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.

Business analytics role in Healthcare and Pharmaceuticals

Business intelligence is essential to the business, helping companies make informed and effective decisions. Regardless of the activity field of the company, business analytics is its integral component. In the healthcare and pharmaceutical industries, analytics is critical to maintaining a company’s competitive position in the market. Medical and pharmaceutical companies own a huge data amount that requires proper storage, processing and analysis. This, in turn, allows to see real information from patients, regulators, competitors, etc. The main business analytics goal in this area is to empower decision-making, develop and implement innovations designed to save lives.

How exactly business analytics helps pharmaceutical and healthcare companies:

At the moment, there is a rapid increase in the cost of launching a new medicine. Also, patents on already existing and in-demand medicines are expiring. It becomes relevant to accelerate the process of new medicines development. In this case, analytics will allow to get the most out of large data sets, publications, scientific information, and also allow to create forecasts and make decisions;

The use of big data technology in the healthcare and pharmaceutical industries helps reduce costs and increase efficiency. This is possible due to the increased speed of clinical trials, analysis and determination of a large number of data points (historical data, patient monitoring data, demographic data, etc.). In turn, a qualitative study of the results of clinical trials provides an opportunity to improve the efficiency of diagnosing diseases;

The modern world is made up of data. This leads to the complexity of their processing. Big data analytics helps solve this problem by combining data from various sources (medical records, medical sensors, genome sequencing, etc.). This allows to identify patterns and create medicines for the patient based on his individual needs;

High-quality work with data contributes to a better market understanding, to analyze the work of sales representatives, to analyze marketing channels and make decisions based on this data. Healthcare and pharmaceutical data is growing exponentially. In this situation, it is important to have modern technologies for processing and analyzing data, as well as predicting future trends using historical trends and data.

Each company has its own requests and needs that need to be covered with the help of data. However, there are basic requirements for an effective result of working with data:

  1. Data structuring

Increasing efficiency is possible with the help of the correct data organization, management and storage. In healthcare and pharmaceuticals data is used for any purpose: evaluating medicines, future use, market potential, funding clinical trials, etc. Data structuring provides a quality process for organizing, processing, extracting and storing data for effective work with them.

  1. Data collection

Algorithms and modeling techniques help to identify data patterns and interrelation. This, in turn, allows to make more accurate forecasts in research, development, marketing, clinical trials, etc. The use of clustering, associative segmentation and data classification tools improves the quality of medicines development and delivery methods.

  1. Artificial Intelligence and machine learning

These tools are used to manage a huge data amount. Pharmaceutical and healthcare companies are using AI and machine learning to find medicines more easily.

  1. Visualization

Better perception and understanding of data, trends and patterns, information is possible with a graphical format. Data visualization helps analysts and clinicians identify patterns and interrelatoin and make informed decisions quickly.

Of course, working with data requires certain skills and training. At the moment, there are many online portals that offer tutorials. However, in order to choose a program, it is important to understand the data that the company owns, its needs and goals, as well as the tasks that employees must solve with the help of data.

Data and analytics for business development

The determining factor in achieving success or failure in business is increasingly data and the effectiveness of interaction with them. Now, information can be obtained from many different sources using available technologies to extract it. Today we are witnessing a shift from an «intuitive» decision-making model to a business model where all decisions are made based on reliable data. Such a wave will cover all industries in 2023 and beyond. This approach adds more confidence in the correctness of the decisions and actions taken.

Data and analytics world is very dynamic, where new technologies are constantly emerging for faster and more accurate access to information. New trends encourage consideration of ways to use them in business and in society.

The most important data and analytics trends for business development in 2023 are:

  1. Data democratization

Expanding the ability to work with data of all team members is one of the important trends of this year. Data democratization refers to providing improved access to data for each team member in order to fulfill duties and tasks. In addition to access to data, it is necessary to organize a learning process to acquire the necessary knowledge and skills. This will help to use data qualitatively, draw the right conclusions and identify new ideas and opportunities. In turn, this implies new forms of augmented work (apps, tools, and devices delivering intelligent ideas to each employee for better results).

Understanding customers, developing a quality product and service, optimizing internal operations, reducing costs are all possible with the help of data. More and more companies are realizing this. However, the ability to work with data and make decisions based on it must be available to all departments of the company and all staff (technical and non-technical).

An example is the use of natural language processing (NLP) tools by lawyers to scan document pages. Also, the use of hand-held terminals by sales assistants to access real-time purchase history, allowing them to better recommend products and make additional sales. A study by the McKinsey Institute found that companies that provide enhanced data access to their employees claim the positive impact of analytics on revenue.

  1. Artificial Intelligence

Artificial Intelligence will have the greatest impact on the life of business and life in general in the future. Its main objectives will be to improve the accuracy of forecasts, reduce the time required for daily and repetitive work (data collection, cleaning, etc.), providing more opportunities for users to act based on data, regardless of roles and technical knowledge.

AI aims to make the process of analyzing data and extracting valuable information faster and more understandable using software algorithms. Today, the principles of machine learning and AI technologies are most often used in business. These include NLP, that allows computers to understand human language and communicate with us, computer vision to understand and process visual information using cameras, and generative AI to create texts, images, sounds and videos.

  1. Cloud and Data-as-a-Service (DaaS)

The work of Data-as-a-Service technology is implemented with the help of cloud. Companies have access to data sources collected and processed by third parties through cloud services. Payment for such services occurs upon the use of services or by subscription. As a result, companies don’t need to build their own costly data collection and storage systems for many types of applications. In addition, DaaS providers offer analytics tools as a service.

  1. Real time data

When working with data to find new solutions and insights, it is critical to understand the current situation. Outdated data (yesterday, last week, etc.) is of no use in this case. Only real-time data is a valuable source of information for business.

Working with such data involves a more complex data infrastructure and analytics, which increases costs accordingly. However, being able to act «here and now» based on data (analysis of data on site visits, determining the best offers and promotions for each client, tracking transactions, and much more) is a strong advantage. Facebook analyzes hundreds of gigabytes of data per second for a variety of use cases, including displaying ads, preventing fake news, and more. Real-time video analysis is performed in a South African national park to detect poachers.

  1. Regulation and data management

Many governments are taking data security into account. Laws are being passed to regulate usage of personal and other data types. Now, there are such data protection regulations: GDPR (Europe), PIPEDA (Canada), PIPL (China). Gartner predicts that 65% of the world’s population will be covered by GDPR-like regulations in the near future.

This will affect every company, regardless of its location. Their internal processes for processing and storing data will need to be documented in a certain way. It also means that companies will have to be tested to see if they know and understand what information they have, what data they collect and store, and for what purpose it is used. Of course, this can become additional work. However, it will be an advantage in the long run. When entrusting their data to the company, it is important for customers to be sure of their security. In turn, companies can use customer data to improve their products or services, as well as develop new ones based on customer needs.

Business Intelligence and Business Analytics Trends in 2022

Data is an indispensable tool for modern business. It is used everywhere in business: from supporting the decision-making process itself to upgrade and improve products and services. 2021 has been a year of intriguing advancements in Business Intelligence and data analytics. The goal was to provide companies with a custom solution based on different technological approaches to make the most of their data. Given the dynamic pace of development in this area, new solutions can be expected in 2022. Consider the main trends in the development of Business Intelligence and data analysis.

  1. Data Factory and Hybrid Cloud

The Data Fabric is a cohesive, conceptual information management architecture that provides complete and flexible access to work with it. Data factories feature is the use of the approach and tools of Artificial Intelligence, Big Data and machine learning in order to organize optimal data management algorithms.

The latest development from Qlik is an example of such a concept. Qlik Forts is designed to connect all data regardless of its location in the cloud. Companies using a variety of private and public cloud platforms have the ability to easily connect and leverage all of the data for an efficient workflow. Moreover, the data is available for analysis to the user from anywhere in the world. In 2022, the development and bringing of analytical solutions to a new level.

  1. Automation and Machine Learning

Nowadays, most of the commonly used machine learning algorithms perform their functions well and smoothly. Specialists, in turn, are trying to improve developments and make them even smaller, faster and more efficient. The next year will be devoted to platforms and tools development that can be used by any user to automate any task.

  1. Small data

To solve the scaling problem mentioned in the previous point, it’s worth starting by changing AI and machine learning goal. It is advisable to process only the most important data, in other words, go to small data. However, this does not mean that big data is losing its value – it will always be needed. We can already see the success of implementing the «small data» approach in self-driving cars to quickly respond to potential road accidents. In 2022, we can expect new ideas emergence with the effective use of small data.

  1. From SaaS to iPaaS

One of the trends next year will be SaaS (software as a service). However, in 2022 there will be some changes, namely iPaaS. The tool is a cloud-based solution with the ability to easily scale and integrate large data amounts. Gartner defines iPaaS as a cloud services set that enable the development, execution and maintenance of integration flows that connect any combination of on-premises and cloud services, processes, applications, and data within one or more organizations. The goal of every company is to avoid data loss and information silos across departments and platforms. Therefore, a breakthrough in this area is expected next year.

  1. Planning and forecasting

Back in 2020, the predictive and prescriptive analytics market was projected to grow by 20% over 5 years. Now analytics is becoming more accessible to users. With the help of Qlik BI platforms, it is possible to easily integrate predictive analytics into CRM, ERP, etc. In 2022 and beyond, this area will actively develop.

  1. Information literacy

The final item on the trend list is information literacy. Knowledge is essential for the implementation and correct use of all technologies and innovations. Otherwise, all progress will be meaningless. Modern companies that strive for dynamic development should think about quality training for their team. End users require particular attention, as they often make data-driven decisions.

Qlik optimizes HR workflow

People are the main source of success for any business. The main HR professionals’ task is to maximize this resource value through a deep understanding of staff performance and predicting changes impact.

Analytics has never been the primary tool for HR professionals. However, they possess a large data amount that can be effectively used to develop business strategy and make decisions. Qlik HR analytics allows to effectively use data, hire the right people, provide them with a favorable climate and conditions for work and business prosperity in general.

Talent acquisition

Modern business requires modern approaches in everything, including in the recruitment process. Qlik Analytics helps to modernize and automate the talent acquisition process specifically:

Education and development

Another task of HR specialists is to provide conditions for the development of each team member. With the help of Qlik analytics, they can accomplish the following tasks:

With Qlik tools, HR professionals have the ability to combine data from the LMS, performance management system, and other employee data sources to create a holistic view of performance. This allows:

DataLabs is a Qlik Certified Partner. A high level of team competence and an individual approach allows us to find a solution in any situation. You can get additional information on the project by filling out the form at the link

Previous #fridaypost “Qlik helps IT specialists to undertake mission”

The main ways to monetize corporate data

Although data is the main asset of modern business, the issue of its monetization remains relevant. Business owners and leaders clearly understand that data is the key to effective operations and overall success. However, they have no idea what monetization opportunities exist.

Let’s consider 3 main options for data monetization to improve business performance.

  1. Transparency and understanding

The first way to monetize data is to use it for better market, trends, behavior and customer needs understanding. Data is a key tool for identifying current and future customer needs.

Regular work with customer data helps to create more relevant, quality and personalized products and services. The client gets the desired product – the business increases profit.

The result of this approach is building long-term relationships with clients, establishing their loyalty, which in turn increases the lifetime value of each client.

  1. Reduction of expenses

Any company seeks to increase revenue and reduce costs. The data can be used for: business processes optimization, workplaces automation using AI, reduction of transport, logistics, time and financial costs, marketing costs optimization, etc. It is important to study the data held by the company to understand exactly how to use it to optimize all processes, cost and increase profits.

  1. Data renting or selling

Of course, selling or renting data can be a great monetization option. Data uniqueness or inaccessibility will be the advantage in this case. Data is an independent asset of the enterprise? and its value is included in the total company value for a sale.

To implement this method of monetization, it is necessary to answer several questions:

Based on the answers and ideas, an effective monetization strategy can be drawn up.

Companies from different market area can use this method. For example, the American engineering company John Deere has created an intelligent farming system. Thus, the system collects data on weather, climate, soil condition to form recommendations for farmers what and when to plant, what fertilizers to use, etc. The company’s annual income from the data sale to farmers is about $1 billion.

Another example of using this method of monetization is the American financial services corporation Visa. The company sells data to retail businesses that need information about shopping patterns. Retailers may not always be able to see their customer’s entire purchase history. Thus. it makes impossible to get a correct understanding of shopping behavior. Visa, possessing this valuable information, sells it to companies and generates additional income of more than $1 billion a year.

The influence of BI on business performance

Until recently, BI applications were mainly used only by IT professionals. With the technology development, Business Intelligence has become the main tool for many business users from various fields of activity.

The main task of Business Intelligence is to extract important facts from structured and unstructured data, transform it into coherent information that allows to make effective business decisions, increase productivity and optimize the operational activities of the organization.

Business Intelligence is a workflow that, by scanning data and extracting facts from it, helps business owners, managers and other business users to see a clear picture of the current situation, analyze and develop an appropriate action plan. With the help of this tool, companies are able to collect information from internal business structures and external sources, that makes it possible to improve the internal business structure, identify market trends, and identify problem areas.

The wide information resources provided by business analytics enable companies to achieve their goals faster. Any interaction with customers (voice messages, testimonials, chatting or email communication) can be carefully analyzed to extract information about customer preferences, technical difficulties they face, their reactions to promotions, purchasing power, etc. Analysis contributes to the improvement of all business indicators.

A high-quality process of making effective business decisions is provided by Business Intelligence. The main business areas affected by Business Intelligence within the company:

  1. Increasing business productivity

The business analyst team is dedicated to extracting and interpreting data using BI applications. Thus, the company management can focus on the management of important resources and workforce. The result of this approach is financial and time costs improvement and business performance optimization.

  1. Extraction of critical information

BI provides business users with the ability to extract critical data by analyzing customer interactions and visualize them in a convenient and understandable way. The tool ensures that detailed and reliable reports are provided to all business users.

  1. Information availability

By incorporating Business Intelligence into the workflow, companies gain access to all the data they need to make decisions. Users can get such access at any time.

  1. Return on investment

The main BI advantages are the ability to reduce costs, increase revenue, and increase margins. It helps to improve ROI. BI also helps to improve indicators such as employee productivity, quality of decision making, customer satisfaction, business process efficiency, etc.

  1. Real-time reporting

A high-quality report based on reliable information provides the company’s management with a clear understanding and the ability to evaluate business processes. By providing reports on critical data (current and historical), as well as data on future trends, customer needs and preferences, companies are able to operate effectively.

Top 6 mistakes in data strategy development process

Any company, no matter the size, needs a data strategy. Only data can provide a business with answers to all questions, thereby improving operations and increasing profits. However, an ineffective strategy makes it impossible for a company to tap its full potential. Here are 6 the most common mistakes in data strategy development process.

  1. Outdated strategy

The world is rapidly transforming. The business strategy must fully comply with the requirements of the 4th industrial revolution. The data strategy, in turn, must support the business strategy and be relevant in the modern world. Investing in an outdated strategy is pointless. This not only does not work, but also leads to inappropriate costs.

  1. Ignore business goals and objectives

Not all existing methods of data analysis are important and effective in achieving the business goal of a particular company. Many companies develop a data processing strategy based on interesting cases. However, they do not take their own interests into account. It is important to define goals and objectives, figure out how the data can be used to achieve them, and only then begin to develop a strategy.

  1. Do not define indicators of success

How can you determine how successful or unsuccessful a strategy is? – determine the KPI. Only by defining the key performance indicators, it is possible to correctly track and analyze the progress. Any data initiative must have a business case with appropriate KPIs.

  1. Concentrate only on technical costs

Implementing a data processing strategy entails technical costs. However, do not forget that changes will be required in the corporate culture. Each team member must rely on data every time he makes a decision. To do this, it is necessary to organize a training process for collecting correct data, assessing their quality and analyzing.

  1. Consider only structured data

At the moment, data comes in different forms – photographs, sound recordings, text files, and much more. The data strategy must take into account structured and unstructured data to get the correct information. Don’t ignore external data sources from repositories, governments and data brokers as it can be useful.

  1. Ignore ethics, confidentiality and legal aspects of data

Any data project should start by defining aspects of the ethical and confidential data usage. Consumers must be confident in the confidentiality of their data usage, and also benefit from it.

The correct data strategy is a key element in the process of achieving business goals. It should be based on 3 to 5 use cases. The use cases for each business will be different, however, they should be consistent with the business strategy.

The main analytics levels in the automation process

Data by itself is not too valuable. What makes it valuable is analytics and insights. Data analytics helps to get the current situation, draw the right conclusions and make decisions that will lead to more efficient business activity and growth.

Let’s consider a car example. Often, we do not think about how it works, but we know that it can go wrong. It is possible to identify the causes and prevent them using analysis. It is enough to collect 2 datasets: the first is about car workflow and the second is about time when the car fails. Such dataset analyzing will help to understand what actions lead to failure and identify ways to prevent recurrence in the future.

Descriptive analytics

Data scientists call the analytics in the example above «descriptive». The result of this analytics type is the description of «what?», «when?» and «why?» happened. However, it does not offer possible solutions.

Now modern companies mostly use descriptive analytics. Specialists get access to the full amount of information in the form of graphs, charts, tables, reports that show the recurrence number of a particular event. This can be interaction with a client, a mechanical failure, sales, etc. Next, the specialist’s task is to analyze all information and develop the action plan.

A good example of descriptive analytics is Google Analytics. This tool perfectly visualizes indicators of site traffic, its load, user behavior on the site and much more. Descriptive analytics can be useful when used strategically.

Predictive analytics

The goal of many companies that adopt artificial intelligence and machine learning technologies is to enable predictive analytics.

An idea if predictive analytics is to analyze data about what has already happened and make predictions. Let’s go back to the car example. There is problems report from January to March. Prediction algorithm provides information on how and when a machine may fail in the future.

The result of predictive analytics is a set of probabilities that do not give 100% confidence that an event will definitely happen in the future. However, having such information, it is possible to prepare, check the availability of spare parts for repairs and develop a «plan B».

Predictive analytics is actively used by banks and lenders to assess risks. In consequence of analytics bank employees receive an estimate of the payment likelihood by a particular applicant and can compare it with risk thresholds.

Until recently, only advanced companies could implement analytical technologies and evaluate benefits. Now these companies are moving to «prescriptive analytics».

Prescriptive analytics

So, predictive analytics provides an answer to the question «what can happen», but does not answer the question «what to do to obtain the optimal result?» The next step in analysis is prescriptive analytics.

The predictive analytics system provides a number of possible outcomes. This can be effective when the specialist exactly knows the effective solution. For example, choose a specific action to increase sales. However, if the goal is to increase revenue in general, it is necessary to know the most effective measures package. This is where prescriptive analytics can help.

Let’s take autonomous vehicles as an example. The car must «know» that the fastest left/right turn can be lengthened by heavy traffic for a certain period. In this case, it is necessary to choose the best option and «register» it to the computer that drives the car.

With prescriptive analytics, the person driving the machine can know not only the cause of the failure, but also the best way to minimize such situations.

The 3 levels of analytics described above are fundamental to the automation process. Until recently, advanced predictive and prescriptive analytics technologies were prohibitively expensive for most companies. Now there are a large number of analytical tools and platforms that small organizations can afford. Gradually progressing through the analytics levels contributes to a successful digital transformation.

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