Decision intelligence что это

What Is Decision Intelligence?

Decision-making intelligence uses AI and compiled data to improve a business’s decision-making processes. The data and predictive models let you make quicker and more accurate business decisions. Instead of making choices based on gut feelings, decision intelligence allows you to optimize plans through valuable insights.

Gartner, an information technology company, solidified the definition of decision intelligence. The Gartner decision intelligence definition describes it as multiple decision-making processes coming together. Overall, decision intelligence helps companies make choices more effectively, accurately and quickly.

Decision intelligence consists of multiple components, including:

Artificial intelligence
Contextual intelligence
Automation

These factors combine to create actionable business recommendations. Decision intelligence can analyze past decisions and predict how new choices might affect the company in the long term. It collects holistic data across all the business domains within a company. Because the methods and approaches streamlines data analysis, decision-making also becomes less expensive for businesses.

Many decision intelligence platforms also use feedback loops for continuous improvement. The platform compares the original recommendation to the company’s ultimate decision. After time has passed and companies can view the impact of their choice, the loop analyzes the effectiveness of their recommendations. It passes on feedback to the rest of the systems so that it can improve future decisions (thru reverse ETL approaches, information gathered here could also help with feeding back into the business domains to further enrich information during intake). In other words, decision intelligence helps you continuously adapt decision models and processes for company improvement.

Why Companies Are Harnessing the Power of Decision Intelligence

With the hundreds of decisions that businesses face annually, investing in strategies to ensure financial success is crucial.

Decision intelligence continues to grow in popularity among businesses due to its effectiveness. With its implementation, you can improve your organization’s outcomes while also providing richer experience to your consumers. It’s a cost-effective and precise strategy for decision-making improvement.

Some of the other benefits of decision intelligence include:

Speed: One of the biggest limitations businesses face is time constraints. The pressure to make decisions in a limited amount of time can cause companies to make rash or reckless choices. Decision intelligence quickens the process by organizing mass amounts of data and providing recommendations. Instead of sorting through individual data, these platforms combine and analyze it for you, letting your company make informed decisions in a much shorter time.
Accuracy: Emotional or preferential biases can often cloud decisions. We often let past experiences and emotions impact our decision-making processes. If these biases affect your choices, you might make inaccurate assumptions about the best business decisions. Decision intelligence platforms examine current trends and business statistics objectively. They can recommend decisions without being affected by bias.
Accessibility: Decision intelligence platforms can combine and distill business analytics. Raw numerical data and business trend analyses are often complex and unclear. Misinterpretation of data could lead to incorrect decisions. Decision intelligence breaks down business data into accessible and easily understandable reports. The better you understand business trends, the more educated business decisions you can make.

How Do Decision Models Work?

Companies often use decision-making models to simplify selection processes and help every team member feel included. These models provide helpful steps and guidelines for making decisions. Decision intelligence incorporates each of these decision models and uses them to optimize workflow and company choices. For example, common decision models include:

Intuitive decisions: These decision-making processes are more subjective than other options. They usually rely on past experiences rather than current data trends and have less structure than other plans.
Rational decisions: Rational decision-making is one of the most commonly used types. In this option, people identify their problem, find potential solutions, analyze their options and finally make a choice.
Bounded rationality decisions: These decisions rely on finding a sufficient answer rather than the best possible one. In other words, you understand the limitations surrounding your decision and come to a compromise rather than a perfect conclusion.
Creative decisions: You make a creative decision when you create an entirely new and innovative answer to a problem.

However, these four decision-making models each have limitations due to biases and other external factors. Decision intelligence platforms allow you to look objectively at the entire situation and compare your decision against current company data.

Instead of typical decision models, decision intelligence models use a five-step sequence:

Observability: First, decision intelligence platforms gather relevant information from many different business sources. From historical product trends to internal financial transactions, the platform takes all of this data and compiles it into one place.
Investigation: Once the system collects your data, it begins to analyze it. It strives to understand the entire situation and begins generating possible solutions.
Modeling: Next, the decision intelligence program will act out potential scenarios to see their impacts on your business. They can predict how this decision will lead to other events or choices in the future.
Contextualization: The model will ensure the potential solutions are fit for your current business context.
Execution: Finally, the decision intelligence platform will arrive at a recommended decision for your business, along with an in-depth analysis of how it will impact your business in the future.

Using decision intelligence instead of human-based decision models reduces the chance of biases hindering a correct decision.

Who Can Benefit From Decision Intelligence?

A wide variety of industries implement decision intelligence to bring upon process efficiencies and cut back on decision cycle times. A few industries to list —

Banking and finance: Finance professionals can use decision intelligence to analyze customer behavior. The platform can help them anticipate upcoming consumer trends and adapt their offerings to meet the expected demand.
Energy sector: Decision intelligence helps those in the energy sector manage resources more efficiently. The software can even anticipate energy usage in the upcoming months.
Healthcare: Many healthcare systems use data analysis to improve their treatment methods. Decision intelligence can assist with predicting medical outcomes or making future treatment decisions.
Retail: Decision intelligence is highly beneficial in the retail sector. Businesses can analyze sales trends and adapt future product plans to meet demand. The software can also help with pricing decisions by examining current price ranges across the market. When you plan products more accurately, you can increase revenue and customer satisfaction.

Applications of Decision Intelligence

Depending on your company’s needs, you can use decision intelligence in many aspects of the production process. Here are some examples:

Stock management: Managing inventory is an essential task across industries. Retailers can use decision intelligence to optimize pricing and inventory strategies. Regularly monitoring inventory levels helps with overall profitability.
Marketing techniques: All businesses work hard to create a brand and raise customer awareness. Establishing a strong brand identity involves marketing across many platforms, from social media to promotional ads. Decision intelligence helps you identify your top campaign strategies and offers recommendations for how to adapt future ads.
Hiring processes: Onboarding new employees is often a time-consuming and complex task. Decision intelligence simplifies the process by examining potential candidates’ past successes. Supervisors can focus on applicants that are most likely to succeed in the company, saving time in the long term.

How to Get Started With Decision Intelligence

Integrating decision intelligence is a beneficial choice that offers companies numerous advantages, from quicker decision-making to deeper analysis. Decision intelligence engineering can seem complex to those unfamiliar with the process. However, companies interested in decision intelligence will find it easier to get started if they follow the tips below.

1. Make Sure Data Ecosystem Is Already in Place

For a decision intelligence platform to work efficiently, it needs to integrate into an existing data architecture.

A data lakehouse allows for efficient data management practices that allows companies to access and update existing data in one place. Using AI and other automation methods helps them manage company information. The platform ingests current data and use AI to understand company data. By centralizing your data, you can uncover insights for efficiency improvement.

2. Have a Specific Focus in Place Beforehand

Try to have a specific plan for your implementation. With all the strategies that decision intelligence can offer, it can be overwhelming to try everything at once. It’s best to take things one step at a time. A precise focus keeps you from overwhelming your team — you can introduce the concept in detail and ensure your team’s understanding. Since decision intelligence is constantly changing to accommodate your needs better, you can take important updates and changes one at a time.

It’s often best to start with the issue that first made you consider adopting decision intelligence. Many companies begin with processes that are well-defined with many examples. That way, you can gauge how well you’ve introduced the concept. For example, if you want to improve product pricing, you can start with a project focusing on that component. Solving a single project first allows you to try the implementation process before taking on more extensive implementations later.

3. Invest in Good AI and Make Sure It Works

Artificial intelligence in decision-making is fundamental for the technology to work properly. AI is a central component of decision intelligence, as it needs to use the output from your AI systems to function correctly. It’s essential to invest in high-quality AI systems. Many companies have AI learning machine models that match their specific goals.

Whether you plan on using your current setup or investing in new software, be sure to use tested and recommended types. Additionally, ensure that your team members are adequately trained in working with your AI systems. The benefits of a decision intelligence platform depend on being able to interpret the AI readings. Having trained employees for AI helps you maximize the benefits of decision intelligence.

4. Utilize New Data and Avoid Biased Data

One of the advantages of decision intelligence is its ability to give unbiased recommendations. If you use old or biased data while working with decision intelligence, that inaccuracy might affect your decisions. Data analytics relies on precise measurements and your company needs accuracy to improve future products.

One way to ensure you have new data is by using data reporting and analytics software. Platforms like RightData’s Analytics Hub can ensure you make strong decisions that help you meet your business goals. The Analytics Hub provides in-depth reports on company data year-round. It allows you to track customer behavior, seasonal trends and other crucial aspects of business maintenance. You can make the best decisions for your company with timely and precise data.

Harness the Power of Decision Intelligence With Dextrus From RightData

At RightData, we understand the importance of decision intelligence and offer various software product development solutions. One of our highlights is our Dextrus platform, which specializes in centralizing and analyzing company data. Dextrus lets you integrate decision intelligence software into your company easily.

Some of Dextrus’s features include:

Data preparation: Our software easily combines and prepares your data for viewing. Easy access and data analysis let you use your data more effectively. The more accessible your information is, the more informed your decisions will be.
Data insight: Dextrus’s “DB Explorer” component allows you to view in-depth insights into your company’s data. You can save these datasets and use them for future planning, wrangling and modeling.
Data analysis and reports: With embedded analytics, you can visualize your company’s information while compiling it. Detailed analysis reports help you make well-informed decisions for your company’s marketing, products and more.

Our Dextrus platform uses a comprehensive approach to sort and analyze your company statistics. In turn, you can make strong choices about future product lines, hiring decisions or whatever else you require.

Contact RightData Today

Decision intelligence continues to grow in popularity for companies across the globe, as making well-informed choices is crucial for company health and future profits. Installing a decision intelligence platform could be just what your company needs. You can process massive amounts of data with sophisticated tools, getting deeper insight into your internal processes.

RightData strives to empower organizations to accelerate data transformation. Our self-service and accessible tools allow companies to transform existing data practices. If you’re seeking a decision intelligence platform, consider RightData’s Dextrus. With Dextrus, you can use a cloud-based platform to enhance your data methods.

To get started with Dextrus, book a free demo today. You can also feel free to contact us with any questions.

Decision Intelligence: Using Data to Drive Better Outcomes

Are you looking to enhance decision-making processes in your organization? Decision intelligence can help you achieve this goal by providing a structured way of combining data, analytics, and technology to make better decisions. Not only that, but it also helps identify new opportunities for growth and transformation in the most cost-effective manner possible.

In this post, we will discuss what decision intelligence is all about, the key benefits it can provide, how to go about implementing it into your own company, and successful real-life examples of its usage. By familiarizing yourself with these topics, you will be able to take advantage of the tremendous power offered by decision intelligence for unlocking value from within your business ecosystem.

What is decision intelligence?

Decision intelligence is a process that involves the use of data, analytics, and artificial intelligence to optimize decision-making. It encompasses a range of techniques including machine learning, natural language processing, and predictive analytics. The goal is to help organizations make better decisions by providing them with insights that can inform their data-driven decision-making processes.

At its core, decision intelligence works using data and analytics to evaluate different scenarios, weigh their potential outcomes, and select the most appropriate option. It is about making decisions based on data, rather than relying on intuition, past experiences, or gut feelings.

Top benefits of decision intelligence

As the business landscape becomes increasingly complex, decision-making has become a critical component of success. Here are some of the benefits you can gain by employing decision intelligence in your business.

Improved decision-making

By applying analytics during times of decision making, executives can better identify and evaluate different scenarios, weigh their potential outcomes, and select the most appropriate option.

Reduced risks and uncertainty

While risks and uncertainty are ever-present, decision intelligence can help organizations identify potential risks, investigate possible outcomes, and develop mitigation strategies

Increased efficiency

By providing leaders with the information they need at the moment of impact, decision intelligence can lead to faster decision-making and a more agile organization overall.

Enhanced collaboration

Because decision intelligence provides a single source of truth for data and analytics, it can assist in aligning stakeholders and improving communication by ensuring that everyone is working toward the same goals.

How to get started with decision intelligence in your organization

1. Define your decision-making criteria

This step involves identifying the factors in the decision-making process that are most important and determining how you will evaluate these factors. Defining the criteria upfront helps to avoid confusion later on. You can also use this as an opportunity to get buy-in from all stakeholders.

2. Gather data and analyze

The next step is to gather data that is relevant to the decision being made. This can include both quantitative and qualitative data from both internal and external sources. Before we move onto the next step, let’s refresh our memories on the differences between quantitative and qualitative data:

Quantitative data

Quantitative data is numerical data that can be measured and analyzed statistically. This can include data such as sales figures, customer demographics, and website traffic. You can collect this data from a variety of sources, such as CRM systems and web analytics tools.

Qualitative data

On the other hand, qualitative data is non-numerical. It provides insights into customer behavior, attitudes, and opinions. This data might include customer feedback, social media sentiment, and focus group results. You can collect qualitative data through surveys, interviews, and other forms of market research.

3. Identify patterns and insights

Once the data has been collected, it needs to be analyzed to identify patterns and insights. This process involves the use of tools such as data visualization and statistical analysis to identify trends and correlations in the data. By identifying patterns and insights, organizations can gain a better understanding of their customers and the factors that influence their behaviors.

4. Evaluate options and make a decision

Finally, you can use the identified patterns and insights identified to evaluate different options and make a decision. By using data-driven insights to inform the decision-making process, organizations like yours can feel confident that their decisions are more likely to lead to positive outcomes.

Examples of decision intelligence in action

The practice of applying data to the decision-making process has become increasingly prevalent across a range of industries. Let's explore some examples of how decision intelligence is being used in four key industries:

Healthcare

In the healthcare industry, decision intelligence is helping medical professionals make more accurate diagnoses and develop more effective treatment plans.

For example, researchers are using machine learning algorithms to analyze medical images, such as X-rays and MRIs, to identify early signs of disease that may be difficult for the human eye to detect. Decision intelligence is also being used to personalize treatment plans based on a patient's genetic makeup and medical history, improving patient outcomes, and reducing healthcare costs.

In the case of MDaudit, they use decision intelligence to help healthcare organizations improve revenue, mitigate risk, and streamline operations. While the relationship between data and healthcare has sometimes felt like oil and water, decision intelligence solutions are able to bridge the gap to apply BI in healthcare. As CEO, Ritesh Ramesh said:

“If you’re a healthcare compliance or a revenue integrity professional, data is not the first thing on your mind. You’re not trained to be a data analyst. So we need to provide people with the right tools to do their job better.”

-Ritesh Ramesh, CEO at MDaudit

Finance

In the finance industry, decision intelligence is being used to improve risk management, fraud detection, and investment strategies. Machine learning algorithms can analyze vast amounts of financial data to identify potential risks and opportunities, enabling financial institutions to make more informed decisions.

Decision intelligence is also being used to develop more accurate credit risk models, reducing the risk of default and improving lending practices. Let’s consider Loan Market Group, Australia's biggest loan aggregator. By revamping the reporting experience in their SaaS product, MyCRM, they were able to empower more finance professionals to make data-driven decisions, and better serve their clients.

Retail

In the retail industry, decision intelligence is helping companies optimize inventory, pricing strategies, and customer engagement. By analyzing customer data, such as purchase history and online behavior, retailers can personalize their marketing efforts and improve customer loyalty.

Decision intelligence is also being used to optimize supply chain management, ensuring that products are in stock and available for purchase when customers need them. That was the case for T-Mobile Netherlands, who was able to sustain their leadership in the market by decreasing time to insight for critical decision making.

“Using ThoughtSpot during our company’s sale we estimated saved us between 10 to 15 days of work—which helped us get the M&A done as rapidly and successfully as it was.”

-Herman Geerts, Product Owner B2B and B2C Value Streams for DI, T-Mobile Netherlands

Manufacturing

In the manufacturing industry, decision intelligence is being used to improve quality control, reduce downtime, and optimize production processes. Machine learning algorithms can analyze data from sensors and other sources to detect potential issues before they become major problems, minimizing downtime and reducing costs.

Decision intelligence is also being used to optimize production schedules and improve resource allocation, ensuring that manufacturing processes are as efficient and effective as possible. That’s the case for Fabuwood, who was able to bring their siloed data together to reduce data-fueled frustrations across the entire organization.

“Before, people would make decisions and then use data to back it up. Today, with ThoughtSpot, people are relying on data to make decisions because they actually have access to the data. Trust and access play a huge role here.”

-David Samet, Data team Manager at Fabuwood

Make more informed decisions with data-driven insights

Decision intelligence is an integral part of modern business practice. It provides meaningful insights on how to streamline and improve operations, as well as economic returns. When properly implemented, decision intelligence can help a business make well-informed decisions faster than ever before.

By leveraging ThoughtSpot’s AI Analytics, your organization can harness a powerful search engine to make data-driven decisions. And because ThoughtSpot is built for businesses of all sizes across all industries, anyone can access their data to make decisions in real time.

If you’re looking for a way to leverage your data and take your business strategy to the next level, join the data leaders mentioned above and start a ThoughtSpot free trial.

Decision Intelligence: Тонкости и нюансы в использовании технологий искусственного интеллекта

О чем следует знать предприятиям, решившим внедрить технологию в ближайшем будущем.

В конце прошлого года исследовательская и консалтинговая компания Gartner, специализирующаяся на рынках информационных технологий, отнесла Decision Intelligence к числу главных IT-трендов 2022 года. Вместе с тем примеры успешного применения технологии в бизнесе есть уже сейчас, что в совокупности с благоприятными прогнозами экспертов выводит её из разряда «модные термины» на совершенно иной уровень. Расскажем о тонкостях ее использования и нюансах, о которых следует знать предприятиям, решившим внедрить технологию в ближайшем будущем.

Что такое Decision Intelligence или «интеллект принятия решений»

Глобальная цифровизация и ускоряющийся темп жизни изменили подход к бизнес-аналитике. Последнее десятилетие традиционные методы принятия решений в компаниях становились всё менее эффективными, и на смену им чаще стали приходить технологии, включающие в себя возможности Искусственного интеллекта (ИИ).

Системы, базирующиеся на ИИ, оперируют большими объёмами данных и на их основании с помощью заданного алгоритма формулируют наиболее эффективные решения поставленных задач. До недавнего времени работа таких систем ограничивались анализом математических показателей. Всё изменилось с появлением Decision Intelligence.

Decision Intelligence (DI) – это технология принятия решений, которая объединяет ключевые знания из прикладного направления data science, социальных наук и науки управления. То есть DI, помимо количественных, оперирует качественными, «эмоциональными», факторами. С её помощью руководители компаний могут принимать решения в соответствии с политической конъюнктурой и настроениями в обществе – важными для экономического сектора, рынка ценных бумаг и ряда других областей тонкостями. В этом принципиальное отличие DI-систем от других систем для принятия решений: они базируются на знании бизнеса, а не на совершенстве алгоритмов.

Из чего состоит система интеллектуального принятия решений

DI-система представляет собой совокупность технологий и алгоритмов:

DI: Совокупность технологий и алгоритмов

Рассмотрим блоки, которые лежат в основе любой DI-системы:

Машинное обучение. Алгоритмизированная обработка структурированных данных и вынесение решений в соответствии с заданными параметрами.
Глубокое обучение. Апгрейд машинного обучения: выработка предложений с учётом ранее принятых решений и их результатов.
Визуальное моделирование решений. Наглядная демонстрация вариантов решений и их результатов для повышения качества их анализа.
Моделирование сложных систем. Быстрое построение сложной бизнес-логики на основе имеющихся данных, правил и цели.
Предиктивная аналитика. Выстраивание максимально точных прогнозов с помощью построения самообучаемых математических моделей.

DI является попыткой усилить преимущества ИИ возможностями человеческого мышления, открывающей ряд преимуществ для бизнеса.

Повышение точности принимаемых решений

Разберём, как DI может повысить точность решений стриминговых сервисов. Существующие программные продукты с ИИ формируют предложения только на основе предпочтений человека: какие параметры он указал при авторизации, что чаще всего смотрит или вбивает в поиске.

DI может добавить к сбору данных о пользователе учёт дополнительных внешних факторов. То есть ориентироваться не только на предпочтения человека, но и на сезонность, текущую погоду в регионе и даже ближайшие праздники. Это поможет системе сформировать более персонализированный список фильмов, которые интересны и актуальны для конкретного пользователя.
Допустим, сервис знает, что сейчас за окном лёгкий минус и большая вероятность выпадения осадков в виде снега, а пользователь N активно интересуется зимними видами спорта. Поэтому ему можно рекомендовать к просмотру документальные фильмы про сноубордистов.

Эмоции условно прогнозируемы, поскольку, оценивая характер того или иного события, можно предугадать его влияние на социум.
Есть шаблоны поведения людей в той или иной сфере деятельности. Если мы говорим о рынке ценных бумаг, то зачастую шаблоны поведения инвесторов со временем меняются – по мере их погружения в тему. Допустим, сначала человек покупает только акции, а затем добавляет к ним облигации и диверсифицирует свой портфель, балансируя его на уровне 50/50 для снижения инвестиционных рисов. Так вот система с DI может учитывать эти изменения в своих прогнозах для клиентов.
Разные модели поведения людей могут касаться и результатов, которые они преследуют. Например, условно консервативный инвестор рассчитывает на небольшую, но стабильную прибыль в 7-9%. Система с DI учитывает и это. И ищет такие решения, чтобы пользователь получал максимально ожидаемую выгоду на основе своих предпочтений.

Таким образом, системы с DI, в отличие от систем, основанных только на математической рациональности, помогают принимать более объективные и персонализированные решения, отвечая на запросы и ожидания человека.

2. Экономия времени сотрудников компании

3. Повышение эффективности бизнеса

Можно ли с помощью DI вывести человека из уравнения принятия решений

Человек должен участвовать во всех этапах работы DI-системы:

Определение и постановка цели перед системой. Самый сложный и ответственный шаг ложится на плечи человека, ведь на этом этапе формируются бизнес-цели и ожидания по их достижению.
Реализация проекта. Контролируя работу DI-системы, человек сможет придерживаться поставленной цели и ориентироваться на специфику правил своего бизнеса, а не только на совершенствование алгоритмов.
Тестирование системы перед внедрением. Важно удостовериться в том, что DI выдаёт корректные решения задач.
Принятие решения о необходимости применения DI. Система для интеллектуального принятия решений подходит для анализа далеко не всех процессов. Поэтому нельзя поставить бизнес-анализ по этой технологии «на поток». В каждом случае целесообразность подхода должна оцениваться и обсуждаться.
Оценка результатов. При всех своих возможностях, безусловно, DI может ошибаться. Большое количество данных, с одной стороны, делает решение более взвешенным, с другой – способно сыграть злую шутку. Это происходит тогда, когда при измерениях была допущена большая погрешность. Поэтому качество входных данных определяет корректность работы DI-системы.

В каких случаях компании будет полезен интеллект принятия решений

Один из ключевых эффектов от внедрения DI-системы – повышение эффективности бизнеса. Однако такие программные продукты ориентированы на те сферы, где нужно принимать быстрые решения, где данные постоянно меняются, а результаты очень зависят от качественных факторов. Для статичных компаний применение технологии Decision Intelligence нецелесообразно.
Помимо этого, думая над внедрением интеллектуальной системы для принятия решений, компания должна понимать, какие цели она преследует. Рассмотрим направления бизнеса, где организации могут извлечь большую пользу из технологии DI и с её помощью использовать свои данные по максимуму.

Рынок ценных бумаг

Decision Intelligence максимально раскрывает свой потенциал при поиске решений для ряда задач финансового сектора. Технология помогает устранить несоответствия между практикой принятия решений и сложным характером текущих обстоятельств. И это хорошо прослеживается при сравнении DI с её предшественницей – прогнозной системой (предиктивной аналитикой), которая оперирует только количественными факторами.
В сфере финансов, например, фактом изменения цен на акции определённой компании в течение большого промежутка времени. На основе этой информации строится регрессионная модель с графиком. И система пытается угадать, по какой зависимости он пойдёт, чтобы рассчитать будущие скачки стоимости. Модель обучаема: если данные меняются, то и она перестраивается, что положительно влияет на точность принимаемых решений.
Но предиктивная аналитика не учитывает эмоциональные факторы – политические события, социальные изменения и сезонность, которые оказывают большое, порой даже очень большое, влияние на стоимость акций. Эти данные сделали бы модель совершеннее, а решения – точнее. Как в системах с DI.

Здравоохранение

В сфере здравоохранения такие системы могут быть дополнены знаниями (фактами) о пациентах. Рассмотрим случай использования DI в дерматологии. Сейчас есть методы обнаружения меланомы по фотографии пациента, но при классическом подходе использования AI, ML, DL в результате работы алгоритма может быть допущена ошибка.
К примеру, алгоритм на основе фото, где виден размытый контур невуса, выдаст высокую вероятность возникновения болезни в будущем. При этом, если учесть этническую принадлежность пациента, регион его постоянного пребывания и другие факторы, то риск появления меланомы может быть сведён к нулю.
DI-система же будет принимать решение аналогично врачу, который ориентируется не только на визуальную составляющую, но и на ряд специфических маркеров, своего рода собранный анамнез пациента. Именно поэтому, пока такие системы не распространены, врачи не любят проводить диагностику с использованием информационных технологий, «по фото», и рекомендуют очные консультации.

Экспорт товаров

На формирование экспортной политики товаров и услуг может влиять множество факторов, и далеко не всегда только количественных. DI-система выдаёт решение о том, стоит ли реализовывать товары и услуги на экспорт, с учётом текущей геополитической ситуации и в соответствии с внутренней политикой страны. Так, DI будет брать в расчёт наличие санкций, налоговую нагрузку, возможные дотации и т. д. Что важно, поскольку порой именно эти факторы указывают на то, что реализация товаров и услуг на внутреннем рынке на данном этапе выгоднее, чем экспорт.

Как работает система принятия решений на основе эмоций участников рынка

Decision Intelligence — технология принятия решений, которая объединяет ключевые знания из прикладного направления data science, социальных наук и науки управления. То есть DI помимо количественных оперирует качественными, «эмоциональными» факторами.

С ее помощью руководители компаний могут принимать решения в соответствии с политической конъюнктурой и настроениями в обществе, учитывая различные нюансы, важные для экономического сектора, рынка ценных бумаг и ряда других областей.

Из чего состоит система интеллектуального принятия решений? DI-система представляет собой совокупность технологий и алгоритмов. Рассмотрим блоки, которые лежат в основе любой DI-системы.

Машинное обучение. Алгоритмизированная обработка структурированных данных и вынесение решений в соответствии с заданными параметрами.
Глубокое обучение. Апгрейд машинного обучения: выработка предложений с учетом ранее принятых решений и их результатов.
Визуальное моделирование решений. Наглядная демонстрация вариантов решений и их результатов для повышения качества их анализа.
Моделирование сложных систем. Быстрое построение сложной бизнес-логики на основе имеющихся данных, правил и целей.
Предиктивная аналитика. Выстраивание максимально точных прогнозов с помощью построения самообучаемых математических моделей.