business intelligence

Using Qlik promotes effective financial management

Business success depends largely on the work of the finance, accounting and reporting departments. The information provided by these departments must be accurate, reliable and in real time. Qlik helps to solve such tasks as reducing costs, managing risk, increasing profits, increasing transparency of processes and others. The main Qlik’s mission is to transform disparate financial and accounting data into powerful financial analytics.

Cost management

Company expenses optimization is possible due to the unification of disparate data and the provision of more complete and accurate information. It contributes to a deeper study of data on costs, purchases, contracts, which allows you to identify new ways to optimize business activities.

Financial analysis and planning

Qlik provides professionals with sophisticated analytics for forecasting, planning, and budgeting.

Benefits management

The list of financial management tasks includes monitoring income and profit indicators to maintain the company’s financial balance. Qlik’s advanced analytics greatly simplifies this task with improved cash cycle and balance sheet visualizations.

Risk assessment and regulatory compliance

With Qlik tools it is possible to solve complex challenges related to risk and regulatory compliance. These tools provide financial professionals with access to aggregated data from multiple sources to effectively align governance with business priorities, make informed decisions, mitigate risk, and prevent fines and fraud.

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 Analytics for the consumer goods market”

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:

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.

AI provides more capabilities for small and medium business

There is a misconception that Artificial Intelligence (AI) is applicable only to large businesses. Artificial intelligence (AI) is the human thought process imitation for solving problems and making decisions using computers and systems.

However, small and medium businesses can also use AI successfully and benefit from it. Moreover, its integration into the main business processes and functions becomes more accessible, in order to greatly simplify the entire process of use.

The introduction of Artificial Intelligence into smaller companies’ operational processes is practically no different from the process of implementation into a large business. The first and important part of this process is to study the strategy, identify options for effective use of AI to better understand customers and optimize business activities in general.

Questions that need to be answered for a successful AI implementation:

  1. How can AI be used to develop more advanced and customized products?
  2. How and what business processes can be automated using AI?
  3. Is it possible to monetize the received data?
  4. What available data sources are appropriate to use (own or external)?
  5. What are the most effective ways to aggregate and analyze data?

A breakthrough in Artificial Intelligence for small businesses is its availability as a service. This has allowed many small companies to leverage AI, develop their own competencies, infrastructure, and benefit. They can also use the data collected by companies such as Microsoft, Amazon, Google and transform it into analytical information. It’s not necessary to set up own AI and it makes more affordable for small businesses.

Another use case for AI is using an Artificial Intelligence-based service. Companies that use cloud-based tools for accounting, HR, marketing, CRM are already an AI user.

Here are some practical examples of how AI can be useful for small businesses:

The main BI techniques for data processing

One of the main successful business components is Business Intelligence. Data amount generated by companies reaches colossal marks. Almost every company deals with Big Data. Modern technological advances have made it possible to efficiently store and process data to develop new ideas and make decisions.

Despite this, there is a gap between data storage and usage. Small and large businesses possessing a huge amount of data, but effectively use only a small part of it in their business activities. Business Intelligence can help bridge this gap. The need for real-time data processing is constantly increasing. This has led to the emergence of a large number of BI techniques, making data and analytics more accessible to business users.

BI tools help to analyze data, make decisions, understand trends, and identify patterns. At the moment, there are a large number of Business Intelligence methods. Let’s consider the main:

Data processing technology, with the help of which summary information is prepared on the basis of large data arrays structured according to the multidimensional principle. OLAP is an essential BI method for solving analytical problems with different dimensions. Due to its multidimensional nature, this technology enables business users to see data from different perspectives. This, in turn, reveals hidden problems in the processes. OLAP is mainly used for the following tasks: budget planning, financial forecasting, CRM data analysis.

Data is often stored as numbers combined into a matrix. Interpreting the matrix is ​​a major challenge for making informed decisions. Data visualization in the form of charts and tables is an easy and convenient way for analysts and other business users to view data from different angles and make decisions.

This method is the process of analyzing large data amounts to discover hidden and meaningful patterns and data relationships. Corporate storage contains a huge amount of data. An important challenge is identifying relevant data to support effective business decision making.

It is processes set of design, planning, production, sales, approval and preservation of information content. This technology helps companies collect and provide necessary information to succeed in business. Depending on their needs, users can view reports at daily, weekly or monthly intervals.

Analytics is the main tool for any business. With this tool, analysts and business users can deeply understand the data, correctly interpret it and extract value. Analytics are applicable to any area. For example, call centers use speech analytics to track customer sentiment to improve customer service.

The method allows to deploy an IT structure using multiple providers and platforms. The emergence of a pandemic has affected the normal rhythm of many companies. Most businesses have had to move their work to the cloud. This led to the development of cloud computing.

The main process in managing data warehouses, which includes 3 main stages: extracting data from various sources, transformation according to user needs and loading into the data warehouse.

Through mathematical methods of statistical analysis, it is possible to reveal the meaning and reliability of data relationships, as well as to identify changes in human behavior that are reflected in the data.

Public sector uses Qlik and improves its mission realization

Qlik is a provider of analytical solutions not only for private businesses, but also for government agencies. Governments and educational institutions hold a large amount of disparate data. Their processing and correct analysis opens opportunities to improve provided service efficiency and the mission fulfillment.

Tasks that Qlik helps to solve:

reduce costs, streamline national security processes, food quality and safety control, drug quality control, etc. With the help of processes transparency provided by Qlik, the public can freely share the experience of using certain drugs and products, read reviews and make informed choices based on this information.

 improve the health care system performance. With Qlik, clinicians can see and analyze differences in patient outcomes, compare with possible medical protocols, and determine the most effective method for a particular patient. This, in turn, leads to cost optimization and greater patient satisfaction.

increase situational awareness and preparedness. By analyzing external and internal data, Qlik can increase the awareness of employees, prevent and mitigate the impact of situations that threaten the population lives.

quickly react to the situation and predict the results. Rapid response services have the ability to prepare for previously unknown situations. Qlik provides machine learning capabilities to generate multiple scenarios and evaluate likely outcomes.

investigate the crime rate, predict the next crimes and quickly respond to them. This analysis allows to optimize police officers and special units work, to determine the optimal number of participants in the operation and location.

Qlik solutions are also effective for all levels educational organizations. By analyzing student performance in the context of multiple factors (teacher qualifications, curriculum effectiveness, budget, etc.), the achievement gap can be narrowed and improved.

333 Philadelphia schools use Qlik to visualize student performance data on a public site. With embedded analytics, it is possible to analyze data in real time and define the curriculum for each school.

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 “High-tech companies’operations optimization with Qlik”

Big Data – Top 5 characteristics

The modern world is made up of data. The daily amount of data generated is 2.5 quintillion bytes (Google search, online shopping, smartphones usage, pictures, videos review, etc.). Companies’ success largely depends on how well they work with their data.

The term «Big Data» appeared because of data amount increase. But how do you know exactly if the corporate data is big? There are 5 main characteristics that define big data: volume, velocity, variety, veracity, value.

Volume

The first Big Data characteristic is volume. Every minute a huge amount of data is generated, which equates to the amount of data generated from the beginning of time to the year 2000. Data amount that needs to be processed every day reaches terabytes and petabytes. The explosive growth of data has led to the development of new technologies and strategies. For example, tiered data warehouses that provide secure collection, analysis and storage of information.

Velocity

The speed of generating and moving data is the second Big Data characteristic. Any user action on the Internet creates data that must be processed instantly: sending a message, viewing the feed of social networks (Facebook, Instagram), online shopping, etc. Anyone can represent the amount of data by assessing the number of personal actions per day and adding to this actions of people around the world. Therefore, processing speed is a key characteristic of big data.

Variety

Data can be structured, semi-structured and unstructured. The processing algorithm may differ depending on the data type. In addition to structured data, big data also includes unstructured data: text, images, video, voice files, and other unstructured data that cannot be fit into the frames of a regular spreadsheet. At the moment, there are technologies that allow to analyze both structured and unstructured data. This allows to take advantage of all the possibilities that data offers.

Veracity

Veracity is the next characteristics of Big Data. Because it comes from a variety of sources, it is important to understand the entire storage chain, metadata, and context to get accurate information. Reliable data drives effective analytics and business excellence.

Value

Data must be transformed into business value. To do this, it is imperative to develop a data processing strategy that combines goals and data that will help achieve them. Effective analysis helps to understand customer behavior and needs, optimize business processes, improve application performance, and be competitive. Regardless of what purpose the data is used for, it should definitely be useful and work «for the business».

Hight-tech companies' operations optimization with Qlik

The high-tech sector is developing dynamically, new and improved products appear. The level of competition in this segment is high and companies have to find new ways to optimize processes.

Companies implement various innovations in their operations. It is possible to optimize them with flexible real-time analytics. Success in high technology depends largely on the company’s ability to identify and respond quickly to market changes.

Qlik’s high-tech analytics enables the process of combining data from different sources and analyzing it in real time. This allows to quickly solve problems based on data:

 solving problems with planning, forecasting and tracking income and expenses, taking into account the technology industry dynamic development, new product and categories frequent appearance, as well as serious competition (managers, finance department);

 target markets identification and coordination, effective work with leads, analysis of the influence of each team on the deal (marketing and sales team in the B2B segment);

 understanding customer behavior and needs, taking measures to retain customers and attract new ones (marketing and sales teams in the B2C segment);

 the innovation process acceleration by analyzing market research data and trends (innovation and development specialists);

 improving UX by analyzing the process of consumer interaction with the product (product development teams);

 optimization of stocks by ensuring transparency of sales and supply chains (production department).

Examples of using Qlik:

Lenovo: Leveraging Qlik has enabled the company to create a single source of truth and self-service analytics. This allowed the team to make efficient and fast data-driven decisions;

DocuSign: Using Qlik, the company was able to transform and combine data from different sources. With self-service analytics, 80% to 90% of applications are developed by business users;

Samsung: Qlik’s powerful embedded analytics gave employees quick access to sales and inventory data. Visiting commission efficiency has been improved by 20% with Qlik mobile retail apps.

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 provides analytic solution for the manufacturing sector”

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.

Qlik provides analytic solution for the manufacturing sector

The manufacturing sector also needs analytical solutions. Streamline production processes, increase chain flexibility, improve customer experience – Qlik can help to solve these and other challenges.

The real trend of our time is the 4th industrial revolution (Industry 4.0), which manifests complete automation of production. Processes are controlled in real time, taking into account the changing conditions of the external environment. There are 4 main technologies that can revolutionize the game:

Manufacturing enterprises are gradually innovating in their operations. Qlik provides a faster ROI and optimization process by offering innovative ideas.

It is also possible to improve enterprise productivity with Qlik technologies. Overall Equipment Effectiveness (OEE) analysis provides insight into the uptime of each manufacturing segment. Associative technology provides the integration of all key indicators at the level of a specific machine (productivity, quality, availability). As a result, manufacturers can analyze and accurately determine whether performance is improving or deteriorating, as well as its impact on performance.

Visualization of data from different sources (production volume, the percentage of scrap, the total operating time of the equipment, orders volume) makes it possible to conduct a comparative analysis and determine the level of compliance with standards.

Supply chain transparency is the foundation of corporate ecosystem management. Qlik provides direct connectivity to CRM, SCM systems, process control and other internal systems and external data sources. This allows to analyze every part of the chain, get accurate information and set up a flexible and dynamic supply process.

The Sales and Operations Planning (S&OP) process is a set of activities that are aimed at transforming strategic goals into real action. It combines financial goals, tracks actual sale points, backorders, and forecasts. Qlik indexing technology brings all this data together, thereby speeding up and simplifying S&OP processes.

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 “Retail industry is becoming smarter, faster and customer oriented with Qlik” 
GoUp Chat