Difference Between Analytics And Business Intelligence – I’m reposting this blog (with updated graphics) because I still get a lot of questions about the difference between business intelligence and data science. I hope this blog helps.
I recently had a client explain to the management team the difference between a business intelligence (BI) analyst and a data scientist. I hear this question often and usually resort to showing Figure 1 (Bi Analyst vs. Data Scientist Traits Chart, which shows the different behavioral approaches for each)…
Difference Between Analytics And Business Intelligence
…and Figure 2 (business intelligence vs. data science, showing the different types of questions each tries to answer) in response to this question.
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But these slides lack the context needed to satisfactorily answer the question – I’m not sure the audience really understands the underlying difference between what a BI analyst does and what a data scientist does. The key is to understand the differences between the goals, tools, techniques, and approaches of a BI analyst and a data scientist. Here is the explanation. Business intelligence (BI) analyst engagement process
Figure 3 illustrates the high-level analysis process that a typical BI analyst uses when interacting with business users.
Step 1: Create a data model. The process begins with the creation of an underlying data model. Whether you use a data warehouse or a data mart or a hub-and-spoke approach, whether you use a star shape, a snowflake shape, or a third canonical shape, a BI analyst must go through a formal requirements gathering process with business users. Identify all (or at least the majority) of questions business users want answered. In the process of gathering these requirements, the BI analyst must identify the first- and second-level questions that business users want to answer in order to build a robust and scalable data warehouse. For example:
Important Difference Between Business Intelligence And Data Analytics
BI analysts then work with the data warehouse team to define and build the underlying data model that supports the questions being asked.
Note: The data warehouse is a “schema-on-load” approach because the data schema must be defined and created before data can be loaded into the data warehouse. Without an underlying data model, BI tools won’t work.
Step 2: Define context. Once the analytical requirements are translated into a data model, the second step in the process is where the BI analyst uses a business intelligence (BI) product – SAP Business Objects, MicroStrategy, Cognos, Qlikview, Pentaho, etc. – To create SQL-based queries for desired queries (see Figure 4).
Business Analytics Vs Business Intelligence
The BI analyst will use the graphical user interface (GUI) of the BI tool to create SQL queries by selecting measures and dimensions. Selection of page, column and page descriptors. Specify limits, subsets and sums, create special calculations (average, moving average, rank, share) and select sorting criteria. The BI GUI hides much of the complexity of building SQL
Step 3: Create the SQL statement. Once a BI analyst or business user defines the desired report or query request, the BI tool generates SQL statements. In some cases, BI Analyst will modify the SQL statements generated by the BI tool to include unique SQL statements not supported by the BI tool.
Step 4: Create a report. In step 4, the BI tool issues SQL commands to the data warehouse and generates the corresponding report or dashboard widget. This is a highly iterative process where the business analyst will modify the SQL request (either using the GUI or manually coding the SQL statement) to improve it. BI analysts can also specify chart rendering options (bar charts, line charts, pie charts) until they get the exact report and/or graphic they want (see Figure 5).
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By the way, this is a good example of the power of schema-on-load. This traditional schema-on-load approach removes inherent data complexity from business users who can then use and explore GUI BI tools for easy interaction. Data (think self-service BI).
In summary, the BI approach relies heavily on a pre-built data warehouse (schema-on-load), which allows users to ask the next question quickly and easily – because the data they need is already in the data warehouse. If the data is not in a data warehouse, adding data to an existing warehouse (and creating all the supporting ETL processes) can take months to complete.
Step 1: Define the hypothesis to be tested. The first step in the data scientist process begins with identifying the hypothesis or hypothesis that the data scientist wants to test. Again, this is the result of working with business users to understand key sources of business variation (eg, how the organization delivers value) and then brainstorming data and variables that might work better.
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Of performance. This is where the Avision Workshop process can add significant value to enhance collaboration between business users and data scientists to identify data sources.
Step 2: Collect data. Step 2 of the data science process is where the data scientist gathers relevant and/or interesting data from multiple sources – ideally both internal and external to the organization. A data lake is a great approach to this process, because data scientists can capture the data they want, test it, determine its value based on a hypothesis or hypothesis, and then decide whether to include that data. Throw them away. #FailFast #FailQuietly
Step 3: Create the data model. Step 3 is where the data scientist defines and creates the plan needed to address the hypothesis being tested. A data scientist cannot define a schema until they know the hypothesis they are testing and what data sources they will use to build their analytical model.
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Note: This “schema on query” process differs significantly from the traditional “schema on load” process of data warehouses. A data scientist doesn’t spend months integrating all the disparate data sources together into a formal data model. Instead, data scientists will define size as needed based on the data used in the analysis. A data scientist will iterate through several different versions of the schema until they find a schema (and analytical model) that adequately answers the hypothesis being tested.
Step 4: Explore the data. Step 4 of the data science process leverages advanced data visualization tools to uncover correlations and outliers in the data. Data visualization tools such as Tableau, Spotfire, Domo, and DataRPM[1] are great tools for data scientists to explore data and identify variables they want to test (see Figure 8).
Step 4: Build and refine the analytical model. Step 4 is where the real data science work begins – where the data scientist starts using tools like SAS, SAS Miner, R, Mahout, MADlib and Alpine Miner to build analytical models. This is real science baby!! At this stage, data scientists will explore various analytical techniques and algorithms to try to build the most predictive model. As my data scientist friend Wei Lin shared with me, it involves some of the following algorithmic techniques. :
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Markov Chains, Genetic Algorithms, Geological Fencing, Personalized Modeling, Trend Analysis, Neural Networks, Bayesian Reasoning, Principal Component Analysis, Singular Value Decomposition, Optimization, Linear Programming, Non-Linear Programming and more.
All in the name of trying to measure cause and effect! I do not suggest trying to win a game of chess against such a person.
Step 5: Verify the fit is correct. Step 5 in the data science process is where the data scientist will attempt to explore the appropriateness of the model. The goodness of fit of a statistical model describes how well the model fits a set of observations. Several different analytical techniques will be used to determine goodness of fit, including the Kolmogorov-Smirnov test, Pearson’s chi-square test, analysis of variance (ANOVA), and analysis of variance (or error) matrices.
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My point is not that business intelligence and schema-on-load are bad and data science and schema-on-query are good. They solve different types of problems. They are different approaches, intended for different contexts and used at different stages of the analysis process. In the BI process, the schema must first be created, and it must be designed to support a wide variety of queries across a wide range of business functions. Therefore, the data model must be extensible and extensible, which means that it is robustly designed. Consider product quality. In a data science process, a schema is created only to support the hypothesis being tested so that the data can be modeled quickly and with less overhead. Consider ad hoc quality.
The data science process is highly collaborative. The more subject matter experts involved in the process, the better the resulting model. And perhaps more importantly, engaging business users throughout the process ensures that data scientists S.A.M. Focused on uncovering transcendent insights. Test – Policy
(where the value of acting on the information is greater than the cost of acting on the information). Home Data Science Tutorials Data Science Tutorials Head Difference Tutorials Business Intelligence vs Business Analytics – Which is Better
State Of Business Intelligence And Predictive Analytics
Business Intelligence is a process that includes technologies and strategies used by business enterprises to analyze existing business data that provides past (historical), current and predictive events of business operations. Business analytics is the process of technologies and strategies used to explore and extract insights and performance from past business information to achieve successful business planning for the future.
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