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The Rumsfeld Matrix as an effective tool in the decision-making process

«There are known knowns. These are things we know that we know. There are known unknowns. That is to say, there are things that we know we don’t know. But there are also unknown unknowns. There are things we don’t know we don’t know»

Donald Rumsfeld

Rumsfeld Matrix

During a briefing on the Iraq War, Donald Rumsfeld divided information into 4 categories:

These differences became the basis of the Rumsfeld matrix (a decision-making system that displays and evaluates different degrees of certainty and uncertainty).

4 quadrants of the Rumsfeld matrix

  1. Known knowns: These are facts that we know and understand. It is our knowledge base that provides a solid basis for decision making;
  2. Known unknowns: These are facts that we know about, but do not fully understand. These are gaps in our knowledge that we need to address through research, investigation or expert consultation;
  3. Unknown knowns: These are facts that we don’t realize we know. Such information is stored in our subconscious, ignored and considered unnecessary. Disclosure of such information can be beneficial and lead to certain breakthroughs in the decision-making process;
  4. Unknown unknowns: These are facts that we do not know and cannot predict. They are the most significant source of uncertainty and risk, as they can lead to surprise and disrupt plans.

Rumsfeld Matrix effectiveness

Rumsfeld Matrix using

  1. Quadrants definition:
  1. Actions:

AI and ML impact on Data Science

The foundation of all innovation, decisions and competitiveness is data. Today this is the new «oil» that can bring success. Companies across industries continue to collect vast data amounts, requiring the use of sophisticated tools and techniques to extract valuable information.

Artificial Intelligence and Machine Learning have contributed to the advancement of data science. These technologies help data scientists conduct analysis, make forecasts and identify trends, automate routine tasks, etc. Data science coupled with AI and machine learning is shaping a data-driven future. Consider the impact of AI and machine learning.

Artificial Intelligence for data analytics

Artificial Intelligence is widely used in many applications, including for data analytics. AI is used to analyze large data sets that allows to obtain valuable information, identify trends, make forecasts, etc. Machine learning algorithms provide fast and accurate work with huge data volumes.

It is important to use AI in the process of data analysis because there are several advantages:

AI is supposed to complement the work of data scientists. The main ways to use AI in data analysis are:

Data storage for quick insights

We know that the main and most valuable resource of modern business is data. However, is all data equally valuable?

Data loses its value within a few days – it becomes irrelevant and provides little benefit to the company. The goal of every company is to find a solution that can extract useful information and insights from data before it becomes outdated.

Data quality and algorithm efficiency can greatly complicate the challenge of delivering data in a timely manner. So, it is worth paying attention to the data warehouse. Making the right storage decisions is essential to delivering insights quickly.

At the very beginning of the digital revolution, companies stored data to comply with regulations or evaluate past performance. The purpose of data storage has changed over the past 10 years. Now historical data can help to make forecasts and determine future trends. This has led to the opening of new opportunities in the business decision-making process. At the same time, the volume of generated data has sharply increased, and many technologies have emerged for collecting and analyzing it.

Data storage is often not a priority. Huge amounts of data are still stored on disks. This significantly complicates access to data, increasing financial costs and energy consumption. A quality data storage solution frees up time and shifts the focus from high-cost issues to analytics, optimization, and AI solutions. For example, in pharmaceuticals, the time it takes to obtain information is essential in fighting pandemics, developing new, more effective drugs, and improving existing ones. Therefore, an efficient data storage solution is necessary for the operational process of data acquisition.

One of the common challenges in the data management process is data quality. Often the desire to put all the information into data lakes turned the lake into a «swamp». Data that does not correspond to reality reduces efficiency. Therefore, it is important for companies moving to data-driven management to emphasize how they measure and address quality gaps.

Data infrastructure can affect data quality. Built-in AI-based tools can ensure that information is stored correctly, taking into account requirements, mandatory checks and security measures. Data infrastructure presents enormous opportunity while eliminating the human costs and energy inefficiencies of legacy storage systems.

Data Science for fintech companies

The basis of any business is information. Now, there are a large number of different data analysis methods that are used by companies to automate and achieve maximum efficiency. Financial companies that provide financial services are no exception. They own and process a huge data amount that requires quality management and a high level of protection. Data Science plays a key role in orchestrating these and other processes.

  1. Fraud detection and prevention

A key indicator of a financial organization success is the security of customer funds and data. This is a rather difficult and time-consuming process. It is important to use not only technologies that will help block fraudulent activities, but also technologies that can detect suspicious activity at an early stage and assess the situation. Such technologies need to be regularly monitored and updated.

The huge number of transactions and processes makes it impossible to manually track suspicious activity. However, Data Science is a great tool in this situation. It allows to create an algorithm for self-analysis of certain actions, which will automate the detection process. This technology is capable of self-learning. Processing more data leads to more experience and knowledge. Detecting counterfeit documents, copies of financial transactions and invoices, suspicious activities, and preventing fraud is possible thanks to Data Science.

  1. Risk assessment and management

Machine learning and AI, among other things, help assess financial risks and security. New machine learning models enable more effective risk analysis and management.

Competitors, authorities, investors and other participants may pose certain risks to the business. Any situation must be resolved taking into account an understanding of the risks, potential losses and possible growth points. This process requires the analysis of a large amount of processed and raw data.

Complex self-learning Data Science algorithms will be most useful in this case. They evaluate data to analyze risks, allowing companies to create a reliable model for future development.

  1. Client data management

The most valuable resource of any company is data, and its management is too important. It is advisable to process such a large data amount that financial companies own automatically. AI will provide fast and efficient analysis of unstructured data.

  1. Personalization

The income of a modern business depends on accurately guessing customer needs. The better the company guesses the client’s desire, the higher the chance of receiving more income. It works the same way for financial companies. Customers will be much more willing to use the company services that has an offer tailored to them based on their income, needs and situation.

Data Science allows to track user behavior and provides a complete picture. The business is able to make more informed decisions, and the client receives a unique and personalized offer.

  1. Analytics

Data collection and analysis are 2 key processes. Modern technologies make it possible to efficiently process huge data amounts of various types. This allows to track data changes and make changes to prevent risks. For example, analyzing customer data opens new opportunities, allowing to respond to customer interest and build a high-quality marketing campaign.

Work with data and team training

The modern world is driven by data. Every person and company generate a huge data amount every day. And if a person can forget about data, companies can’t miss the possibility to work with data. Modern businesses are active data users in their daily routine. Based on the assigned tasks, various indicators are analyzed, and forecasts are made. This allows companies to move away from a «blind» approach and act based on real and reliable data.

Modern analytics tools are quite easy to use, that allows any company employee to perform a certain set of data functions. However, for better and more complete work with data, training and regular professional development are necessary. Full involvement and commitment of the team can guarantee success and a good overall result.

Company leaders often encounter resistance from employees to learn something new about data. Below are a few recommendations that will help overcome resistance and resolve the main problems when implementing the training and professional development process.

  1. Overcoming resistance

Employee resistance to undergo training is one of the main problems. It’s worth looking at the problem as a marketer. You should make the training so that people want to take it. Proper promotion of training is one of the most important elements of employee engagement. Engaging company leaders who can highlight the value of acquiring data skills will be a powerful selling point. However, it is worth focusing not only on the final result and company benefits, but also on the capabilities of each employee and their career success.

  1. «Simplicity» of data

The main reason for employee resistance is the fear of the new and the perception of working with data as something extremely complex and incomprehensible. First of all, it is necessary to clearly explain why working with data is so important for everyone and for the company in general. In addition, machine learning, Artificial Intelligence and data science create a fear of «unnecessary» employees. Managers should explain that the introduction of these technologies doesn’t imply a reduction in the team, but rather the automation of some functions to optimize, simplify and improve the employee’s work. Good use of data is the key to making effective decisions across one position in the company, and therefore, one team and the entire company.

  1. Understanding data usage

The use of data must be clear to employees. Each employee must understand why he is using the data: what his specific goal is, what the company’s goal is, to improve what business processes, etc. Such transparency and understanding will allow employees to feel more confident and correctly present the data in discussions about the results.

How I Almost Destroyed a £50 million War Plane and The Normalization of Deviance

‘Recovery!’ came the shout from the back seat of my Tornado GR4 combat jet but it wasn’t necessary – I had already started to yank back on the control as hard as I could!

Our 25 tonne fuel laden bomber was now a treacherous 40 degrees nose down and shuddering madly as the airflow violently separated from the wing due to my impossible demands.

As we broke through the base of the cloud, my Head Up Display was suddenly filled with a sickening amount of earth and fields.

This was bad.

The Ground Proximity Warning System sounded.

‘Woop, Woop! – Pull Up, Pull Up!’

‘7,6,5 – that’s 400 ft Tim!’, called my Weapons System Officer.

We were well outside ejection seat parameters and we both knew it.

How had I got us into this mess?

Stop.

Yes, sometimes you just have to stop.

And that can be very hard indeed, especially when you have been doing something for so long that it has become routine.

For most of us it might be societal addictions such as smoking, drinking, drugs, gambling – things that have now become normal in your life but aren’t doing you any good.

For others it might be work habits or just ‘things you do’ that, over time, have become routine and are now hard to change.

Sometimes, though, it can be a lot worse.

I recently learnt of a flying accident that so appalled my colleagues and I that it generated a discussion about if sometimes, what is described as an ‘accident’, should actually be defined as something that was more intentional.

You can read a continuation of original article at the link

Key benefits of Business Analytics

An indispensable tool for modern business is Business Analytics. Regardless of the field of activity, each company generates huge data amounts. Proper work with it opens many opportunities for business development.

Business Analytics is a tool that uses quantitative methods to extract valuable information (meaning) from the data provided. Based on the information received, the business is able to make informed decisions, take certain actions and conduct a detailed analysis of the situation.

Key benefits of Business Analytics:

Data Science for telecommunication sector

Now, telecommunications are an important component, that provides uninterrupted communication and data exchange. In parallel with technology development, data amount generated by telecommunications companies is growing. This has contributed to the search for data science solutions to transform the telecommunications industry.

Telecommunication companies collect a huge data amount: call records, text messages, location data, customer interaction information, etc. Proper use of such data will help to reveal information that will drive business growth, improve customer experience and optimize operations.

Data science can help solve the following challenges in the telecommunications sector:

Data processing solutions in the telecommunications sector are based on Big Data and AI technologies.

Key points for successful AI integration

The transformative power of AI is driving its popularity of using. Every company of any size strives to use this technology and implement it in their daily activities. However, AI implementation can be quite a challenge that requires a strategic approach, planning, resources and a willingness to innovate. Below are the main points that will help to implement AI successfully.

  1. Business goals and needs

First of all, it is necessary to determine clear business goals and needs. Understanding all current processes can easily answer the question «where, how and what processes can be optimized by AI». It is advisable to use this technology in order to automate time-consuming and repetitive tasks, make more efficient decisions and improve the quality of customer service.

  1. Solution choice

The market has many AI solutions, including chatbots, natural language processes, machine learning, deep learning, etc. It is important to choose the right solution that meets the business needs. When choosing a solution, it’s worth exploring AI technologies and platforms and take into account scalability, flexibility, and ease of integration. It is also worth paying attention to the compatibility of the solution with the existing infrastructure.

  1. Data strategy

AI uses large amounts of data to train and make predictions. Therefore, there should be a clear and understandable strategy for working with data, which includes determining the necessary data, determining how to obtain different data types, how to collect, store and access data, privacy rules for the data, responsible for storing data, how and why data analytics are used for information and trends.

  1. Team

The successful implementation of this technology requires a qualified team. This may include data scientists, machine learning engineers, data engineers, and subject experts.

  1. AI model training

To train an AI model, learn patterns, and make sound predictions, it is necessary to provide a comprehensive data set. Collaboration with data scientists and AI experts is also needed. This will help to develop and fine-tune the model to produce accurate and reliable results that are consistent with business goals.

  1. Integration into processes

Once trained and tested, the AI model can be integrated into business operations. During the integration process, it is possible to make some changes to existing systems and processes. Deployments should make an effort to minimize errors in existing workflows. It is also important to provide stakeholders with regular support and training. This will help successfully transition to AI-driven operations.

  1. Performance monitoring and evaluation

Regular performance monitoring and evaluation is essential to ensure that the AI model is working correctly and is performing well. It is worth identifying key indicators that can be used to measure the impact of AI on the company’s activities. Analysis of the results will help to identify problems and areas for improvement.

  1. Improvement

Like any technology, the AI model needs to be updated regularly. This is the key to maintaining a concrete business advantage. As the business changes and develops, it is necessary to make adjustments to the AI model. There are constant innovations in AI technologies and methodologies that can be added and applied. But before that, it is worth considering how and for what they can be applied. Revising and updating data strategy will drive business forward.

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