![\"Infograqphic](\"https:\/\/bisk-preprod.go-vip.co\/msu\/wp-content\/uploads\/sites\/3\/2019\/06\/Developing-Business-Solutions-with-Business-Analytics-Cycle-Infographic.jpg\")
<\/a><\/figure>\n\n\n\nAsk the Right Question<\/strong><\/h2>\n\n\n\nAsking questions helps you define what insight you\u2019re trying to\ngain, problem you\u2019re trying to solve or larger business goal you\u2019re trying to\nmeet. Asking the right<\/em> question(s) helps you build the model to collect\nrelevant data. So how do you determine the \u201cright\u201d question to ask? <\/p>\n\n\n\nIf, for example, sales of a particular product are flat and your goal is to increase sales by 10% to 15%, you\u2019d want to be sure you\u2019re processing the right sales data to discover the source of your highest and lowest sales. Who within these markets is purchasing the product and how? Have online sales ticked up? Are in-store or same-store purchases flat or in decline? Then\u2026<\/p>\n\n\n\n
Process the Data to Answer the Question<\/strong><\/h2>\n\n\n\nThe sources and streams of data flowing into your organization are abundant\u2014from email, mobile apps, social networks, e-commerce sites and web traffic\u2014providing information on sales figures, customer engagement, demographic profiles and the overall health of your business. All that data feeds into or is collected and fed into your central data center. Effective data management and programming strategies<\/a> can help you gather it, clean it and apply it to answer your question. <\/p>\n\n\n\n- Gather:<\/strong> Extract data from your database through advanced queries using Structured Query Language (SQL), which supports, extracts, transforms and loads data in preparation for analytics model development.<\/li>
- Clean:<\/strong> Data cleaning or cleansing<\/a> identifies and removes errors, corruptions, inconsistencies or outliers that can affect data accuracy and, ultimately, its functionality. <\/li><\/ul>\n\n\n\n
Visualize and Analyze the Data <\/strong><\/h2>\n\n\n\nNow that you have solid data to work with, it\u2019s time to gain\nintuition and insight through data visualization and analysis. <\/p>\n\n\n\n
Data visualization\u2014presented in statistical graphs, plots, charts\nor other visual context\u2014descriptively summarizes the information so you can\napply the right structured data model for analysis. Specialized analytics\nsystems based on your model can provide the analysis and statistics to start answering\nyour question.<\/p>\n\n\n\n
- Statistics and Algorithms:<\/strong> This can\ninclude correlation analysis, hypothesis testing, and regression analysis to\nsee whether simple predictions can be made.<\/li>
- Machine Learning:<\/strong> Decision\ntrees, neural networks and logistics regression are all machine learning-based\npredictive analytics techniques that can turn data into proactive solutions. <\/li><\/ul>\n\n\n\n
As you go through data analysis, different comparisons can derive different insights (i.e. applying differing machine learning approaches). With enhanced analysis capabilities<\/a>, you can realize new opportunities and develop innovative solutions to your business questions. <\/p>\n\n\n\nValidate the Analysis<\/strong><\/h2>\n\n\n\nReady to ask more questions? Examining \u201cIs it accurate?\u201d, \u201cIs it\nappropriate?\u201d and running \u201cwhat-if\u201d scenarios will help you determine if your\nanalysis is valid or on the right track. <\/p>\n\n\n\n
Statistical analysis, inference and predictive modeling are applied to define and validate target parameters, leading to an optimal solution and model most aligned to your business goal. <\/p>\n\n\n\n
Apply It to the Business<\/strong><\/h2>\n\n\n\nThis is where all that data starts providing solutions to your\nbusiness questions. You\u2019ve deepened your insight and understanding and now can\nstart making business recommendations and taking action based on your data analysis,\nvisualization and models. <\/p>\n\n\n\n
As you begin to measure outcomes and derive new insights, new\nquestions will arise: \u201cWas the action effective?\u201d \u201cIs a different decision or\nsolution needed?\u201d \u201cWhat was the return on investment?\u201d <\/p>\n\n\n\n
Evaluating appropriateness, managing the value creation of your analytics project, and being able to identify and emphasize the success factors are all critical to leveraging your analytics for a competitive advantage<\/a>. <\/p>\n\n\n\nRemember, the business analytics cycle is continual. Your data model will need to be updated as the new insights and questions arise, and as your business needs change with new problems to solve and challenges to meet.<\/p>\n\n\n\n
Michigan State University\u2019s online Business Analytics Certificate programs cover the entire business analytics cycle, equipping you to turn big data into big solutions for your business. <\/em><\/strong><\/p>\n\n\n\n
If, for example, sales of a particular product are flat and your goal is to increase sales by 10% to 15%, you\u2019d want to be sure you\u2019re processing the right sales data to discover the source of your highest and lowest sales. Who within these markets is purchasing the product and how? Have online sales ticked up? Are in-store or same-store purchases flat or in decline? Then\u2026<\/p>\n\n\n\n
Process the Data to Answer the Question<\/strong><\/h2>\n\n\n\nThe sources and streams of data flowing into your organization are abundant\u2014from email, mobile apps, social networks, e-commerce sites and web traffic\u2014providing information on sales figures, customer engagement, demographic profiles and the overall health of your business. All that data feeds into or is collected and fed into your central data center. Effective data management and programming strategies<\/a> can help you gather it, clean it and apply it to answer your question. <\/p>\n\n\n\n- Gather:<\/strong> Extract data from your database through advanced queries using Structured Query Language (SQL), which supports, extracts, transforms and loads data in preparation for analytics model development.<\/li>
- Clean:<\/strong> Data cleaning or cleansing<\/a> identifies and removes errors, corruptions, inconsistencies or outliers that can affect data accuracy and, ultimately, its functionality. <\/li><\/ul>\n\n\n\n
Visualize and Analyze the Data <\/strong><\/h2>\n\n\n\nNow that you have solid data to work with, it\u2019s time to gain\nintuition and insight through data visualization and analysis. <\/p>\n\n\n\n
Data visualization\u2014presented in statistical graphs, plots, charts\nor other visual context\u2014descriptively summarizes the information so you can\napply the right structured data model for analysis. Specialized analytics\nsystems based on your model can provide the analysis and statistics to start answering\nyour question.<\/p>\n\n\n\n
- Statistics and Algorithms:<\/strong> This can\ninclude correlation analysis, hypothesis testing, and regression analysis to\nsee whether simple predictions can be made.<\/li>
- Machine Learning:<\/strong> Decision\ntrees, neural networks and logistics regression are all machine learning-based\npredictive analytics techniques that can turn data into proactive solutions. <\/li><\/ul>\n\n\n\n
As you go through data analysis, different comparisons can derive different insights (i.e. applying differing machine learning approaches). With enhanced analysis capabilities<\/a>, you can realize new opportunities and develop innovative solutions to your business questions. <\/p>\n\n\n\nValidate the Analysis<\/strong><\/h2>\n\n\n\nReady to ask more questions? Examining \u201cIs it accurate?\u201d, \u201cIs it\nappropriate?\u201d and running \u201cwhat-if\u201d scenarios will help you determine if your\nanalysis is valid or on the right track. <\/p>\n\n\n\n
Statistical analysis, inference and predictive modeling are applied to define and validate target parameters, leading to an optimal solution and model most aligned to your business goal. <\/p>\n\n\n\n
Apply It to the Business<\/strong><\/h2>\n\n\n\nThis is where all that data starts providing solutions to your\nbusiness questions. You\u2019ve deepened your insight and understanding and now can\nstart making business recommendations and taking action based on your data analysis,\nvisualization and models. <\/p>\n\n\n\n
As you begin to measure outcomes and derive new insights, new\nquestions will arise: \u201cWas the action effective?\u201d \u201cIs a different decision or\nsolution needed?\u201d \u201cWhat was the return on investment?\u201d <\/p>\n\n\n\n
Evaluating appropriateness, managing the value creation of your analytics project, and being able to identify and emphasize the success factors are all critical to leveraging your analytics for a competitive advantage<\/a>. <\/p>\n\n\n\nRemember, the business analytics cycle is continual. Your data model will need to be updated as the new insights and questions arise, and as your business needs change with new problems to solve and challenges to meet.<\/p>\n\n\n\n
Michigan State University\u2019s online Business Analytics Certificate programs cover the entire business analytics cycle, equipping you to turn big data into big solutions for your business. <\/em><\/strong><\/p>\n\n\n\n
- Gather:<\/strong> Extract data from your database through advanced queries using Structured Query Language (SQL), which supports, extracts, transforms and loads data in preparation for analytics model development.<\/li>
- Clean:<\/strong> Data cleaning or cleansing<\/a> identifies and removes errors, corruptions, inconsistencies or outliers that can affect data accuracy and, ultimately, its functionality. <\/li><\/ul>\n\n\n\n
Visualize and Analyze the Data <\/strong><\/h2>\n\n\n\n
Now that you have solid data to work with, it\u2019s time to gain\nintuition and insight through data visualization and analysis. <\/p>\n\n\n\n
Data visualization\u2014presented in statistical graphs, plots, charts\nor other visual context\u2014descriptively summarizes the information so you can\napply the right structured data model for analysis. Specialized analytics\nsystems based on your model can provide the analysis and statistics to start answering\nyour question.<\/p>\n\n\n\n
- Statistics and Algorithms:<\/strong> This can\ninclude correlation analysis, hypothesis testing, and regression analysis to\nsee whether simple predictions can be made.<\/li>
- Machine Learning:<\/strong> Decision\ntrees, neural networks and logistics regression are all machine learning-based\npredictive analytics techniques that can turn data into proactive solutions. <\/li><\/ul>\n\n\n\n
As you go through data analysis, different comparisons can derive different insights (i.e. applying differing machine learning approaches). With enhanced analysis capabilities<\/a>, you can realize new opportunities and develop innovative solutions to your business questions. <\/p>\n\n\n\n
Validate the Analysis<\/strong><\/h2>\n\n\n\n
Ready to ask more questions? Examining \u201cIs it accurate?\u201d, \u201cIs it\nappropriate?\u201d and running \u201cwhat-if\u201d scenarios will help you determine if your\nanalysis is valid or on the right track. <\/p>\n\n\n\n
Statistical analysis, inference and predictive modeling are applied to define and validate target parameters, leading to an optimal solution and model most aligned to your business goal. <\/p>\n\n\n\n
Apply It to the Business<\/strong><\/h2>\n\n\n\n
This is where all that data starts providing solutions to your\nbusiness questions. You\u2019ve deepened your insight and understanding and now can\nstart making business recommendations and taking action based on your data analysis,\nvisualization and models. <\/p>\n\n\n\n
As you begin to measure outcomes and derive new insights, new\nquestions will arise: \u201cWas the action effective?\u201d \u201cIs a different decision or\nsolution needed?\u201d \u201cWhat was the return on investment?\u201d <\/p>\n\n\n\n
Evaluating appropriateness, managing the value creation of your analytics project, and being able to identify and emphasize the success factors are all critical to leveraging your analytics for a competitive advantage<\/a>. <\/p>\n\n\n\n
Remember, the business analytics cycle is continual. Your data model will need to be updated as the new insights and questions arise, and as your business needs change with new problems to solve and challenges to meet.<\/p>\n\n\n\n
Michigan State University\u2019s online Business Analytics Certificate programs cover the entire business analytics cycle, equipping you to turn big data into big solutions for your business. <\/em><\/strong><\/p>\n\n\n\n
- Machine Learning:<\/strong> Decision\ntrees, neural networks and logistics regression are all machine learning-based\npredictive analytics techniques that can turn data into proactive solutions. <\/li><\/ul>\n\n\n\n
- Clean:<\/strong> Data cleaning or cleansing<\/a> identifies and removes errors, corruptions, inconsistencies or outliers that can affect data accuracy and, ultimately, its functionality. <\/li><\/ul>\n\n\n\n