Difference Between Business Analytics And Business Intelligence – I’m writing this blog again (with updated images) because I still get a lot of questions about the difference between Business Intelligence and Data Science. I hope this blog will help.
A client recently asked me to explain to his management team the difference between a Business Intelligence (BI) specialist and a Data Scientist. I hear this question a lot and often show Figure 1 (BI Analyst vs. Data Scientist performance chart). , which shows different methods for each)…
Difference Between Business Analytics And Business Intelligence
…and Figure 2 (Business Intelligence vs. Data Science, showing the different types of questions each is trying to answer) in answering this question.
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But these images don’t have the necessary meaning to answer this question effectively – I’m not sure the audience really understands the important difference between what a BI professional does and what a data scientist does. The key is to understand the difference between a BI professional and the goals, tools, techniques, and methods of a data scientist. Here’s an explanation. Business Intelligence (BI) Analyst Engagement process
Figure 3 shows the high-level analysis process that a BI professional uses when interacting with business users.
Step 1: Create a data model. The process begins with creating a data model. Whether you use a data warehouse, a database, an arrow-based approach, a star, a snowball, or a third, a BI professional must go through the data collection process with business users to determine all (or most) of the questions. which business users want to be addressed. In gathering these requirements, the BI expert must identify the first and second questions that business users want to answer in order to create a reliable and scalable data warehouse. Example:
Business Analytics Vs. Business Intelligence
The BI specialist then works closely with the data warehousing team to define and create data structures that support the questions being asked.
Note: The data warehouse is a “schema-on-load” method because the data schema must be defined and created before the data is loaded into the data warehouse. Without a basic data model, BI tools will not work.
Step 2: Defining the report. After the analysis requirements are written in the data model, in the second stage of BI, the BI Analyst uses Business Intelligence (BI) products – SAP Business Objects, MicroStrategy, Cognos, Qlikview, Pentaho, etc. – creating a SQL-based Query for the required queries (see Fig. 4).
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The BI Analyst will use the BI Graphical User Interface (GUI) tool to create a SQL query for selecting metrics and measures; selection of page, paragraph and reference pages; define limits, subtotals and summaries, make special calculations (average, moving average, position, section) and choose selection methods. The BI GUI hides many of the complexities of creating SQL
Step 3: Create SQL commands. After a BI search or business user identifies the report or query they want, the BI tool generates SQL commands. In some cases, the BI expert modifies the SQL statements generated by the BI tool. including special SQL commands that may not be supported by the BI tool.
Step 4: Create a report. Step 4, the BI tool issues SQL commands to the database and creates a corresponding report or dashboard widget. This is an iterative process where a business analyst configures the SQL (or through the GUI). or manually typing SQL statements) to properly configure the SQL query. The BI Analyst can also specify graphical display methods (bar charts, line charts, pie charts) until they get the exact report and/or graph they want (see Figure 5).
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By the way, this is a good example of the power of the circuit under load. This traditional download method helps business users to solve many data problems, who can use GUI BI tools to interact with and explore the data (for example, self-build BI).
In short, the BI method relies heavily on a database (schema at boot time) that allows users to ask additional questions quickly and easily if the desired data is already in the database. If the data doesn’t exist in the data warehouse, adding the data to the existing data warehouse (and creating all the processes to support ETL) can take months.
Step 1: Define the hypothesis to be tested. Step 1 of the Data Scientist process begins with the Data Scientist identifying the predictions or hypotheses they want to test. Again, this is the result of collaborating with business users to understand the key sources of business differentiation (such as how the organization delivers value) and then consider data and diversity that can drive performance.
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Productivity. This is where the aVision Workshop approach can add value by fostering collaboration between business users and data scientists to identify the source of data.
Step 2: Data collection. In the second stage of the Data Science process, the expert collects relevant and/or interesting data from many sources – mostly inside and outside the organization. The data ocean is a great way to do this because a data scientist can take any data, look at it, see if it has value for theory or prediction, and then decide whether to include that data in a predictive model or discard it. .# FailFast #FailQuietly
Step 3: Create a data model. In step 3, the data scientist identifies and creates the schema needed to address the hypothesis being tested. A scientist cannot define a schema until they know the concepts they are testing AND know what they can use to build their analytical models.
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Note: This schema-on-query method is very different from the traditional schema-on-load method of the database. A scientist cannot spend months trying to bring all the different sources of data together into a single data model. Instead, the data scientist will determine the schema as needed based on the data used in the analysis. A scientist may iterate through different versions of the scheme until he finds a scheme (and analytical model) that fully fits the hypothesis being tested.
Step 4: Check details. Part 4 of the Data Science process uses data visualization tools to find correlations and other interesting data. Data visualization tools such as Tableau, Spotfire, Domo, and DataRPM[1] are excellent tools for data scientists. examine the data and identify the variables they would like to test (see Figure 8).
Step 4: Create and edit analysis models. This is where real data science begins, where a data scientist starts using tools like SAS, SAS Miner, R, Mahout, MADlib, and Alpine Miner to create analytical models. It’s true. Science baby!! During this time, the data scientist will explore different analysis methods and algorithms to try to create more predictive models. As my data scientist friend Wei Lin shared with me, this includes the following algorithmic steps:
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Markov Chain, Genetic Algorithm, Geofencing, Individual Modelling, Propensity Analysis, Neural Network, Bayesian Reasoning, Principal Component Analysis, Singular Value Decomposition, Optimization, Linear Programming, Non-Linear Programming, etc.
All in the name of trying to calculate causation! I don’t recommend trying to win a chess game against these guys.
Step 5: Check model fit. At stage 5 in the data generation process, the researcher will try to ensure that the model is appropriate. The goodness of fit of a statistical model refers to how well the model fits the observations. Several analytical methods will be used to determine fit, including the Kolmogorov-Smirnov test, Pearson’s chi-square test, analysis of variance (ANOVA), and the confounding (or error) matrix.
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My point is not that business intelligence and schema loading are bad, but data science and the schema you want is good. The truth is that they deal with different types of issues. These are different methods designed for different environments and used at different stages of the evaluation process. In the BI process, you first need to create a schema that should support different queries for different business processes. Therefore, the data model must be comprehensive and comprehensive, which means that it is carefully designed. Think about the benefits of manufacturing. In the data science process, the schema is built to fit the hypothesis being tested, so the data model can be created quickly and at a low cost. above. Think of a special character.
The process of data science is closely related; The more experts involved in the project, the better the final example will be. And perhaps most importantly, using business users throughout the process ensures that data scientists are focused on uncovering insights that bypass S.A.M. test – strategic
(where the value of rational action is greater than the cost of rational action). Home » Technology » IT » Database » What is the difference between business intelligence and business intelligence?
Business Intelligence Vs Data Science: The Truth Behind The Difference
The main difference between Business Intelligence and Business Analytics is in their objectives. Business Intelligence is the technology and techniques for analyzing the past and providing historical, current and forecasted business trends. While Business Intelligence is a set of skills, technologies and practices to learn and gain insights and operations from the past business to better prepare for the future business.
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