data analytics

The main characteristics of modern analytics and BI platforms

Selecting and obtaining software for data analysis and BI can be an extensive and intricate process that demands careful consideration and time. Now, various analytical platforms are available from numerous providers, making it essential to take several factors into account when choosing a provider, including budget, the cost of specific data analysis tools, and their functionality, which should satisfy the company’s needs and objectives. Additionally, it is vital to comprehend the direct user of the tool before making a purchase.

When searching for and selecting analytics and BI platforms, the main characteristics to take into account are as follows:

  1. Automated Insights: modern analytics platforms must incorporate automated learning methods to inform end users and help them understand key indicators;
  2. Data Preparation: a high-quality tool should generate analytical models based on user data, including metrics, groups, categories, structures, and data from multiple sources;
  3. Data Visualization: effective visualization is essential for clear data display. Users should have the flexibility to approach visualization creatively and easily understand information, draw conclusions, and make informed decisions;
  4. Manageability: an analytical platform should include data verification functions and enable monitoring and control of information exchange and promotion process;
  5. Ease of Use: Modern analytical and BI platforms should be user-friendly for users of all levels, operate smoothly and clearly, and not require specialized knowledge.

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.

Data as a key element of a decision-making process

Data is an integral part of modern life. Almost every human action generates large data amounts. The most valuable use of this data is how companies use it to make business decisions. For example, viewing candidates’ profiles on LinkedIn to recruit a targeted candidate, research and identification of priority markets for product promotion.

The most serious business data applications are automated and used to solve more complex and important tasks.

These processes take place automatically without human involvement.

Experience and intuition are traditional assistants to business leaders. However, despite the value of these qualities, a business using data in the decision-making process is 19 times more profitable. Data helps to make better business decisions that leads to goals achievement.

Many companies claim to be data-driven because it is «trendy» these days. But in practice this is not entirely true. They only take data into account if the data matches the beliefs and intuition of the business leader. A data driven business assumes that data is the only point of truth. Decision making of any complexity occurs as a result of data analysis.

There are 4 main areas where data is needed to make effective decisions:

  1. Solutions related to customers, markets and competitors

The data will help to understand better customer behavior, track changes in habits and interests, make a targeted offer, meet customer expectations and stay ahead of the competition.

  1. Financial decisions

The company’s management has the ability to investigate in detail sales trends, cash flow cycles, profit forecasts and changes in stock prices. This allows to make informed budget allocation decisions and leads to cost savings and growth.

  1. Decisions related to internal operating activities

The joint use of data and Artificial Intelligence allows to optimize the operation of equipment, set up the process of preventive maintenance. This will allow to determine in advance where breakdowns may occur, and repairs will be required. With this information it’s possible to plan the optimal replacement / repair process and minimize deadtime.

  1. Solutions related to human resources

The data helps to study the team composition and quality, to determine the shortage of certain specialists, qualifications level, the appropriateness of compensation for a certain type of work, since employees are always tempted to go to a competitor, taking their experience and skills with them. So, using the data, Google identified 8 basic qualities of a good manager, including «a good coach», «a clear vision of the team». This analysis allowed the management of the company to make informed decisions on the promotion of employees on the career ladder.

Machine Learning & Big Data

Among other modern terms and concepts, the most relevant are machine learning (ML) and Big Data. These 2 terms are often used in conjunction, although they have a fundamental difference. And it is important to understand this difference during a data strategy development.

The similarity between machine learning and Big Data is that both terms refer to the field of theoretical academic research and practical data-driven business applications. It is a scientific discipline that studies information and use cases.

Data is the main engine of technological progress. It helps to create new tools and platforms to change the world through analytics, more accurate modeling and forecasting. The development of the Covid-19 vaccine is a great example of the data importance in today’s world. Usually, it took up to 10 years to develop a vaccine. However, over the past decade, the ability to collect and process data has expanded significantly. It has significantly accelerated the pace of vaccine development. If this pandemic had happened in 2010, it would have taken a lot longer to solve this problem, just because technologies for deep data understanding were in their infancy.

This situation is made possible by both Big Data and Machine Learning. Let’s make sense of the terms.

Big Data is a collective term that includes a huge amount of ever-growing information, as well as tools, methods and technologies that have been developed to work with data, including Machine Learning. With the Internet transformation into a daily use tool, Big Data has begun to be identified as a powerful tool. Big Data isn’t just about size. Data definition as big assumes the presence of 3 characteristics («3 V»):

Machine Learning is a type of computer algorithm. It can be viewed as part of Artificial Intelligence (AI). A fundamental aspect of intelligence is learning. Machine learning is involved in creating programs that help to perform better taking into account an ever-growing data amount.

It is important to understand the difference between supervised and unsupervised ML. Supervised learning is a Machine Learning technique that includes tagged learning algorithms that lets you know immediately how well an operation has been performed. Unsupervised learning is a method of Machine Learning, as a result of what the system under test spontaneously learns to perform tasks.

Big data and Machine Learning are intertwined. The best results are most likely to be obtained by using the most appropriate ML and Big Data processes.

However, if the business does not work with Big Data, Machine Learning is unlikely to be needed. Its main advantage is the extraction of value from datasets that are difficult for classical computer and statistical analysis. For example, for a static dataset that fits into an Excel worksheet, the ML implementation will not be justified. It is advisable to use this tool in the case of working with unstructured data that cannot be understood using tables (text, graphic, sound data etc).

What’s the best way to start interacting with data?

High quality interaction with data is the market leaders’ hallmark. Data has become the foundation for groundbreaking concepts like Artificial Intelligence and the Internet of Things. The main goals of using data: products and services upgrade, internal processes optimization and performance improvement, understanding user needs and behavior, monetizing and additional revenue generating.

Everyone understands that it is necessary to work with data, but not everyone understands where to start. Any process should start with setting goals. Before you can start working with data and getting value, it’s necessary to establish long-term and short-term goals for the company (for example increase profits, scale, reduce customer churn and manufacturing defects, understanding customers and the market). Moreover, each team member must know and understand them.

Here are a few guidelines to help you identify a company’s capabilities using data:

  1. Use cases

An effective solution would be to familiarize yourself with existing cases and look at the other companies’ experience, how they used the data and what result was achieved. A great example is the American company Netflix, which has adopted data mining. The company uses the collected data on the behavior of their customers to form recommendations for films and shows, content, etc. With the help of data company monitors the quality of video playback and it helps to increase the customer service level. Also, Netflix monetizes the received data through advertising partners.

  1. Brainstorm

To solve the problem, it is necessary to gather all interested business participants and brainstorm the way. The purpose of this process is to combine business goals and possible use cases of data to achieve a result.

During the brainstorming process, it’s important to answer the following questions:

Top 6 Benefits of Business Intelligence Usage

Business intelligence is a powerful tool for the modern business. Companies and organizations in different activity fields generate a huge data amount. This tool is a set of qualitative methods for obtaining the values ​​of the collected data. Business analysts’ task is to process, interpret and analyze information. Action and plan development, informed decisions making, current situation seeing – the result of business intelligence.

The main benefits of business analysis for companies:

  1. Informed decisions making

Making strategically important decisions such as planning a marketing budget, predicting the most popular product or service, determining keywords for promoting a business is not complete without business intelligence. The tool provides an informed decision-making process, thereby improving results.

  1. Increased efficiency

Business intelligence helps to identify all the shortcomings in the course of a specific task. This allows to reduce time, energy and resources costs which, as a result, leads to the optimization of all business processes.

  1. Budget increasing

Small businesses have much less financial resources than large businesses. This tool usage helps to learn more deeply and understand customers’ behavior and needs, to gain competitive advantages. This leads to the maximization of budgetary funds.

  1. Goals achievement

Every company must have short and long term goals. Data processing strategy and business strategy are built is consideration of these goals. Data visualization allows to track past and present performance information against KPIs. Detailed and easy-to-use diagrams, tables, graphs greatly simplify the decision-making process. This, in turn, leads to the achievement of business goals.

  1. Revenue increase

Many companies that implement analytics technologies into operations have already benefited and experienced significant revenue growth. McKinsey (an international consulting company that specializes in solving strategic management problems) conducted a research. As a result, it was found that the average increase in the income of organizations that invest in big data is 6%.

  1. Relevant information possession

A large number of proposals on the market contributes to the dynamic change in customers’ interests and needs. Analytics provides a detailed description of the target audience, their needs and actions. Companies can correlate product and services with analyzes and determine the relevance of their offerings, upgrades and new developments.

MDM implementation requires a clear strategy

Digital transformation has forced management to rethink current business models to accelerate the digitalization process and update analytics tools. However, this process is not fast.

Now, data collection and management are a usual business practice. Despite this, there is a high probability that the data is scattered, fragmented, and uncleaned. Inaccurate data and flawed data management can play against business and hinder effective decision making and development. Therefore, this leads the company to poor performance indicators.

Master data management (MDM) plays an important role in enabling intelligent business processes (providing datasets with the correct structure, hierarchy and management). MDM manages critical data across multiple sources, channels, and departments. Master data management implementation requires a well-defined strategy. Let’s look at several important steps in developing a successful master data management strategy.

  1. MDM clear objectives setting

The master data vision must be consistent with the whole business vision. This contributes to the success factors identification and objectives achievement in functional, technical and financial terms. First, the MDM economic model should answer the questions “Why?”, “How?”, “Who?”. This will help identify business pains and data problems. Solving these problems at early stages ensures all business stakeholders support and approval.

  1. Focus on a holistic approach to master data management

Using a multiphase approach to an MDM strategy can be more effective by working with a minimal set of phase objects and scaling it into the next phase. Ignoring such a detailed model when building an MDM solution further can lead to the master data creation from isolated and disparate sources.

  1. Determining the most relevant implementation style in accordance with the existing IT architecture

Companies should clearly define their target architecture, existing technologies, and select a system integrator. Effective MDM technology must support real-time analytics and operational processes to align with the overall the organization’s IT architecture and ecosystem.

  1. Data management rules defining

Business owners need to manage data across all processes and departments. The effective master data management process should identify, measure, record and correct data quality problems in the source system. A formal data governance model should include detailed business rules, governance mechanisms, controls, and data compliance.

  1. Implementation with a strategic plan

The strategic plan can demonstrate the steps implementation in accordance with the business objectives. This prevents MDM solutions from failing as a result of structural flaws that damage the entire data system.

  1. Stagewise ROI verification

First, it is necessary to determine the parameters and indicators that determine the data management success throughout the entire life cycle. MDM stakeholders can be from different organization parts and have different goals. In that situation, it makes sense to check ROI in stages. For example, when custom domain is implemented into a strategy, you need to check your ROI in terms of increasing cross-selling, sales, etc.

  1. Tracking results after implementation

The MDM strategy requires analysis before implementation and monitoring afterwards. All employees, company’s management and stakeholders must work together to achieve their business goals.

  1. Regular improvement

All company staff must be trained in how to format, enter, store and access data. Regularly checking configuration, installation, data models, data management tools, hierarchy helps to avoid problems.

More capabilities with real-time data analytics

If we compare modern business to a car – data will be the fuel material for its successful work. Every day companies have to collect, process and analysis a large volume of data and consequently it’s not always possible to make answer instantly. Real-time data analytics remove time lags between information collecting and processing and gives a ready answer here and now. It helps to improve business operations.

Gartner defines real-time analytics as «symbiosis of logic and mathematics that applicable for data to provide understanding and quick effective decision making. Sometimes analytics completes during some seconds or minutes after new data income».

Accuracy and speed are the determinative of data analytics. And modern business space needs such tool for rapidly information providing that helps to minimize costs, cut downtime and improve business decisions.

Real-time data analytics benefits:

1. Quick and effective business decisions

The main task of real-time data analytics is business information rapid receiving that is used for strategy and decisions improving/correcting, problems uncovering and fast response to them. Currently business demands rapid data processing and momentary answers. This tool provides such opportunity.

2. Operatiional processes improving

Real-time data analytics promotes better understanding of the working structure and recourses using. Hence, it allows to plan working processes rationally, reduce costs, track staff performance, identify possible weaknesses and improve operational processes.

3. Focus on customers

Customers are the main element of the banging business. «Satisfied customer = successful business». Reputation of the company creates with the help of customers. And as their needs will be met so the company’s reputation will improve. That’s why improved customer experience will promote business growth and performance. Tracking consumer behavior online and service customizing according to their preferences can be made by using real-time data (for example product design changing). Also real-time data collecting and analysis play an important role in customer interactions in contact centers and allow to know customers’ calls history and reduce the time gap.

4. Errors and frauds minimizing

Reduction of errors amount and operational efficiency increasing directly depends on using of real-time analytics. The ability to evaluate data every second enables organizations to respond to errors and problems in plenty of time. This toll integration into the security system enables early detection of frauds. Regular monitoring of the operational space allows identify suspicious movement, theft and hacking.

5. Agility and revenue enhance

As real-time analytics enables to make more efficient business decisions so business ability is increasing. Real-time data helps to identify and predict problems and risks that ensures smooth functioning of the company. Hence, revenue and market value are growing.

Quick expansion in automation and IoT connections will result in an exponential data growth. Consequently, real-time analytics will be indispensable and useful business tool across all sectors to improve outcomes.

Qlik: Gartner's Magic Quadrant Leader

Recently Gartner has published the annual report where gives information about conditions of analytic platforms and business analytics, market and its trends. According to the report, Qlik has a dominant position of Gartner’s Magic Quadrant for analytics and BI platforms for 11th consecutive year.

«Qlik – is a leader in this Magic Quadrant. It has a strong product vision for ML and AI-driven augmentation».

Qlik’s strengths and benefits that were signed in the report:

Full report you can download HERE

The role of Data Analytics in organizations’ activities

If we’re talking about critical business tools  ̶  without dispute one of them will be data analytics, which works with huge volume of data. It’s too necessary for business to make meaningful insights, but not only collect and analyze data. High quality and proper data analytics project could create a clear picture of your actual point, past point and your future direction of development.   

We’re living in the data era and almost every task is solved by analytics and it doesn’t depend on any kind of answers or business dimension. Recently such tool like analytics was used mostly by huge and profitable companies, that can pay, including payments for data analytics. Now it’s time for common analytics usage. Such popularization is inspired by increasing understanding of analytics value and profits, which we get from making decision after analyzing. Setting hopes on Business Intelligencea major part of organizations already use it needs to focus on improving and optimizing benefits from decision making results. Other companies even don’t have clear analytical strategy and, on this basis, they need to create correct and effective one. Also, you should understand, that preparations and implementations depend on chosen model and it could take 3  ̶  7 months.  

In addition to external tasks solving, like vector of development, designation of decision effectiveness etc., also analytical research assists to solve internal tasks of the company, which is connected with employees’ motivation, resources and time. Statistically, the major part (59%) of companies uses analytics and monetizes in different ways. 

It’s already understandable, that analytics provides a lot of possibilities. In such case, one of the most important skills is a data sorting skill. At first, it’s critical to understand important and relevant components for every particular business. Among numerous questions and tasks can be solved with data analytics are advertising campaign optimization, revenue and spends analysis etc. And the main challenge is clear conception what goals we want to achieve and what kind of tasks will be solved in every situation. 

The COVID-19 situation became a perfect demonstration of data analytics values and benefits. Butch Works and the International Institute for Analytics conducted a survey, where were interviewed 300 analytics professionals around the US. Almost the half of them (43%) confirmed analytics major part in making important decisions for future business existence. 

Aaron Kalb (a Chief Data and Analytical Officer and Co-founder of Alation) mentioned that consequences and losses subject to pandemic will be increased. Moreover, as COVID-19 crushed and turned over each country’s economy particularly and the world economy in common, companies had to make unplanned investments in BI for making solutions and understanding how to work after.  

During last ten years the world has achieved the new figure in terms of data. Every organizations’ work, way of development, business strategy or choice depend on Data Analysis, which can transform it in different ways and change the vector. Meanwhile you always have a possibility to get all necessary data in short order, just in minutes. 

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