rules to predict what lies ahead and to prescribe how to take advantage of this predicted future without compromising other priorities.[6]

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

History[edit]

Prescriptive analytics has been around since about 2003. The technology behind prescriptive analytics synergistically combines hybrid data, business rules with mathematical models and computational models. The data inputs to prescriptive analytics may come from multiple sources: internal, such as inside a corporation; and external, also known as environmental data. The data may be structured, which includes numbers and categories, as well 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 effort at data transformation.[7] More than 80% of the world's data today is unstructured, according to IBM. In addition to this variety of data types and growing data 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 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.

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 system to a fee-for-performance, value-based system. Prescriptive analytics is playing a key role to help improve the performance in a number of areas involving various stakeholders: payers, providers and pharmaceutical companies.

Prescriptive analytics can help providers improve effectiveness of their clinical care delivery to the population they manage and in the process achieve better patient satisfaction and retention. Providers can do better population health management by identifying appropriate intervention models for risk stratified population combining data from the in-facility care episodes and home based telehealth.

Prescriptive analytics can also benefit healthcare providers in their capacity planning by using analytics to leverage operational and usage data combined with data of external factors such as economic data, population demographic trends and population health trends, to more accurately plan for future capital investments such as new facilities and equipment utilization as well as understand the trade-offs between adding additional beds and expanding an existing facility versus building a new one.[8]

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

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

Applications in oil and gas[edit]

Energy is the largest industry in the world ($6 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’s structure and layers 5,000 - 35,000 feet below the surface and to describe activities around the wells themselves, such as depositional characteristics, machinery performance, oil flow rates, reservoir temperatures and pressures.[9] Prescriptive analytics software can help with both finding and producing oil and gas [10] . It can take in seismic data, well log data, production data, and other related data sets to prescribe where to drill, how to drill (to maximize production, and minimize cost and environmental impact).

In unconventional resource plays, Prescriptive Analytics software can accurately predict production and prescribe optimal configurations of controllable drilling, completion, and production variables by modeling numerous internal and external variables simultaneously. Prescriptive analytics software can also provide decision options and show the impact of each decision option so the operations managers can proactively take appropriate actions, on time, to guarantee future exploration and production performance. In the realm of oilfield equipment maintenance, Prescriptive Analytics can prevent unplanned downtime, optimize field scheduling, and improve maintenance planning.[11] In the area of Health, Safety, and Environment, prescriptive analytics can predict and preempt incidents that can lead to reputational and financial loss for oil and gas companies.

Pricing is another area of focus. Natural gas prices fluctuate dramatically depending upon supply, demand, econometrics, geopolitics, and weather conditions. Gas producers, pipeline transmission companies and utility firms have a keen interest in more accurately predicting gas prices so that they can lock in favorable terms while hedging downside risk. Prescriptive analytics software can accurately predict prices by modeling internal and external variables simultaneously and also provide decision options and show the impact of each decision option.[12]

See also[edit]

References[edit]

  1. ^ Evans,James R. and Lindner, Carl H. (March 2012). "Business Analytics: The Next Frontier for Decision Sciences". Decision Line 43 (2). 
  2. ^ Lustig,Irv, Dietric, Brenda, Johnson, Christer, and Dziekan, Christopher (Nov–Dec 2010). "The Analytics Journey". Analytics. 
  3. ^ Davenport,Tom (November 2012). "The three '..tives' of business analytics; predictive, prescriptive and descriptive". CIO Enterprise Forum. 
  4. ^ Haas, Peter J., Maglio, Paul P., Selinger, Patricia G., and Tan, Wang-Chie (2011). "Data is Dead…Without What-If Models". Proceedings of the VLDB Endowment 4 (12). 
  5. ^ Stewart, Thomas. R., and McMillan, Claude, Jr. (1987). "Descriptive and Prescriptive Models for Judgment and Decision Making: Implications for Knowledge Engineering". NATO AS1 Senes, Expert Judgment and Expert Systems, F35: 314–318. 
  6. ^ Riabacke, Mona, Danielson, Mats, and Ekenber, Love (2012). "State-of-the-Art Prescriptive Criteria Weight Elicitation". Advances in Decision Sciences. 
  7. ^ Inmon, Bill; Nesavich, Anthony (2007). Tapping Into Unstructured Data. Prentice-Hall. ISBN 978-0-13-236029-6. 
  8. ^ Foster, Roger (May 2012). "Big data and public health, part 2: Reducing Unwarranted Services". Government Health IT. 
  9. ^ Basu, Atanu (November 2012). "How Prescriptive Analytics Can Reshape Fracking in Oil and Gas Fields". Data-Informed. 
  10. ^ Basu, Atanu (December 2013). "How Data Analytics Can Help Frackers Find Oil". Datanami. 
  11. ^ Presley, Jennifer (July 1, 2013). "ESP for ESPs". Exploration & Production. 
  12. ^ Dr. Watson, Michael (November 2012). "Advanced Analytics in Supply Chain - What is it, and is it Better than Non-Advanced Analytics?". Supply Chain Digest. 

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