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The “Business Intelligence vs. Data Science” debate is one of the most popular topics in research, and a hot topic among data scientists.
Data Science For Business Intelligence
A data scientist might argue that BI skills are obsolete, while a researcher might argue that techniques like deep learning and AI will never be useful research tools.
Data Science Vs. Business Intelligence
The problem with these discussions is that they tend to become unproductive debates where people are pressured to choose sides as if it were some kind of zero-sum game.
Instead of arguing about the “correct” or “better” approach, we should talk about how BI and Science Science have many aspects.
Whether you’re talking about data analytics, data science, machine learning, predictive modeling, business intelligence, or any of the “fun” of analytics, they all boil down to one ultimate goal: Using data to make smarter decisions.
Business Intelligence And Data Science
For those of you who appreciate examples, consider the building process. Although everyone supports the same goal, there are actors who each focus on different tasks and apply special skills to achieve them.
For example, designers create vision and creativity, construction workers do foundation and design work, electricians and plumbers take care of water and electricity. electricity goes to the right place, and the designers and manufacturers make the house friendly and attractive.
When you’re building a house, it doesn’t make sense to choose between a plumber and an electrician. Both work directly, and are important in the building process.
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Similarly, it is not useful to choose a side between business intelligence and data science, as both play an important role when it comes to generating insights from data.
The differences are in the types of questions you ask of the data and the types of tools you use to answer them:
Either method should be used. This comes down to the space at hand, and the specific question you are trying to answer.
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In general, the goal of business intelligence is to identify processes and procedures that can be used to obtain clear, actionable insights and solutions.
On the other hand, data science is often used for predictive analysis and analysis, as well as answering questions to help us understand things.
The purpose of Science is to test hypotheses through experimentation and iteration, and to develop statistical models to understand the world around us.
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Although there are some overlaps, data scientists and business intelligence analysts rely on different types of tools to achieve the above goals.
BI professionals often use a “stack” of tools designed to capture, organize, transform, store, analyze and visualize data. This 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 comprehensive BI systems like Power LIVE on Tableau.
Data scientists rely heavily on programming tools and languages like Python, R and JavaScript, which are designed for flexibility and collaboration. These tools excel at handling large amounts of data, identifying complex patterns and relationships that are impossible to detect through visual inspection.
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As the field of analytics evolves, the line between pure BI and data science tools continues to blur. Platforms designed for business intelligence support AI/ML models, and languages like Python play a major role in BI workflows as they become more accessible to non-coders.
Which data also changes. Although the word “data” was reserved for numerical values, now it can be applied to anything that can be analyzed: text, audio, video, image, IoT signals, etc.
Business intelligence tools and systems often deal with structured data sources, such as data tables or relational models with dimensions and metrics (product information, sales records, customer databases, etc.).
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Data science tools are well-suited for handling high-volume, unstructured data because they rely on mathematical and statistical models (vs. human reasoning or visual analysis) to organize data. large, discover complex relationships, and ordinary data sets are transformed into models. -Friendly features.
Last but not least, let’s consider the output or the output associated with business intelligence and data science work.
In the field of business intelligence, these findings are often presentations, reports, dashboards, or tools designed to create data-driven reports, communicate key issues and business recommendations, or provide social media end users for data analysis or media analysis.
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For data science jobs, the outputs are usually statistical or predictive models that are trained and optimized for answering specific questions. This could include a logistic regression model used to predict fraudulent transactions, a decision tree that predicts subscriber churn, or customer segmentation from an unsupervised machine learning model.
If you are an aspiring research professional trying to find your way, remember that there are no hard and fast “rules” here.
A business intelligence analyst can build predictive models to predict profit margins, or write Python code to scrape data from the Internet. Similarly, a scientist can design a Power BI dashboard to track business KPIs, or use Excel for ad hoc analysis.
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My advice? Ignore the signs and create your own path. If you love working with data and looking for a job that challenges and inspires you, you can’t go wrong.
Chris is a research expert and best selling trainer with 10+ years of experience in data viz and business intelligence. Since Maven Analytics was founded in 2014, its studies have been featured by Microsoft, Entrepreneur.com and the New York Times, reaching more than 500,000 students worldwide. Topic: dating. However, how to use this data to find value and create insights from data collection is another story. Business intelligence and data science are two terms that are often used interchangeably when talking about who, what, why and how to work with data.
Although both seem to work with data to solve problems and make decisions, what is the difference between the two? Let’s get back to the basics by getting into the similarities and differences of each when it comes to their primary roles, outputs, and overall role as it relates to decision making. causing data.
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Business intelligence develops and communicates strategic insights based on available business information to support decision making. The purpose of business intelligence is to provide a clear understanding of the organization’s current and historical data. When BI was first introduced in the early 1960s, it was developed as a method of sharing information across business units. Since then, BI has evolved into advanced data analysis techniques, but communication is still at the core.
In addition, BI is more than the methods and methods for analyzing data or answering specific business questions, it also includes the technology behind these methods. These tools, often self-service, allow users to quickly visualize and understand business information.
Since the amount of data is increasing rapidly, business intelligence is more important than ever to provide a complete overview of business information. This provides guidance for decision-making and identification of areas of improvement, leading to greater organizational performance at lower cost.
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Although there is no universally accepted definition of data science, it is generally accepted as a field that includes many disciplines, including statistics, advanced programming skills, and machine learning, to extract possible insights from raw data.
In simple terms, data science is the process of extracting value from company data, often to solve complex problems. It is important to note that data science is still developing as a field and the definition is constantly changing over time.
Data science is the direction in which companies can predict, optimize and optimize their operations. Also, data science can be important to the user experience, for many businesses data science is what allows them to provide personalized and customized services. For example, streaming services, such as Netflix and Hulu, can recommend entertainment options based on a user’s previous viewing history and taste preferences. Subscribers spend less time searching for what to watch and can easily find value among hundreds of offers, giving them a unique and tailored experience. This is significant as it increases customer retention and improves subscriber potential.
Data Science Vs. Data Analytics: The Differences Explained
Collectively, business intelligence and data science play an important role in developing the operational capabilities of any organization. So what exactly is the line between the two? When does business intelligence end and data science begin?
BI and data science differ in many ways, from the types of data they work with to the output and methods. See the picture below for the visual difference between these two common features.
And predict what might happen next. BI works with historical data to determine reaction patterns, while data science creates predictive models that identify future opportunities.
Business Intelligence (bi) & Data Analytics Platform
Business intelligence works with structured data, often stored in data warehouses or data silos. Similarly, data science also works with structured data but mostly with unstructured and semi-structured data,
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