Business intelligence (BI) is the set of strategies and technologies that companies use to analyse business information data. Common BI strategies include reporting, online analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining and analysis, and predictive or prescriptive analytics.
If you work in or run a company, you have probably heard of Business Intelligence (BI) and use it in your work routine. Practically all companies already use it in all areas and business units, especially during the decision-making process.
BI has become the pillar on which all the foundations of a business are built. In an increasingly competitive market, making informed decisions based on quality data is crucial to ensure business growth and differentiate from the competition.
The term "business intelligence" gives us a clear idea of what the term means. In simple terms, it is the ability of companies to obtain valuable information for their business.
In more concrete terms, business intelligence refers to the ability to transform information into useful knowledge that enables an organisation to make better decisions, identify business opportunities and optimise its operations, processes and tasks.
Despite appearing to be a modern concept, the term "business intelligence" first appeared in 1865 in the "Cyclopaedia of Commercial and Business Anecdotes," published in the United States. The application of business intelligence began in the 1960s when it was used to describe a system that allowed information sharing between companies. In the 1980s, with the emergence of the Internet, business intelligence became associated with technology and computer models.
In the 1960s and 1980s, business intelligence was already being used to support business decision-making, although in a more rudimentary and less efficient manner than today. In the 1960s, companies collected information about their competitors to predict market behavior and adjust their offerings. In the 1980s, BI began to develop in a technological and digital environment, but it was still far from the level of data correlation and IT integration that is common today.
The popularization of the BI we know today began in the 1990s with the emergence of Business Intelligence tools. However, in the 1990s, access to Business Intelligence was not easy as the tools were difficult to use and required IT specialists. With the beginning of the new millennium, the market and software providers recognized the potential of BI and analytical tools, leading to an increase in the availability of Business Intelligence applications and software. With the increase in supply came improvements as providers created more intuitive, user-friendly, and accessible BI tools for non-technical users.
In the last decade, the application of Business Intelligence in businesses has advanced so much that business intelligence is now essential for business operations. In this context, the options, tools, and Business Intelligence systems have multiplied and improved exponentially.
Although technology has advanced, and we now generate more data than ever, the challenge lies in filtering and processing relevant information. Data overproduction has made it difficult to determine which data is useful and which can be discarded. Furthermore, the task of extracting value from data has become more complex due to the amount of information available.
Transforming collected data into valuable information has also become more challenging compared to the past. The vast amount of information available, from data assets to data sources, systems, and data repositories, makes it difficult to understand and draw conclusions from the information. Therefore, business intelligence requires data processing strategies and processes such as interoperability, data and system integration, and data governance, among others.
It is clear that obtaining business intelligence requires complex processes that are closely related to technology and data. Today, you cannot talk about business intelligence without talking about data analytics.
Data analytics is the foundation of business intelligence. Data has become the key input for organisations to generate knowledge and valuable information, and data analytics is the process by which companies transform data into information and then into insights.
However, technological progress has led to business intelligence going beyond data analysis and now includes more complex processes such as data mining, branches of artificial intelligence such as machine learning or deep learning, among others. The possibilities for companies to generate intelligence are practically endless.
Ultimately, all of this leads to a common goal: using data to make more informed (data-driven) decisions, optimise business strategies, generate opportunities, drive continuous progress, solve problems that affect productivity, and adapt as quickly as possible to market and customer changes.
Business intelligence is a long-term project that starts with data collection, continues with data analysis and ends with data visualisation and presentation of information in dashboards, reports or other interactive reporting and visualisation systems.
We transform data into knowledge to help companies make better decisions. Our team of experts works with Big Data, Big Data analytics, dashboards, key performance indicators (KPIs), data mining, reporting, data warehousing and data integrations. We use Bismart Data Analytics and Business Intelligence to solve your toughest business challenges.
With Big Data, Internet of Things and machine learning technologies, we want to provide companies with different types of real-time tools to improve decision making through data. We want to use technology to make the world a better place. That's why we encourage businesses to use it to its full potential, help organisations make better decisions and work with local governments to improve people's quality of life.
Business intelligence can take many forms. Here are some solutions developed with BI:
Smart Destination is a mobile APP that uses the latest technology on the market on Big Data, Internet of Things, Machine Learning and Stream Analytics.
Smart Destination is able to capture, process and correlate the large amount of live information generated throughout the city where the user is located. Smart Destination provides an optimal route through the city in real time to give you the best experience and the best holiday of your life.
It analyses large amounts of data from multiple and complex information sources:
Using Big Data technology, Internet of Things, Stream Analytics and machine learning, the app proposes, in real time, a smart route in the destination city to enjoy a tailor-made holiday. The user only has to enter their tastes, budget and availability. The application then takes care of creating the route that optimises their time and budget and meets their expectations to the maximum.
We collect live information that is generated throughout the city:
All this, in terms of correlating data from the city's sensors, the Open Data platform, booking portals, the City Council's internal systems, POI information systems, social networks and much more data of interest to tourists.
We analyse the information to help Smart Cities to make decisions, as well as in their process of transformation into more efficient, coherent, innovative and self-sufficient cities.
This application enables better management of the tourism sector, as it helps authorities to offer a better service to citizens, and to improve the management of public services, such as: mobility, accessibility and transport, infrastructures, security, emergency services, cleaning, etc.
The application makes it possible to distribute the influx of tourists in the different points of interest offered by the city, deconcentrating those that may become collapsed at any given time.
Bismart Face and Emotion Recognition is based on Microsoft Cortana Analytics Suite, a leader in Advanced Analytics. Through artificial intelligence, business applications are improved, evolving from simple descriptive analysis to prescriptive recommendations.
Emotion Recognition software starts from an image and returns trust to the user, through a set of emotions for each face and image.
The emotions detected are anger, contempt, disgust, fear, happiness, neutrality, sadness and surprise. These emotions break through cultural barriers and can be universally communicated through facial expressions. Bismart Face Detection software identifies gender and age, as well as checking whether two faces belong to the same person or not.
This powerful software includes Microsoft Cortana, a personal digital assistant that allows you to interact with this intelligent machine in a natural way.
Medic Mirror is a mirror for your home that you can interact with to get a quick diagnosis and advice on how to treat common illnesses. You can interact with the mirror by facial recognition with the built-in camera, by voice or touch to answer the questions in the questionnaire.
The camera recognises the patient's age, gender and emotional state to start with and, using a questionnaire, can find the area and cause of the pain or illness. Depending on your answers the questions will change to reflect the most likely diagnosis.
Medic Mirror is equipped with a variety of tools and devices to help better understand the patient's condition and prescribe the best possible treatment. Using this equipment, Medic Mirror can measure Body Mass Index, blood pressure, glucose levels and so on.
With this data Medic Mirror will provide you with useful information to treat the disease you are suffering from. If the situation requires it, you can even call the emergency services from the mirror itself if you need to go to the emergency room.
When Business Intelligence (BI), dashboards and KPIs are mentioned, it is common to associate them with customers, profitability and sales in the business environment. However, there is an equally relevant area that should not be overlooked: the public sector. In this context, the language and approach is different, focusing on citizens, productivity and service improvement.
Business intelligence plays an important role in public services. In both the business and public sectors, there are important common aspects: process optimisation, cost reduction and the ability to anticipate future needs.
In both cases, we have an increasing amount of data every day and it is crucial to be able to anticipate what citizens really need. The key is to correlate citizens' demand with the current supply of services, taking into account demographic trends, economic activity and mobility.
We have carried out numerous projects in the public sector, applying our extensive knowledge in Business Intelligence, Big Data, data integration, and machine learning. We have some really interesting examples, such as the project we conducted for the city of Berlin. Using predictive analytics, we created dashboards that allowed us to predict the needs of educational centers. To do this, we combined data from urban planning, geographical information, population densities by areas, updated census data, the number of enrolled students, existing and planned educational centers, and even population growth estimates up to the year 2025. Thanks to our dashboards, those responsible for the education sector can not only respond to potential educational demands by neighborhoods but also allocate resources more efficiently, allocating additional funds to existing centers or building new ones.
Another equally relevant project was developed for the Barcelona City Council, focusing on the prevention of traffic accidents. Instead of relying solely on intuitions for the placement of cameras or police checkpoints, it is essential to analyze data to understand the causes of accidents, taking into account all the details of each incident: type of vehicle, dates and times, damages caused (including injuries and fatalities), and whether pedestrians were involved. In fact, the European Union has set a goal to reduce traffic accidents by up to 50% by 2020 compared to the figure from a decade ago.
Finally, the City Council of Terrassa made a strong commitment to technology by implementing Bismart's bigov solution. This solution, based on Microsoft's business intelligence strategy and developed by Bismart, provided several advantages to the government institution.
The implementation of Bismart's bigov solution allowed the City Council to have real-time, centralized, and unified information. The benefits and improvements were immediately apparent to the organization. With this new system, the City Council could rely on clear indicators that engaged the entire organization. Furthermore, they had a tool that facilitated change and innovation by improving decision-making and leadership in this area. Finally, they managed to translate the strategy into an operational level, establishing a continuous process.
The City Council's goal was to address the challenges associated with decision-making and data analysis, and this local administration dashboard platform built on Microsoft Business Intelligence provided them with a significant increase in performance, as well as the opportunity to improve management and optimize costs.
Let's talk about Business Intelligence tools. This term refers to software tools used to analyze raw data within a company. These applications display data that has typically been stored in a data warehouse.
Business Intelligence tools can handle a large volume of unstructured data and help organizations identify, develop, and transform data into new strategic business ideas and opportunities.
Business Intelligence tools have become the way for companies to monitor data and generate business insights. They assist companies in searching for new knowledge, market trends, and lead to quicker and easier decision-making within businesses. Business Intelligence tools can harness data and turn it into useful information that companies can benefit from.
The primary goal of Business Intelligence tools is to enable an easier and more valuable way of analyzing this data for business purposes. By identifying new opportunities and developing a knowledge-based strategy, organizations gain a competitive edge in the market, leading to greater business stability.
Business Intelligence tools can also encompass a wide variety of advanced analysis methods, such as data mining, text mining, predictive analysis, and data analytics. Business Intelligence tools were initially leveraged by data analysts and professionals who conducted analyses and produced results through the use of reports.
Business intelligence tools can be divided into a number of key categories:
A spreadsheet is one of the most interactive Business Intelligence tools and helps organisations to analyse and store data in a tabular format. The results are displayed in a file with rows and columns in order to sort and organise the data more easily, while calculating the numerical data.
Its ability to calculate data values by using mathematical formulas makes this spreadsheet a unique Business Intelligence tool.
Reporting and consulting software
These extracts from Business Intelligence tools sort summaries and submit selected data and are then used to generate readable reports for everyone from a variety of data sources.
OLAP: Online Analytical Processing
Business Intelligence OLAP tools allow the interaction of multidimensional data to be analysed from various points of view. The analysis process of OLAP Business Intelligence tools involves three different functions:
Slicing and Dicing is a function that allows users to sort a precise set of data. Slicing also allows the visualisation of data from different viewpoints. Drilling down allows users to navigate through the details. Consolidation is the accumulation of data that can be calculated in several dimensions.
These dashboards are Business Intelligence tools that present summary activities and business results by process. These indicators are intended to monitor the achievement of established objectives and to make the necessary decisions at appropriate intervals and within a limited timeframe. It is a decision support tool for the manager, which allows projecting into the future and forecasting.
Data mining is a set of algorithms from various scientific disciplines such as statistics, artificial intelligence and machine learning, to build models of data, search for interesting structures or patterns according to predefined criteria, extract the maximum knowledge, and then transform it into a comprehensible structure for analysis use.
Data warehousing is a database that helps us to collect, sort, record and store operational database information and thus provide a basis for decision making in business by many methods. Data warehousing is part of the Business Intelligence environment. It is used to store current or historical data and also to create analytical reports from company researchers.
Data visualisation is a general term and image used to understand the meaning of data by inserting it into a visual context such as graphs. It is a Business Intelligence tool that allows us to highlight correlations that are undetectable in text. Data can be displayed and can be recognised more easily thanks to data visualisation tools.
How many times have you heard the phrase that the company has millions of data and doesn't know what to do with it? It is constantly repeated that data has great value, but this is not always true. Without good analytics to extract valuable information, data is of little use. Companies can use business intelligence to gain valuable insights from their data and make more confident decisions. Business intelligence is the use of data to derive decision-useful information. Businesses can consume data in different ways, here are some of them:
1. Embedded BI
Embedded BI is defined as the integration of reports, dashboards and analysis views into an application. The information is displayed and managed in a BI platform and integrated directly into the application's user interface to improve the context and usability of the data. That is, with embedded BI you can access your BI charts and KPIs within your CRM, PMS, CMS or other application, without having to leave it to consult your Business Intelligence software. Using embedded BI reduces the cost and time required to create reports and analysis.
With embedded BI, BI is integrated with the user experience of the application and offers customers an enriched working context and access to information within the applications they already use. In this way, users can make decisions faster and more efficiently with the help of interactive dashboards and integrated reports. In addition, these dashboards and reports can be customised to users' specific needs, combining multiple data streams, as opposed to traditional reporting software.
With the use of embedded Business Intelligence, users can base their decisions on BI as they go about their daily tasks. Embedded BI can also be part of process automation, allowing specific actions to be performed according to parameters set by the user.
2. Data Discovery
Data discovery is a user-driven process of identifying patterns and unusual values in data. It involves bringing together information from different sources and consolidating them into a single source for easy evaluation in real time. With data discovery, the factors influencing a trend once identified can be quickly uncovered.
The user uses visual tools to search for specific elements in a data set. These tools make the process intuitive, easy to use, dynamic and fast. Data visualisation has evolved beyond traditional static reports to include geographic maps, heat maps, pivot tables and more, allowing you to create accurate presentations of your findings.
3. Self- Service BI
Self-service BI allows end users to analyse their data easily, creating their own reports or modifying existing ones without training. For example, if an organisation only requires one report per year, IT resources can be allocated to that task. However, if the organisation has 1,000 employees and each employee requires several reports per day, the IT team will not be able to keep up with the demand.
Ad hoc reports give users the ability to create reports quickly, allowing them to get data analysis in minimal time. End users can analyse their data by dynamically modifying or adding calculation functions to a report. This flexibility lessens the burden on the technical department, freeing up development resources. This allows business users to be in control of their own analytical needs and extract maximum value from both their data and their application. In this way, the IT team manages interactive reports that each end user can filter to find the information they need.
4. Augmented analytics
Augmented analytics provides automation of data analysis through machine learning and natural language processing. This advanced data manipulation and presentation simplifies data to present clear results and provides access to sophisticated tools so business users can make everyday decisions with confidence. Users can move beyond opinions and biases to gain real insight and act on data quickly and accurately.
Augmented analytics solves the problem that many organisations still have with generating knowledge from data. It alleviates the reliance a company may have on its data scientists by automating the generation of insight in an enterprise through the use of advanced machine learning and artificial intelligence algorithms.
An augmented analytics engine can automatically process a company's data, cleanse it, analyse it and turn it into actionable insights for executives or marketers with little or no supervision by a technic.
Data consumption for business use can take different forms and each of them can be used individually or in combination with others. Each specific company, department or situation will require one way or another of analysing data, although the goal of these processes and technologies is similar: to achieve a good basis for making good business decisions and optimising processes within the company.
Business Intelligence tools help companies make informed decisions by providing detailed data and analysis. These programmes enable companies to assess their performance, identify trends, predict future outcomes, analyse customer behaviour and compare data from different sources. By collecting and storing information from a variety of sources, including internal databases, cloud services and external applications, these tools provide data-driven insights that help companies make strategic decisions, improve processes, reduce costs, identify growth opportunities and optimise the customer experience.
Choosing the right business intelligence tool for each company today can be a challenge due to the wide variety of options available in the market. However, most leading technologies offer sufficient capabilities to improve business performance. The effectiveness of a software will depend on the specific needs and characteristics of each company. Therefore, there are many types of tools with unique capabilities and features to meet specific requirements.
To help you choose the one that best suits your needs, here is a comparison of some BI tools, including some of the most effective business intelligence tools and BI software for enterprises.
The increase in the implementation of analytics and business intelligence tools in companies is unstoppable. The number of analytics experts and data consumers is growing at a faster rate than IT resources. Therefore, analytics is evolving into a more user-friendly mode, using automation, natural language processing or voice; and into a wide range of features including augmented analytics with embedded machine learning, augmented alerts and anomaly detections, which are the new trends in the market.
Many companies are ready to fill that need, and these are three of the ones that do it best with their strengths and cautions:
Microsoft Power BI offers powerful self-service analytics features that allow users to explore their data without relying on technical help or specialised knowledge. It offers a wide range of data visualisation options and the ability to ask natural language queries (NLQ), making it easy for users to ask simple questions about their data in their own language without having to use complex SQL queries.
Power BI integrates seamlessly with other Microsoft services such as Excel and SharePoint, allowing companies to have full visibility across all departments in the organisation. Power BI is a comprehensive, easy-to-use platform that allows users to create, analyse and visualise their data, helping them make informed decisions by quickly creating visuals such as charts and graphs of their data sets.
In addition, Power BI offers tools for efficient cross-team collaboration and secure access to confidential information. Some of its best features include interactive visualisations, natural language queries, robust data connectivity and automated alerts that help users detect patterns in their data and make predictions based on them.
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Maximise your Power BI reports and dashboards with our guide!
Tableau is a data visualisation tool that offers a wide range of features to help businesses explore and understand their data effectively.
Some of Tableau's most notable capabilities include:
Intuitive interface: Tableau has an easy-to-use interface that allows users to create engaging and interactive visualisations of their data without requiring advanced technical skills.
Real-time analytics: Tableau can connect to real-time data sources and provides real-time updates to analytics, helping businesses make more informed decisions.
Dynamic visualisations: Tableau offers a wide range of dynamic visualisation options, including graphs, charts, tables, maps and dashboards, allowing businesses to explore their data in an engaging and effective way.
Integration with other tools: Tableau integrates with other enterprise tools, such as Excel and Salesforce, to provide complete visibility across all departments in the enterprise.
Collaborative analytics: Tableau enables teams to collaborate in real time and share insights and visualisations, facilitating data-driven decision making across the enterprise.
These capabilities make Tableau attractive to businesses because they enable them to gain a better understanding of their data, make informed decisions and collaborate more effectively.
Estas capacidades hacen que Tableau sea atractivo para empresas porque les permiten obtener una mejor comprensión de sus datos, tomar decisiones informadas y colaborar de manera más efectiva.
Qlik - Qlik Sense
Qlik is a BI platform that offers powerful data analysis and visualisation capabilities.
Real-time data analytics: Qlik enables large amounts of data to be analysed in real time, giving companies complete, up-to-date visibility into their business.
Flexible data models: Qlik uses an associative data modeling approach that allows users to create links between data from different sources for a more complete and connected view of their data.
Intuitive visualisations: Qlik offers a wide range of interactive visualisations, from charts to graphs to maps, to help users explore and understand their data more effectively.
Integration with enterprise tools: Qlik integrates with popular enterprise tools, such as Microsoft Excel and Salesforce, allowing companies to have complete visibility across all departments in their organisation.
These capabilities make Qlik an attractive platform for companies because it enables them to gain a complete and connected view of their data, make informed decisions, and improve the efficiency and effectiveness of their business processes.
Google Analytics is a free website tracking and analysis tool that offers a wide variety of features to help businesses better understand their online audience and performance.
Some of the most notable capabilities include:
Traffic tracking: Google Analytics allows businesses to see how many people are visiting their website, where they are coming from, what pages they are visiting and more.
Audience analytics: Businesses can learn more about their audience through Google Analytics, including demographics, interests and online behaviours.
Conversion tracking: Google Analytics allows businesses to see how many people are taking specific actions on their website, such as making a purchase or signing up for a newsletter.
User experience analysis: Google Analytics helps businesses understand how their visitors are interacting with their website, including how long they are spending on each page and which pages they are leaving.
In short, Google Analytics' capabilities are valuable to businesses because they allow them to get a clearer picture of their online performance and make informed decisions about how to improve their website and online marketing strategy.
Sisense is an enterprise data analytics platform that provides cutting-edge analytics capabilities for businesses. It offers a variety of data visualisation tools that enable users to explore and understand their data quickly and efficiently. In addition, Sisense offers robust data connectivity that enables enterprises to connect and analyse data from a variety of sources.
Other features include the ability to create custom dashboards, the ability to perform advanced analytics, the ability to integrate with other enterprise systems, and the ability to collaborate in real time with other teams.
Overall, Sisense is attractive to businesses because it offers a complete data analytics solution that enables them to make informed decisions and improve their business performance through the analysis of their data. In addition, its ability to integrate with other systems and collaborate with other teams makes it an attractive solution for businesses of any size.
Tibco Spotfire is a data analysis and visualization tool that offers a wide range of capabilities for enterprises.
Some of its key features include:
Advanced analytics: Spotfire enables users to perform complex analysis and real-time visualizations of large data sets.
Interactive visualization: The platform offers a wide range of interactive charts and visualizations that enable users to effectively explore and understand data.
Data integration: Spotfire allows the integration of data from different sources, enabling a complete and uniform view of the data.
Collaboration: The tool allows users to collaborate and share reports and analysis in real time with other team members.
Security: Tibco Spotfire offers a wide range of security measures to protect sensitive data.
These capabilities make Tibco Spotfire attractive to businesses by enabling them to make informed and efficient data-driven decisions, improve collaboration and workflow, and protect confidential information.
IBM Cognos Analytics
IBM Cognos Analytics es una plataforma de inteligencia empresarial que ofrece una amplia gama de capacidades para empresas. Algunas de sus características clave incluyen:
IBM Cognos Analytics is a business intelligence platform that offers a wide range of capabilities for enterprises. Some of its key features include:
Data Analytics: Cognos Analytics enables users to perform complex analysis and real-time visualizations of large data sets.
Data Visualization: The platform offers a wide range of interactive charts and visualizations that enable users to effectively explore and understand data.
Data integration: Cognos Analytics enables the integration of data from different sources, allowing for a complete and uniform view of data.
Collaboration: The tool enables users to collaborate and share reports and analytics in real time with other team members.
Report automation: IBM Cognos Analytics enables report automation and the delivery of relevant information to the right users at the right time.
These capabilities make IBM Cognos Analytics attractive to businesses by enabling them to make informed and efficient data-driven decisions, improve collaboration and workflow, and automate the delivery of relevant information.
Domo is a BI platform that offers a wide range of capabilities for enterprises. Some of its key features include:
Data integration: Domo allows the integration of data from different sources, enabling a complete and uniform view of data.
Data analysis: The tool allows users to perform complex analysis and real-time visualizations of large data sets.
Data visualization: Domo offers a wide range of interactive charts and visualizations that allow users to explore and understand data effectively.
Collaboration: The platform allows users to collaborate and share reports and analysis in real time with other team members.
Process automation: Domo enables automation of business processes, which saves time and resources and improves efficiency.
These capabilities make Domo attractive to enterprises by enabling them to make informed and efficient data-driven decisions, improve collaboration and workflow, and automate business processes. amplia gama de capacidades para empresas. Algunas de sus características clave incluyen:
Board is a Business Intelligence tool that enables users to visualize, analyze and understand their business data in an intuitive and efficient way. With Board, users can create custom dashboards and reports, perform real-time data analysis, integrate data from different sources and collaboratively share information with their teams. In addition, Board offers a wide range of advanced analytics tools and artificial intelligence algorithms to help users make informed decisions based on their data.
Today, implementing a BI tool in a company is not just about extracting, organizing and storing data to create a list of corporate reports, but about providing the user with impressive, interactive and immediate reports.
If the user wants to know, "Which territory had a lower order conversion rate last month?" they want to be able to ask the question in their own language and get an answer that allows them to see directly what they are looking for and easily navigate to the source of the situation or its secondary effects.
If you also give the user a personalized output, with all the information they need in one place, mobile access if they require it, automatic updates as often as necessary and the ability to evolve or modify what they are seeing to suit their specific requirements, they gain the freedom to explore and discover new perspectives and ultimately make timely, informed and confident decisions in their business.
For all these reasons, data visualization tools are crucial to a business intelligence environment.
Excel is not the first option that comes to mind when we think of data visualization in a Business Intelligence system, but it is the most widely used analytical tool by business users because of its accessibility. There is no other solution that can reach a billion users through tools they already know and use. Today, Excel offers end-to-end standalone Business Intelligence functionality through capabilities such as Power Query, Power Pivot, Power View and Power Map. With the accessibility of Excel, the rigorousness provided by the connection to analytics services and the proliferation of Office 365, the barrier to entry is lowered for companies looking to take advantage of the benefits of business intelligence.
Microsoft Power BI
Power BI, from the multinational company Microsoft, allows us to integrate data from different sources and create a model that relates them for later visualization. We will be able to analyze our information and share it with other users of the company both online and offline, in the office or through mobile devices.
The following components are available for this purpose:
The use of visual objects in Power BI Desktop is one of the easiest ways to generate and share reports with our data. It has many standard visuals that are integrated into reports without programming (graphs, tables, interactive maps, etc.), with an extensive Marketplace maintained by the user community and also has the ability to create our own custom visual objects and package them as a file so they can be imported into any Power BI report we want to generate.
If we also need to transform and model the data obtained from the source before visualizing it, we can run DAX functions to obtain calculated columns and metrics.
Zebra BI is a self-service business intelligence (BI) solution that offers numerous advantages over other data visualization tools. It is compatible with Excel and Power BI, extending the data analysis and visualization capabilities of these tools by enabling additional features such as multi-dimensional charts, segmented tables with custom segments (MTD, YTD, YTG and full year), simplified data modeling, column highlighting in tables and charts, pricing calculations, among others.
Zebra BI is presented as the ideal platform for areas such as finance, marketing and sales, as it facilitates the creation of revenue, profit, expense and loss reports in Power BI, which are easy to use and complete. In addition, it promotes valuable information for business decision making, allowing analysts to gather detailed information and explore in depth every aspect of the business situation and activity. As Zebra BI's own website states, "Zebra BI charts not only let you see if something is wrong or right, but you can see exactly what is happening and why."
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Some of the outstanding advantages and functionalities of Zebra BI compared to other platforms are:
In addition, Zebra BI introduces enhancements and updates from time to time. It recently surprised the BI community with Zebra BI Office, which enables Zebra BI visuals for Excel and Powerpoint.
At Bismart, as a Microsoft Power BI partner, we have been developing technology and business solutions using Power BI for several years. We always strive to stay ahead of the curve and show the best ways to get the most out of this tool. In fact, in a previous post on our blog, we explained how to create a Google Analytics date comparator in Power BI.
Recently, we established a partnership with Zebra BI, a solution designed to meet the needs of data visualization, reporting and data analysis in the enterprise world. Zebra BI acts as a provider of attractive, powerful and easy-to-understand charts for Power BI and Excel, complementing the capabilities of both tools with advanced reporting techniques and functionality not found in Power BI natively. In fact, Zebra BI charts are benchmarked and even used by Microsoft for internal reporting.
Power BI and Excel are among Microsoft's most widely used technologies and are also among the most commonly used tools by businesses.
In many instances, users may think that Excel and Power BI are rival tools, but in reality, they are not used for exactly the same purposes.
Both Excel and Power BI are Microsoft products used for data manipulation and analysis, although each has different capabilities.
Excel, launched in 1985 as part of Office 365, is a program widely known by most people. Whether you are an expert in data analysis or not, you have probably used Microsoft Excel at some point. It is presented as a spreadsheet that organizes data into rows and columns and offers the ability to perform calculations and mathematical formulas easily and quickly. Like Power BI, Excel is also used to convert data sets into visual information.
On the other hand, Power BI is a suite of business intelligence tools, software services, and applications geared towards businesses. It is a platform with a more specific focus than Excel, as it concentrates on data processing in business environments, although it can also be used in other sectors. One of the main advantages of Power BI is its ability to connect to a wide variety of data sources of different sizes, such as Excel spreadsheets, relational and non-relational databases, cloud services, files in various formats, Big Data tools, and web applications. Additionally, Power BI features advanced graphical capabilities and the ability to transform data into engaging, interactive, and easily understandable reports, dashboards, and custom visualizations.
In summary, Excel and Power BI share similarities as both applications allow access to data in different formats and convert it into visual information. They are also regularly updated, incorporating improvements and new features in each version. However, they have distinct characteristics and functions.
It is not appropriate to claim that one tool is better than the other, as their potential depends on the specific needs of each user regarding their data. Excel is a more suitable choice if data manipulation and transformation, searching, calculations, and mathematical formula application, or creating complex tabular reports are required. On the other hand, Power BI is more suitable for working with Big Data, creating visualizations, collaborating as a team, integrating data from multiple sources, and analyzing data to derive insights, conclusions, and data-driven decision-making.
In short, if you are considering which tool to use, it is important to be clear about what actions you want to perform and review the comparison table in this article to determine which of the two is more suitable for your needs.
Pyramid is a product of Amsterdam-based Pyramid Analytics that focuses on Enterprise Analytics. It offers stand-alone analytics services and BI systems.
Pyramid consists of six modules: modeling, discovery, formulation, illustration, presentation and publication. The illustration module is the one that interests us in this article and allows users to create data-driven charts, infographics and text for later presentation or publication. Pyramid has 36 charts that can be extended to more than 150 different visualizations, including:
Reusable content sharable in presentations and publications
Dynamic data-driven text
Composite visualizations with SVG and raster images
Custom visual workflows to illustrate processes.
Tableau is a U.S.-based company that was recently acquired by Salesforce, a giant in customer management software. For several years, Gartner considered it a leader in its Magic Quadrant, but in recent times it has been surpassed by Microsoft.
Tableau is a data visualization tool that allows you to simplify and convert information into easily understandable formats. This tool allows you to create dashboards and worksheets, and perform data analysis quickly and efficiently. In addition, the visualizations that can be achieved with Tableau are clear and accessible even to non-technical users.
With Tableau you can:
Create graphs and dashboards that show KPIs.
Share reports and dashboards with team members who have access to the tool.
Explore data easily thanks to its interactive interface.
Connect to more than 60 different data sources, such as Microsoft SQL Server, Spark SQL, Amazon Redshift or Hadoop Hive Server.
QlikTech is a company founded in Sweden in 1993 and currently headquartered in the U.S. It specializes in business intelligence and business search.
Its QlikSense tool enables non-technical users to visualize and ask questions about data. The goal of QlikSense is to make it easy to ask and answer questions based on graphical representation of data.
QlikSense offers the ability to:
Explore visual views of information through simple interactions.
Ask any question related to data visualization.
Interactive and innovative visualizations with responsive capabilities.
Explore in any direction.
Collaborate and share information gained from visual analysis.
Microstrategy is a technology company based in the United States, founded in 1989. It offers Business Intelligence (BI) and OLAP software.
Its Business Intelligence platform includes interactive dashboards, well-formatted reports, report distribution, among others.
With Microstrategy we can:
Discover new insights through powerful visualizations thanks to its extensive library of charts. Visualize data quickly using maps, charts and graphs. Use third-party visualizations such as D3 or create our own charts with its Visualization Builder and SDK. Create Data Discovery presentations. When choosing a data visualization tool, it is important to consider factors such as its ability to integrate with existing tools, licensing cost, type of users that will use it, scalability, ease of use and the manufacturer's product lifecycle.
Business Objects is a Business Intelligence (BI) tool designed to help companies transform data into meaningful and actionable information. Some of its capabilities include:
Data queries and analysis: allows users to query and analyze large volumes of data with ease.
Reporting and dashboards: allows users to create customized reports and dashboards to effectively visualize enterprise data.
Data integration: provides a platform for integrating data from different sources, including databases, spreadsheets and files.
Process automation: allows automating repetitive processes and tasks, improving efficiency and reporting accuracy.
Collaboration and information sharing: enables users to securely share reports and dashboards with other members of the enterprise and collaborate in real time.
In summary, Business Objects is a powerful tool to help companies make data-driven decisions and improve their business intelligence.
When choosing a data visualization tool, we must consider factors such as its ability to integrate with existing tools, the cost of the license, who will be the users in charge of managing it, its scalability, its ease of use and the product life cycle provided by its manufacturer.
Power BI is a fast tool that allows you to display data with graphs and various visuals. The tool is capable of handling huge databases, but even so, sometimes we can find ourselves waiting minutes for the charts to load.
There are many reasons that can cause poor performance in a Power BI report. Using too many charts, too much data, using uncertified custom charts, or leaving the default options are some examples of practices that can lead to Power BI having to overexert itself to load our report and therefore increasing the time it takes for the machine to load.
A slow Power BI report is not only cumbersome for the end user, but can mean that the load time expires and the report is not displayed. To detect such problems with a Power BI report it can be interesting to ask end users what their experience with the report has been and what their habits are when working with it. In this way, reports can be better tailored to their exact needs.
If, indeed, we detect that our report has speed problems, there are certain best practices that help to lighten the work that the machine has to carry out in order to speed up the processes and, therefore, the loading of the graphics.
Limiting the Use of Charts
Microsoft suggests limiting the number of charts per page. This is because each chart generates at least one query per interactive filter, so a high number of charts can slow down the report's performance.
Using Only the Necessary Data to Convey Information
The more data a chart has to display, the longer it will take to load. This may seem obvious, but it is sometimes overlooked. In some cases, it is not necessary for a chart to contain an abundance of data for the user to understand the message. It is preferable to reduce the data to the minimum required to increase loading speed. If you are concerned that performance improvements may negatively impact the user experience, you can choose to keep a larger amount of data than the user needs but avoid the default option that keeps the entire datasets. Remember: less is more.
Choosing Power BI Custom Visuals with Good Performance
Power BI has a library of custom visuals that are Microsoft certified. This means that Microsoft has conducted rigorous and exhaustive tests to ensure that these visuals have the best performance. Using visuals that do not perform well can affect the overall functioning of the report. In addition to performance improvement, Microsoft-certified custom visuals offer more options than non-certified ones, such as the ability to export them to PowerPoint, for example.
Checking that Custom Visuals Used Have Adequate Performance
Custom visuals not certified by Microsoft do not have the same guarantee of performance. However, Microsoft clarifies that non-certified custom visuals are not of lower quality; they may simply be visuals that do not meet Microsoft's requirements for certification. If you are going to use custom visuals, perform tests to ensure they have the expected performance or consider using visuals that offer better performance.
Reducing the Number of Interactions Between Charts
Charts in a report interact with each other unless specifically configured not to do so. Reducing the number of interactions to the minimum necessary will improve the report's performance.
Limiting the Use of Slicers
Slicers are very useful for quickly understanding which data situation the filters are applied to and for reports primarily used on touchscreen devices. However, their performance is limited, which can affect the overall functioning of the report.
These are just a few examples of practices that can improve the performance of Power BI reports. If the problem persists, there are other measures that can be taken.
These are just a few examples of practices that can improve the performance of Power BI reports. If the problem persists, there are other more technical actions that can be found in Microsoft's best practices manual.
Bismart's team of Power BI experts has created a guide to 21 best practices for Power BI reporting.
Download the guide and improve your Power BI creations!
Here are 10 of the best practices you will find in the book.
In Power BI, the ability to visualize data is invaluable. Visual elements contribute to democratizing data, understanding information, and facilitating data-driven decision-making.
However, it's important to avoid overloading a report with too many visual elements, as this can lead to confusion and slow down the report's performance.
Microsoft's "Power BI Optimization Guide" recommends limiting a report to no more than 8 visual elements and one table per page.
Optimize Interactions Between Visual Elements
When we add visual elements in Power BI, the tool automatically enables interaction between all visual elements on the same page. However, these interactions consume resources and can slow down the report's loading.
How to address this? Reduce the number of automatic queries in the backend and improve the report's performance by deactivating unnecessary interactions.
Use Visual Elements Certified by Microsoft
In Microsoft's AppSource, you'll find a list of visual elements certified by the company that have passed quality tests.
Certified visual elements offer high performance, comply with Microsoft's code standards, and are the only ones that can be exported to PowerPoint or used in email subscriptions.
Evaluate the Performance of Custom Visual Elements
Custom visual elements are generally not verified by Microsoft, which means their performance and loading capacity may be slow. While these elements allow you to tailor data visualization to the specific needs of each company, they can affect the overall report's performance.
If a custom visual element significantly slows down a report, it is recommended to consider replacing it.
How to do it? Menu > Performance Analyzer.
Facilitate User Customization of Visual Elements
To provide a personalized user experience, it's important to enable personal bookmarks and allow users to explore more information through the report's visual elements.
However, it's essential to note that Power BI has a limit of 20 personal bookmarks per report.
How to do it? Menu > View > Bookmarks > Add > Personal bookmark.
Implement Data Governance and Security Measures in Power BI
It's common for companies to share corporate reports with partners and clients. Additionally, if the tool is effectively used at the enterprise level, employees from different departments with diverse needs will work with or query the same report.
In this regard, it's crucial to apply data security and governance measures to the data, assign different roles to users, and securely share reports.
Bismart has developed a set of solutions designed to implement data governance and security measures in Power BI: Power BI Viewer and Power BI Analytics.
Avoid Importing Complete Data Sets
To ensure that a report functions smoothly and is more comprehensible, it's preferable not to import complete data sets. Furthermore, it's recommended to limit the number of tables and reduce their size as much as possible, as long as it doesn't affect the usability of the report.
Use the Hierarchy Function in Slicers Instead of Custom Visuals
If you need to display a hierarchy in slicers, use the built-in function provided by Power BI Desktop rather than custom visual elements.
Limit the Use of Filters
Filters or "slicers" make it easier for users to navigate. However, each filter requires additional queries, which can impact the report's performance and capacity. Therefore, it's advisable to remove filters that are used infrequently.
How to do it? Through the filter pane, you can assess the filters and remove those used less frequently.
Limit Complex Aggregations in Power BI Data Models
If complex calculations and aggregations are necessary, it's recommended to perform them as close to the data source as possible rather than within the Power BI visualization environment. This way, you'll avoid overloading the report and reduce loading times.
By following these tips, you can improve the efficiency and performance of your reports in Power BI while ensuring data security and governance.
By following these tips, you can optimize your Power BI reports, enhance the user experience, and maximize the performance of your data analysis.
The approach to Business Intelligence (BI) known as self-service BI has revolutionized how companies leverage their data and business information. This model allows anyone to analyze and interpret company data without needing to be an expert in information technology.
In the past, only users with advanced knowledge of BI tools could use these technologies and work with their own data. However, self-service BI has democratized data analysis by putting the necessary tools in the hands of anyone to understand and analyze data.
This new possibility has completely changed the game, as data has become the foundation for business decision-making and strategy evaluation. Having data-driven corrective measures is essential to optimize outcomes.
Microsoft Power BI is one of the most widely used business intelligence tools by companies. According to Gartner's latest report on analytical and BI tools in 2023, Microsoft Power BI is the market-leading platform.
One of the main advantages of Microsoft Power BI is its ability to turn data into valuable information and provide insights through a self-service approach. Additionally, companies can choose the type of Power BI service or license that best suits their business needs.
In a world where business analytics, business intelligence, and data utilization are priorities, Power BI stands out as a key tool for companies looking to harness technological and data-driven development to their advantage.
Developing a data-driven culture has become one of the top priorities in the business world. To transform data into business value through a self-service approach, tools like Power BI, which turn data into easily analyzable information, are indispensable.
At Bismart, as a preferred Microsoft Power BI partner, we have been helping other companies for many years to develop self-service BI strategies by adopting Power BI in their corporate environment.
The development of a self-service BI strategy goes beyond the simple technical construction of reports. It requires a comprehensive approach that considers technical criteria, the quality and health of the organization's data, security and data governance policies, as well as usage criteria.
It is important for companies to be aware of the different models available for developing a self-service BI strategy. Two of them are presented below:
This model applies to companies where the majority or all users have the necessary technical knowledge to create their own reports using BI tools like Power BI.
In this approach, the organization outsources the processing, integration, organization, and preparation of datasets for later use in Power BI by internal users. These users leverage the data and create their own reports using the tool. This model is ideal for organizations in which all users have the ability to build their own reports.
For this model to work optimally, it is essential to provide professional training to the internal users of the organization so that they can make the most of Power BI's capabilities.
At Bismart, we offer a type of personalized training for the adoption of Power BI. This training is based on the company's own datasets, allowing for tailored training to the specific needs of users, enabling them to start working with Power BI immediately and with knowledge of the internal content they will be leveraging.
Additionally, this approach also requires the implementation of data governance and security policies to ensure the integrity and confidentiality of information.
Developing a self-service BI strategy is a comprehensive process that must be addressed holistically, considering technical, data, security, and user training aspects. By adopting models like the internal experts' model and leveraging tools like Power BI, companies can enhance their ability to make data-driven decisions in an agile and efficient manner.
This approach is suitable for companies that have a small BI or IT department responsible for creating Power BI reports, and the rest of the company's users have business profiles and only act as consumers of these reports. It is also suitable for companies that do not have users with technical knowledge, as they can outsource the entire report generation and reporting process.
However, one of the main challenges of this approach is the increase in costs. Many organizations incur unnecessary expenses by acquiring multiple Power BI Pro licenses for each business user who only needs to view reports, even though this is not necessary.
Furthermore, this approach requires the development of policies related to the use and exploitation of data sets, as well as policies for the development of data flows and policies for the creation and consumption of corporate reports. These policies are essential to ensure the proper management and utilization of data in the corporate environment.
By adopting the business-focused approach in developing a self-service BI strategy, companies can optimize report generation and information distribution within the organization. While there are challenges in terms of costs and policies, a carefully designed approach can help maximize the value of data and improve data-driven decision-making within the company.
We present to you the e-book "10 Best Practices for Enterprise Adoption of a Self-Service BI Model with Power BI," where you can discover the fundamental steps to develop a self-service BI strategy using Power BI.
This e-book is designed to provide you with a clear and concise guide on how to successfully implement a self-service BI approach in your organization using the powerful Power BI tool. Through these best practices, you will be able to take full advantage of Power BI's capabilities and leverage data analytics in your organization.
By following the steps described in the e-book, you will be able to:
Establish the objectives: Understand the business objectives and define how self-service BI can contribute to their achievement.
Identify key users: Identify the users who will benefit from the adoption of self-service BI and understand their needs and skills.
Assess existing infrastructure and data: Conduct a thorough assessment of the technology infrastructure and data available in your enterprise to ensure a successful implementation.
Define security and governance policies: Establish policies and procedures to ensure data security, integrity and privacy in the self-service BI environment.
Design an efficient data model: Create a robust and scalable data model that enables users to access and analyze information effectively.
Develop interactive reports and dashboards: Use Power BI tools to design intuitive reports and dashboards that provide valuable information in a visually appealing way.
Promote training and support: Provide adequate training to users to take full advantage of Power BI capabilities and offer ongoing support to resolve any issues or questions.
Encourage collaboration and knowledge sharing: Establishes channels of communication and collaboration among users to encourage the sharing of ideas and best practices.
Conduct continuous monitoring and analysis: Monitors Power BI usage, analyzes results and makes adjustments as needed to continuously improve the self-service BI strategy.
Drive a data-driven culture: Foster a data-driven decision-making culture and promote the use of self-service BI as a key tool to drive business transformation.
Dashboards are usually developed by the functional department and tend to be static, while self-service BI is an environment implemented in an organization that allows each user to create his or her own reports according to his or her needs. Although they are different approaches to BI, the current trend is to combine them, i.e. to implement a dashboard environment that allows advanced or selected users to have self-service tools to create their own reports.
In this webinar, basic concepts of business intelligence were explained, such as measures, indicators, and dimensions that are included in a self-service BI environment. Víctor illustrated with examples the value of data analysis through metrics that show what happened and dimensions that explain why it happened.
This translates into a data model that helps us determine what we want to know and why we want to analyze it, such as sales by product, subsidiary, time, etc.
Víctor emphasized the importance of thinking about the end users of the tool to ensure its success and adoption. It is crucial to develop an appropriate working methodology and have a tool that facilitates its implementation. However, the most important thing is to provide continuous support to users.
At Bismart, we have developed a tool called the "Indicators & Dimensions Definition Tool" for this purpose. It provides an environment where indicators and dimensions can be documented during the definition phase. Additionally, it includes a business matrix (Bus Matrix) that displays the relationships between dimensions and indicators.
This tool guides us in the definition process and also serves as a documentation and reference tool. It has a dictionary section and an indicator card where all attributes defined for each indicator can be consulted. Indicators can be filtered by area of interest. The tool also features a dimensions card that allows for filtering dimensions by functional area and viewing associated hierarchies. Furthermore, it shows the data source used to display the data. Finally, the business matrix allows you to see the analysis capabilities, meaning it shows which dimensions can be analyzed for each indicator and vice versa.
During the last few minutes, there was an opportunity for questions and doubts from the attendees, where Víctor clarified that this working approach is applicable to companies of any size. He also emphasized the importance of teamwork when using these tools and explained that the tool is not dependent on a specific BI environment but can be used with the environment already implemented in the organization.
Business intelligence plays a key role in strategic decision making in any organization. It refers to the combination of tools, processes and infrastructures that enable companies to identify and analyze relevant information. It is used to evaluate the company's performance, give employees access to data, have a better understanding of customer opinions, identify areas for improvement, among other benefits.
Business intelligence is the ability to convert data into information and then into knowledge. According to Gartner, the lack of knowledge is the biggest threat to contemporary companies, so having a good business intelligence system is essential.
To organize a Business Intelligence project, the following elements must be taken into account:
Evaluation of data sources: internal or external, accessibility, reliability, quality and integration possibilities.
Transformation of data into information: contextualization, categorization, calculation, correction and integration.
Definition of the knowledge obtained: comparison with other sources, making predictions and interrelationships.
In addition, it is important to keep in mind that the business objective must be the priority and not the technology. It is also essential to have the support of future users and management, to be flexible to changes, to ensure the usability and understandability of the information, to offer easy-to-use transversal solutions, self-analysis options, a good communication and data protection system, to use an agile methodology and to define a change management process.
Project success depends on taking these factors into account and providing not only historical, but also predictive and simulation analytics.
It is common to make the mistake of focusing on defining data extraction, cleansing, analysis and storage tasks in these projects. Before this, it is essential to consider who will use the information and for what purpose.
There are several relevant factors that must be taken into account in any project:
Business intelligence and data warehouses are essential for the proper functioning of modern companies. Both contribute to the digital transformation of companies in terms of data storage, security and exploitation. Today, data analysis is an essential process for business management, so much so that it is difficult to imagine a successful company that does not perform data analysis.
Organizations have always required information to make decisions, but with the advent of Big Data, the amount of information available has multiplied. This makes it essential to have data governance and management processes in place to generate business intelligence.
This is the role of data warehouses and business intelligence. Data warehouses refer to the processes of collecting and storing information, while business intelligence focuses on the analysis and processing of this data to generate insights and support data-driven decision making.
In conclusion, both are critical elements for companies to take advantage of the value of data and transform it into better decisions and differential business strategies.
On the other hand, it is important to understand that a data warehouse is not a common database. The key difference lies in its processing capacity and integration with data sources. Business intelligence and data warehousing are essential elements of an organization's information system.
Companies require space to store their data, but the relationship between data warehousing and BI goes beyond that.
Data fragmentation is an obstacle that makes it difficult to leverage the value of data and transform it into intelligence. Departments store information in incompatible warehouses, preventing integration and knowledge sharing. According to Gartner research, 52% of executives said that fragmented silos prevent them from sharing data, and 33% said their company does not have the right data management technologies in place.
The data warehouse was designed to solve data fragmentation by consolidating data flows into a central repository that is accessible to all members of the organization. In addition, an efficient data warehouse speeds up the loading time for analyzing data, improves data security and contributes to compliance with data protection regulations.
To build a solid business intelligence foundation, the following steps need to be taken using a data warehouse.
The first step in establishing a data warehouse is to determine what data needs to be collected and locate the source of origin. This allows for the transfer of data to the data warehouse or its subcategory, the data mart.
This step is crucial because it requires managers to evaluate their data objectives, identify the necessary data to achieve them, and determine which data assets can be harnessed.
Once the data to centralize and its location have been identified, the Extract, Transform, and Load (ETL) process takes place.
ETL is a critical aspect of the process because it not only extracts the desired information to load into the data warehouse but also cleans and consolidates it to ensure data quality and consistency across all databases, regardless of the source system.
Essentially, ETL is the necessary processing to transform raw data into useful information, ready for use by data analysts, BI consultants, or any other user profile.
Most ETL processes are automated and promote data quality and information governance.
Like all technologies, in recent years, the ETL process has evolved toward a new perspective: ELT, which changes the order of the Extract, Transform, and Load sequences.
After data has been processed and loaded into the data warehouse, it is ready to be analyzed and converted into insights with the help of business intelligence tools. These tools enable users to turn data into valuable information and useful insights for decision-making.
Business intelligence tools include reporting systems like Microsoft Power BI, data visualization platforms, and the creation of dashboards and corporate reports. Leading market tools, such as Power BI, have been designed for users with little technical knowledge to use them and obtain important information for their decisions.
Ensuring that end-users have effective access to the information they need is key to the success of business intelligence and data utilization.
So, do you need a data warehouse to generate business intelligence?
While some companies generate business intelligence without a data warehouse, this approach poses many challenges in terms of performance, time, and costs. Processing the necessary data for generating business intelligence without a data warehouse can overload transactional databases, reduce performance, and increase loading times, delaying the data-to-insight transformation process.
Additionally, the lack of proper infrastructure to integrate data and systems can create other issues.
In summary, transactional databases cannot perform the same work as a data warehouse, and their ability to generate intelligence is limited. This explains why 48% of organizations consider their business intelligence environment "critical" or "very important" for their long-term productivity.