How To Learn Business Intelligence – BI (Business Intelligence) is a set of processes, architectures and technologies that transform raw data into meaningful information that drives profitable business. It is a suite of software and services for transforming data into actionable intelligence and knowledge.
BI directly impacts the organization’s strategic, tactical, and operational business decisions. BI supports fact-based decision making by using historical data rather than assumptions and gut feelings.
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BI tools perform data analysis and create reports, summaries, dashboards, maps, graphs, and tables to provide users with insight into the nature of operations.
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Step 1) Raw data is extracted from company databases. Data may be distributed across many different systems.
Step 2) The data is cleaned and transformed in the data warehouse. The table can be linked to form data blocks.
Step 3) Using the BI system, the user can ask questions, request specific reports, or perform any other analysis.
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As a result, adding a new product line or changing a product price could increase revenue in a BI system query that would be run for the product area.
Similarly, in the BI system query that could be made would be the number of new customers added due to the radio cost change
Correspondingly in the OLAP system query that could be done, it would be possible for changes in the customer profile to support higher product price support
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A hotel owner uses a BI analytics program to gather statistical information about average occupancy and room rates. Helps to find the total revenue generated for each room.
It also collects market share statistics and business survey data for each hotel to determine its competitive position in various markets.
Analyzing these trends year by year, month by month and day by day helps management to offer room rental discounts.
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Bank offers branch managers access to BI applications. Helps branch managers determine who are the most profitable customers and which customers they should work on.
Using BI tools frees IT staff from the task of creating analytical reports for departments. It also gives faculty staff access to a richer data source.
The data scientist is a statistician who always has to dig deep into the data. The BI system helps them gain new insights to develop unique business strategies.
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Business intelligence users can be found throughout the organization. There are mainly two types of business users
The difference between the two is that a power user has the ability to work with complex data sets, while the average user’s requirement will have them use dashboards to evaluate pre-defined data sets.
With a BI application, it is possible for companies to generate reports with one click, thus saving a lot of time and resources. It also enables employees to be more productive in their activities.
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BI also helps improve the visibility of these processes and allows you to identify any areas that need attention.
The BI system assigns responsibility in the organization as there must be someone who should be responsible and take ownership of the organization’s performance against its set goals.
The BI system also helps decision-making organizations gain a bird’s-eye view through typical BI features such as dashboards and scorecards.
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BI removes all the complexity associated with business processes. It also automates analysis by providing predictive analytics, computer modeling, price comparison and other methodologies.
BI software has democratized its use, allowing even non-technical or analytical users to rapidly collect and process data. This also allows you to put the power of analysis within the reach of many people.
Business intelligence can prove costly for both small and medium-sized businesses. Using this type of system can be costly for routine transactions.
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Another disadvantage of BI is the complexity of implementing the data warehouse. It can be so complex that business technology is difficult to manage.
Like all improved technologies, BI was originally created with the purchasing power of wealthy companies in mind. Therefore, the BI system is still not within the reach of many SMEs.
It takes almost a year and a half to fully implement the data storage system. Therefore, this is a time-consuming process.
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Artificial Intelligence: Gartner’s report indicates that artificial intelligence and machine learning are now performing complex tasks performed by human intelligence. This capability is leveraged for real-time data analysis and dashboard reporting.
Collaborative BI: BI software combined with collaboration tools, including social media and other cutting-edge technologies, enhances teamwork and sharing for collaborative decision-making.
Embedded BI: Embedded BI allows BI software or some of its features to be integrated into another business application to enhance and enhance its reporting functionality.
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Cloud Analytics: BI applications will soon be offered in the cloud and more and more companies will switch to this technology. According to their forecasts, within a few years, spending on cloud-based analytics will grow 4.5x faster. Your browser is not supported. Download another browser to be able to use all Maven features.
The discussion on “Business intelligence vs. data science” is one of the hottest topics in analytics and a common point of contention among data professionals.
A data scientist might argue that BI skills are obsolete, while an analyst might argue that technologies like deep learning and artificial intelligence will never become practical analytical tools.
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The problem with these conversations is that they inevitably turn into unproductive debates, where people feel pressured to pick sides as if it were some kind of zero-sum game.
Instead of arguing about which way is “right” or “better”, we should talk about how BI and Data Science have so many more
Whether you’re talking about data analytics, data science, machine learning, predictive modeling, business intelligence, or any other “trick” of analytics, it’s all about the same end goal: USING DATA TO MAKE SMART DECISIONS.
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For those of you who appreciate analogies, consider the process of building a house. While all are aligned with the same goal, there are individual stakeholders who each focus on specific tasks and use specialized skills to achieve them.
For example, architects guide vision and design, construction workers build foundations and frames, plumbers and electricians ensure water and electricity flow to the right places, and designers and landscapers make the home user-friendly, friendly and visually appealing.
When building a house, it makes no sense to take sides between a plumber and an electrician. Both play distinct and equally important roles in the house building process.
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Similarly, there is no sense in choosing sides between business intelligence and data science, as both play an equally important role when it comes to extracting insights from data.
The difference is in the types of questions you ask the data and the types of tools you use to answer them:
Either approach should be used. This depends on what your goal is and what question you are trying to answer.
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In general, the goal of Business Intelligence is to identify patterns and trends that can be used to derive clear, actionable insights and recommendations.
Data science, on the other hand, is typically used for predictive and prescriptive analysis and answering questions to help us understand what
The goal of Data Science is to test hypotheses through experimentation and iteration and develop statistical models to help understand the world around us.
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While there is certainly some overlap, data scientists and business intelligence analysts tend to rely on different types of tools to achieve the above goals.
BI professionals often use a “stack” of tools designed to ingest, prepare, transform, store, analyze, and visualize data. This typically includes web analytics tools like Google Analytics, database tools like SQL or Azure, ETL tools like Power Query or Alteryx, spreadsheet tools like Excel, and full-stack BI platforms like Power BI and Tableau.
Data scientists tend to rely heavily on programming tools and languages like Python, R, and JavaScript that are designed for flexibility and open source collaboration. These tools excel at handling massive amounts of data and identifying complex patterns and relationships that would be impossible to discern through visual analysis.
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As the analytics landscape evolves, the lines between pure BI and data science tools continue to blur. Platforms designed for business intelligence now support AI/ML models, and languages like Python are playing a larger role in the BI workflow as they become more accessible to non-programmers.
Data is also changing. While the term “data” was once reserved for numerical values, it can now be applied to almost anything that can be analyzed: text, audio, video, images, IoT signals, etc.
Business intelligence tools and techniques are typically designed to handle structured data sources, such as data tables or relational models that contain leading dimensions and metrics (product information, sales records, customer databases, etc.).
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Data science tools are typically better suited to handling high-speed, unstructured data, as they rely on mathematical and statistical models (versus human intuition or visual analysis) to process large amounts of data, identify complex relationships, and model abstract data formats -compatible features.
Last but not least, we compare outputs or outcomes commonly associated with business intelligence and data science projects.
From the business intelligence side, deliverables tend to be visuals, reports, dashboards or tools, designed to create data-driven narratives, communicate key elements
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