1.1.How prescriptive analytics influence on decision-making process?

Необходимо дать определение предписывающей аналитики и описание ее функционала.

Например:

Prescriptive analytics is the process of analyzing business data, with the goal of taking a specific course of

action. Prescriptive analytics allows users to prescribe a number of different possible actions and guide them

towards a solution.

Prescriptive analytics help businesses identify the best course of action so they achieve organizational goals.

While figuring out what you should do is a crucial aspect of any business, the value of prescriptive analytics is

often missed.

Prescriptive analytics is the third and final phase of business analytics, which also includes descriptive and

predictive analytics. Prescriptive analytics entails the application of mathematical and computational sciences

and suggests decision options to take advantage of the results of descriptive and predictive analytics.

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.

Prescriptive analytics provides organizations with recommendations around optimal actions to achieve business

objectives such as customer service, profits and operational efficiency. Prescriptive analytics solutions use

optimization technology to solve complex decisions with millions of decision variables, constraints and

tradeoffs. Organizations across industries are using prescriptive analytics for a range of use cases spanning

strategic planning, operational and tactical activities.

Необходимо проанализировать влияние предписывающей аналитики на этапы принятия решений, показать,

какие этапы могут быть замещены или улучшены с использованием технологий больших данных, показать

место предсказательной аналитики в общем процессе, который выглядит примерно так:

1. Identification of the purpose of the decision to address it

2. Information gathering

3. Principles for judging the alternatives

4. Brainstorm and analysis the different choices

5. Evaluation of alternatives

6. Select the best alternative

7. Execute the decision, create a plan for implementation.

8. Evaluate the results, evaluate the decision for effectiveness.

Необходимо показать, какие этапы принятия решений улучшаются при использовании предписывающей

аналитики. При этом должно быть показано отличие от технологий анализа данных.

Очевидно, что этапы 1 и 2 – это этапы, не связанные напрямую с предписывающей аналитикой.

Очевидно, что наибольшее влияние оказывается на создание альтернативных решений, их оценку, правила

выбора и план реализации, т.е. на этапы 3-8.

Prescriptive analytics can help organizations make decisions based on facts and probability-weighted

projections, rather than jump to under-informed conclusions based on instinct.

Prescriptive analytics makes decision-making effort less by gleaning granular and actionable insights from data

– users don’t have to go through and analyze the massive amounts of data. Equipped with AI and machine

learning, prescriptive analytics effectively harnesses unstructured data and helps decision-makers build what-if

scenarios.

Prescriptive analytics can assist leaders in making critical decisions by providing an unconventional roadmap

that not only identifies the optimal decisions, but also the impact of each decision.

Нужно показать возможные плюсы применения предписывающей аналитики. Например:

INDUSTRIAL USE CASES FOR PRESCRIPTIVE ANALYTICS

1.CPG and Retail

Optimize the assortment of products in a retail store

Optimally price items and services

Find the best mix of marketing methods (online, print, radio, etc.)

Measure trade promotion effectiveness and ROI and profitably optimize expenditure on promotions.

2.Transportation and shipping

Improve driver retention to reduce training costs

Eliminate unnecessary driving, flight, and sea transportation miles

Increase driver productivity by improving routes and eliminating wait times to load/unload

Increase speeds and reduce costs by optimizing distribution networks

Eliminate nearly all warehouse packing errors

3.Oil and Gas

Improve drilling completion rate by training machine learning models to recognize the most beneficial

places to set up field operations

Determine the best possible locations in a particular field to drill first

Optimize equipment configuration to eliminate downtime due to breakage and maintenance

Improve operational safety and eliminate potential environmental disasters

Establish the best possible pricing by predicting the rise and fall of fuel markets

4.Financial Services and Banking

Decrease transaction processing time

Lower transaction costs

Increase the total amount of possible transactions processed in a particular time period

Create better portfolios for financial investment

Optimize financial decisions like when to invest, how much to invest, etc.

Reduce investment risk

1.2.Data Analytics-as-a-Service: in which areas is it appropriate to apply such solutions? If you

know, give examples of existing software tools.

При ответе на вопрос нужно дать краткое описание, определение, акцентировав отличия от просто

анализа данных.

Data Analytics as a Service is the delivery of statistical analysis tools or information by an outside provider that

helps organizations understand and use insights gained from their large amounts of data in order to achieve a

competitive advantage.

What does Analytics as a Service (AaaS) mean?

Analytics as a service (AaaS) refers to the provision of analytics software and operations through web-delivered

technologies. These types of solutions offer businesses an alternative to developing internal hardware setups

just to perform business analytics.

In the example of analytics as a service, a provider might offer access to a remote analytics platform for a monthly

fee. This would allow a client to use that particular analytics software for as long as it is needed, and to stop

using it and stop paying for it at a future time.

Analytics as a service is becoming a valuable option for businesses because setting up analytics processes can be a

work-intensive process. Businesses that need to do more analytics may need more servers and other kinds of

hardware, and they may need more IT staff to implement and maintain these programs. If the business can use

analytics as a service instead, it may be able to bypass these new costs and new business process requirements.

Along with the appeal of complete outsourcing that analytics as a service provides, there is the option of going with

a hybrid system where businesses use what they have on hand for analytics and outsource other components

through the web. All of this equips the modern business with more choices and more precise solutions for

changing business needs in markets that work largely on the availability of big data.

Data Analytics as a Service (DAaaS) represents the approach to an extensible platform that can provide cloud-based

analytical capabilities over a variety of industries and use cases. From a functional perspective, the platform

covers the end-to-end capabilities of an analytical solution, from data acquisition to end-user visualization,

reporting and interaction. Beyond this traditional functionality, it extends the usual approach with innovative

concepts, like Analytical Apps and a related Analytical Appstore. In addition, the platform supports the needs

of the different users who interact with it, including those of the emerging ‘Data Scientist’ role.

Architecturally, and due to the intrinsic complexities of analytical processes, the implementation of DAaaS

represents an important set of challenges, as it is more similar to a flexible Platform as a Service (PaaS) solution

than a more fixed Software as a Service (SaaS) application. Aspects like the PaaS internal architecture, the

distinction between real-time vs. non real-time processing, the specific characteristics of the Analytic Services,

the needs for data storage and modeling, the delivery over hybrid cloud models and several others, make its

design a complex challenge.

WHY?

DAaaS (Data Analytics-as-a-Service) is an extensible platform that uses a cloud-based delivery model.

It provides a selection of tool that are available for Data Analytics that can be configured by the user to efficiently

process huge quantities of data. Customers will feed enterprise data into the platform, this data can be processed

by analytics applications which can provide business insight using analytical algorithms and machine learning.

KEY BENEFITS

Enables small to midsize organization to have the same capabilities as larger organizations

More cost effective to business as it prevents the need for costly upfront capital costs

Allows users to focus on exploring and analyzing data, a high-value activity.

THE SOLUTION

Each business will have a different data requirements, but by adopting a DAaaS solution it will provide a platform

to allow you to ingest diverse data for both unstructured and traditional data sources, extending the analytical

capability of your business using a subscription payment model.

The platform can scale as the business grows. Empowering users to make more productive and profitable decisions.

Далее показать основные сегменты использования:

Financial analytics, risk analytics, marketing analytics, web analytics, supply chain analytics, security

analytics, IT operations analytics and others (HR analytics, legal analytics and others)

Если кто-то приведет оценки рынка таких сервисов – замечательно!

Основные вендоры:

Main vendors in the global AaaS market include Alteryx, Oracle, MicroStrategy, SAS, Atos, IBM, Qlik,

Microsoft, Tableau, Salesforce, SAP.

Привести примеры конкретных продуктов, например:

Microsoft Power BI, Microsoft Azure Stream Analytics, AWS Analytics, IBM Watson Analytics, Tableau

Online, Microstrategy Cloud, SAP Lumira Cloud, TIBCO Spotfire Cloud, Zoho Analytics, Domo, etc.

Привести примеры использования DAaaS. Это тоже большой плюс. Например:

Potential Business Use Cases. Data Analytics as a Service, as a general analytic solution, has potential use cases in

very different vertical sectors.

In the Oil & Gas sector, companies could deploy predictive maintenance solutions for device fleets in

remote installations, without deploying very complex solutions in-house. The solution could be rented for

short-term specific analysis.

In the Electrical Utilities sector, DAaaS is the basis of a specific solution to detect Non-Technical Losses,

which cover among others, fraud detection. The customer can upload SmartMeter information into the

system where it is processed by specific analytical services created and configured by experts in this kind

of business analysis.

In SmartCities solution, the DAaaS service provides analytic capabilities for the very different data sources

that are provided by the city, like the sensor networks deployed in the city. As many cities are under big

cost-reduction pressure, DAaaS could provide a very cost effective solution.

In Retail, a DAaaS model can be used for campaign management and customer behavior. This could include

the customer internet activities and also activity in physical stores via the customers’ mobile devices.

In Manufacturing, DAaaS can use the ever growing data coming from connected fabrication machines and

when matched with demand it can allow optimal production with minimizing scrap and redundancies.

Задание 2.2

Введём функцию Q(x) – вероятность того, что информация, попавшая в выбранный наугад узел,

достигнет лишь конечное число узлов, Q(x) = 1 – P(x).

Из каждого узла выходит q независимых каналов, по которым распространяется информация.

Найдём вероятность того, что один такой канал прерывается на некотором этапе. Это может быть

результатом одного из двух несовместимых событий:

a) первая связь этого канала оказалась разорванной,

b) первая связь оказалась целой, но узел, в который она ведёт, способен передать

информацию лишь конечному числу других узлов.

Вероятность события (a) равна 1 – x, а вероятность события (b) равна xQ(x). Вероятности

несовместимых событий можно складывать. Поэтому вероятность того, что прервётся один

канал, равна 1 – x + xQ(x). Так как все каналы независимы, то вероятность того, что все они

прервутся, равна (1 – x + xQ(x))q.

Отсюда получаем уравнение для Q(x):

Q(x) = (1 – x + xQ(x))q.

Перепишем его через P(x) = 1 – Q(x):

P(x) = 1 – (1 – x + x (1 – P(x))) q = 1 – (1 – x P(x)) q, или

(1 – x P(x)) q + P(x) – 1 = 0.

Можно заметить, что одним из решений этого уравнения является P(x) = 0 для всех x, а при x = 1

решением является P(1) = 1. Однако, при q > 1 уравнение для P(x) нелинейное и имеет другие

решения.

В общем виде:

𝑃(𝑥) =

𝑥 − 1𝑞 2𝑞2

𝑞 − 1 .

Задание 2.3

Прежде всего требовалось представить формальное описание грамматики, например:

Формально грамматика G определяется как четвёрка G (VT, VN, P, S), где:

VT - множество терминальных символов, или алфавит терминальных символов;

VN - множество нетерминальных символов, или алфавит нетерминальных символов;

P - множество правил (продукций) грамматики вида α → β, где α ϵ (VN ∪ VT)+, β ϵ (VN

∪ VT)*;

S - целевой (начальный) символ грамматики, S ϵ VN.

Класс грамматики в задании не определён, соответственно могут быть представлены различные

решения, удовлетворяющие заданным условиям. Ниже представлен пример записи одного из

самых простых правильных вариантов:

G ({a,b,c}, {T}, P, T)

P:

Т abbcccabcT

Задание 2.4

Обозначим p вероятность выхода накопителя за 10000 часов, p = 0.006.

Тогда вероятность выхода накопителя за 20000 часов можно оценить, как p = 2·0.006 = 0.012.

(Более точный расчёт: p + p (1 – p) ≈ 0,01196)

Искомые вероятности событий можно посчитать по формуле Бернулли 𝑃𝑛(𝑘) = 𝐶𝑛𝑘𝑝𝑘𝑞𝑛−𝑘, где

Cnk — число сочетаний, n = 10, p = 0,012, q = 1 – p = 0,988, k = 0, 1, 2, 3.

𝑃10(0) = 𝐶100 𝑝0𝑞10 =

10!

0! 10!∙ 0,0120 ∙ 0,98810 ≈ 0.8863 = 88,63%

𝑃10(1) = 𝐶101 𝑝1𝑞9 =

10!

1! 9!∙ 0,0121 ∙ 0,9889 ≈ 10 ∙ 0,012 ∙ 0,897 ≈ 0,1076 = 10,76%

𝑃10(2) = 𝐶102 𝑝2𝑞8 =

10!

2! 8!∙ 0,0122 ∙ 0,9888 ≈ 45 ∙ 0,000144 ∙ 0,9079 ≈ 0,00588 = 0,59%

𝑃10(3) = 𝐶103 𝑝3𝑞7 =

10!

3! 7!∙ 0,0123 ∙ 0,9887 ≈ 120 ∙ 0,000001728 ∙ 0,91896 ≈ 0,00019 = 0,02%

Однако, учитывая, что вероятность р мала, а число n велико (n·p = 0,12 < 10) можно сделать

вывод, что случайная величина распределена по Пуассоновскому распределению и в данном

расчёте можно использовать приближённую формулу Пуассона: 𝑃𝑛(𝑘) ≈𝜆𝑘

𝑘!𝑒−𝜆, где λ = n·p =

0,12 (среднее число появления событий в одинаковых независимых испытаниях), e ≈ 2,71828.

P10(0) = e-λ = e-0,12 ≈ 0,8869 = 88,69%

P10(1) = λe-λ = 0,12e-0,12 ≈ 0,1064 = 10,64%

𝑃10(2) =𝜆2

2!𝑒−𝜆 =

0,122

1∙2𝑒−0,12 ≈ 0,00639 = 0,63%

𝑃10(3) =𝜆3

3!𝑒−𝜆 =

0,123

1 ∙ 2 ∙ 3𝑒−0,12 ≈ 0,000255 = 0,03%

Задание 2.5

Вероятность любой последовательности из трёх фиксированных чисел равна 1

6∙1

6∙1

6=

1

216.

Число разных последовательностей, удовлетворяющих поставленным условиям для чисел 1, 2,

3 равно 6 (123; 213; 321; 231; 132; 312), а для чисел 1, 2, 2 равно 3 (122; 212; 221).

Вероятность того, что осуществляется одна из возможных последовательностей, равна сумме

вероятностей.

Следовательно, в первом случае искомая вероятность равна 6 ∙1

216=

1

36, а во втором случае

3 ∙1

216=

1

72.