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Business Analytics

Transcript: Introduction to Business Analytics Definition of Business Analytics Business analytics involves the skills, technologies, practices for continuous iterative exploration, and investigation of past business performance to gain insight and drive business planning. It utilizes statistical analysis and other quantitative techniques to interpret data. Key Concepts and Terminology Important concepts in business analytics include 'descriptive', 'predictive', and 'prescriptive' analytics. These terms represent different approaches to understanding data, from summarizing historical data to forecasting future outcomes and recommending actions. Importance in Modern Business In today's data-driven landscape, business analytics is crucial for understanding market trends, optimizing operations, and enhancing customer experiences. Companies leveraging analytics make informed decisions, reducing risks and improving profitability. Different Types of Analytics Business analytics comprises four main types: descriptive analytics, predictive analytics, prescriptive analytics, and diagnostic analytics. Each type serves unique purposes, enabling organizations to analyze data trends, forecast future scenarios, or recommend specific actions based on insights. Historical Overview The field of business analytics has evolved significantly since the 1960s, with early data processing systems leading to today's advanced predictive analytics technologies. The rise of big data and machine learning has transformed how businesses leverage insights from data. Analytical Techniques and Tools Business Analytics Data Collection Techniques What is Descriptive Analytics? Descriptive analytics focuses on summarizing historical data to identify patterns and trends. It utilizes statistical measures to interpret data, providing insights into what has happened in the past and facilitating reporting processes. Common Analytical Tools Understanding Predictive Analytics Several analytical tools are popular among businesses for their effectiveness in data management and analysis. Tools like Tableau, Power BI, and SAS offer user-friendly interfaces, making data visualization and reporting more accessible to users. Predictive analytics employs statistical models and machine learning techniques to forecast future outcomes based on historical data. This method aids organizations in anticipating customer behavior and market trends, improving decision-making. Case Studies of Tools in Action Primary Data Collection Secondary Data Sources Real-world applications of analytical tools illustrate their value. For instance, Netflix uses predictive analytics to tailor content recommendations, enhancing user satisfaction and retention, while retailers employ data analysis to optimize inventory management. Primary data collection involves gathering new, firsthand data directly from the source. Techniques include surveys, interviews, and experiments, ensuring relevance and specificity to research objectives. The Role of Prescriptive Analytics Secondary data sources provide pre-existing data collected by others. Common sources include government reports, academic papers, and industry publications, offering insights without the time and cost of primary research. Prescriptive analytics goes beyond predicting future trends by recommending actions based on data analysis. It utilizes optimization and simulation algorithms to aid businesses in decision-making, ensuring efficient resource allocation and strategy formulation. Data Quality and Integrity Data Cleaning Methods Data quality and integrity are essential for reliable analytics. Factors include accuracy, completeness, consistency, and timeliness, all of which significantly influence decision-making outcomes. Data cleaning methods ensure that datasets are free from errors and inconsistencies. Techniques include removing duplicates, handling missing values, and standardizing formats, all crucial for accurate analysis. Ethical Considerations in Data Collection Implementation and Impact Ethical considerations in data collection ensure privacy, consent, and transparency. Organizations must comply with data protection regulations and establish trust with participants to maintain integrity and credibility. Measuring ROI of Analytics Initiatives Steps for Implementing Business Analytics Measuring ROI involves quantifying the benefits of analytics initiatives against their costs. Metrics can include increased revenue, improved efficiency, and enhanced customer satisfaction, ensuring that analytics investments align with corporate goals. Key steps for implementation include defining business objectives, identifying relevant data sources, selecting appropriate tools, and developing a skilled analytics team. Successful implementation requires strategic alignment and continuous assessment of analytics outcomes. Future Trends in Business Analytics Challenges in Adoption Emerging trends include the rise of artificial intelligence in

Business Analytics

Transcript: Data set #3 Compute the Sample “variance”. Add results and divide by 1 less the sample size 6153336.67 434291.5441 3491262.354 13363396.74 1188.732484 23443476.04 / (5-1) = 5860869.01 This is your sample variance. Question- In 2012 how much money per employee did enterprise spend in hiring? *Root cause analysis 5 whys Why Does enterprise spend money hiring people? Why does enterprise want to hire the best candidate? Why does enterprise care about retention? Why is management recognized for retention efforts? *Employees possess business knowledge (both internal and external)- that can benefit the company as a whole. An example of this is our “we’ll pick you up” slogan. Supporting decision(s) HR Spending decisions, forecasting decisions Deployment Who was most calibrated? Formatting Recruiting Cost Ratio The only difficult part of data preparation was formatting the data in a way that I could easily identify the costs involved. Part of my data required alteration so as to properly analyze it. Data Analysis Apply for this job: Job ID: 2012-73826 Location: US-MO- Area: St. Louis * Apply for this job online We are an Equal Opportunity Employer M/F/D/V. More information about this job: Overview: Enterprise Holdings has an exciting opportunity for a Business Analyst on the Corporate Airport Team! The Airport Business Analyst is responsible for solving business problems through data research and system/tool development. This position will analyze needs, create and deliver new rental reports for both our Group and Corporate Airport operations. Under general supervision, this position will gather, interpret and document reporting requirements/requests and develop moderately complex to complex queries/SQL statements to produce report results. The Airport Business Analyst will serve as the subject matter expert for their area of focus, providing the groups insight in key business metrics to identify areas of continued opportunity. This position is based at our Corporate Headquarters in St. Louis, Missouri. Questions? Detailed transactions 101.61- CPH went to the employee referral program 72.64- CPH went to the ADP Screening 32.33-CPH went to Commerce bank- (flight expenses) *Okay now what is the cost per FT employee? Per PT employee? The T-Statistic The T-Statistic Thinking back to my secondary research regarding the recruiting cost ratio (RCR). I remembered them dividing cost up into four categories Internal External Travel Signing Bonus I would like to add one more “Unknown” With these cost types, I couldn’t help but think about another chart detailing the expense per hire in regards to cost type….. Upper bound winner? Lower bound winner? Combined data set How I calculated FT and PT CPH Business understanding Business understanding Business understanding Did our results answer our question? It did more Not only was I able to calculate the cost per new hire- I did the cost per FT new hire and PT new hire. I discovered a new metric to rate recruiting cost. (RCR) Using the T- Statistic I calculated a 90% CI for next month’s recruiting expense Data preparation How do other companies handle this? Humanresourcemetrics.org Shared a metrics called the recruiting cost ratio (RCR) This is metrics was new to me and peeks my interest The formula is simple enough. Does Enterprise have analysts? Programs used to analyze my data set Excel SAS I started with excel and created a pivot table With the pivot table I graphed a bar chart to highlight the areas where we are spending the most money per new hire. (full time, part time, total) Data set #1 Business understanding The T-Statistic Data preparation (snap shot) Created these calculated columns- Business understanding Identify the opportunity Data Understanding Where did the data come from Data preparation Quality of data Data Analysis What methods did we use Evaluation Deployment Compute the Sample “variance”. Subtract average from each sample. 6865.54 - 4384.948 = 2480.592 3725.94 - 4384.948 = -659.008 6253.44 - 4384.948 = 1868.492 729.35 - 4384.948 = -3655.598 4350.47 - 4384.948 = -34.478 Square the results From this Pie Chart you can see that for every PT new hire, the company will spend on average, $72.64 in Screening, $40.68 in employee referral bonuses. Some differences in this chart is It differently so that it would include the percentages of the pie with the data. Formulate Confidence Interval Add Sample Error to mean to get UB 4384.948 + 2306.866 = 6691.814 Subtract sample error to mean to get LB 4384.948 - 2306.866 = 2078.082 CI statement- I am 90% confident that next month’s recruiting expense will be between $2078 and $6691 Final Project The T-Statistic The T-Statistic Business understanding The End SaS Compute the Sample “variance”. Square the results 2480.592 ^2 = 6153336.67 -659.008 ^2 = 434291.5441 1868.492 ^2 = 3491262.354 -3655.598 ^2 = 13363396.74 -34.478 ^2 = 1188.732484 Add results and divide by 1 less the sample size Data set #4 In order to produce this chart

Business Analytics

Transcript: Team 7 Abhishek Hiremath Anita Mohan Siyu Bi Swati Dixit You Ding BUSINESS ANALYTICS PROJECT 'PERSONA MODELLING' Problem Statement Problem Statement “To identify different student persona groups using descriptive and statistical analysis of available data sets to execute resource-intensive marketing strategies so that we can attract maximum number of suitable/relevant students to apply for admission" Revenue Acceptance Rates Exploring the Data Research Our Work : Cleaned the Data Normalized the data sets Eliminated outliers in calculation Explored the Application status Current standings Application Status Persona Modelling Implications ACADEMIC WANDERER (MALE) Persona 1 Attributes: Male Average age between 18 & 19 yrs USA Nationals Mostly residing in the East Coast Intended Term Fall 2019 Recruitment Source : College Board Suspected Program : Undergraduate Interested in an on-campus full-time program They want to go to college but don't know exactly what they want out of it. They are 'Undecided' Dashboard Persona 2 They want to go to college but don't know exactly what they want out of it. They are 'Undecided' Attributes: Female Average age between 18 & 19 yrs USA Nationals Mostly residing in the East Coast Intended Term Fall 2019 Recruitment Source : College Board Suspected Program : Undergraduate Interested in an on-campus full-time program ACADEMIC WANDERER (FEMALE) Dashboard Following their passion is the most important thing to them. THE PASSIONISTAS Persona 3 Attributes: Females Interested in Biology Average age between 18 & 19 years USA Nationals Intended Term Fall 2019 Suspected program : Undergraduate Interested in an on-campus full time program Dashboard THE SAVVY OPERATORS Persona 4 They know how the body works. They want to treat a patient's physical ailments as well as his or her emotional needs Attributes: Female Interested in Nursing USA Nationals Mostly residing on the east coast. Specifically New Hampshire & Massachusetts Broad range of age groups Recruitment Source : College Board Dashboard ASPIRING ACADEMICS Persona 5 They want to be a professor, researcher or academic Attributes: Female Interested in 'Education & Training' Already applied for the program Residing in USA Interested in Undergraduate program Intended Term Fall 2018 Dashboard Weights Source Info The Action plan Summary of Sources Available Majors/courses that the University has to offer University location Recruitment sources (college board, ACT, etc.) Student Location College ranking Matching Interest What is causing a student to engage with the University ? Student Engagement Questions ? THE END END

Business Analytics

Transcript: Photos Reusable assets Business Analytics Presented by:- Harsh Singh:- 12237181 MD Faizan:- 12237182 Chhavi Gupta:- 12237183 Ante molestie mattis arcu gravida viverra adipiscing volutpat. Ultrices eget viverra eu lectus ullamcorper. Consequat dictum tristique lectus augue felis nascetur amet non. Velit sit placerat tincidunt integer amet massa justo risus netus. Ornare sagittis malesuada varius cursus ipsum erat libero metus eget. Colors Assets Definition, Evolution, and Scope 04 01 02 03 Title Aa Aa Subtitle S M T W T S F Presented to:- Dr. Rajit Verma Paragraph 01 03 02 Aa Aa Predictive Models Introduction to Business Analytics Descriptive Models Statistical methods for analyzing business data Utilizing technology in business analytics Extracting insights from business data Application of statistical techniques in analysis Technology-driven business data examination Summarizing historical data to derive insights Facilitating decision-making through historical analysis Identification of trends and patterns in past data Understanding historical performance for informed decisions Offers comprehensive summaries for decision support Understanding key aspects of predictive analytics Utilizing statistical algorithms for predictive modeling Incorporating machine learning techniques for accurate forecasting Forecasting future outcomes based on data-driven insights Leveraging predictive analytics for informed decision-making Benefits of Descriptive Analytics Descriptive Analytics Example Predictive Analytics Example Benefits of Predictive Analytics Descriptive Analytics Example Benefits of Descriptive Analytics Evolution of Business Analytics Models in Business Analytics Scope of Business Analytics Examining past performance and historical data Identifying trends and patterns in previous data analysis Offering insights into historical performance Facilitating informed decision-making based on past data Summarizing comprehensive historical data for decision support Visualizing historical sales data trends Illustrating past performance through visualization Using visuals for historical sales analysis Demonstrating trends with data visualization Applying visualization to historical sales data Business analytics scope: Data analysis Data visualization Business intelligence Intro: Statistical methods and tech in analysis Descriptive models: Summarizing historical data Insights: Past performance, trends, and patterns Predictive models: Statistical algorithms and machine learning Forecasting future outcomes with data predictions Customer behavior data used for predictive analytics Predicting future purchasing patterns Forecasting customer purchase behavior Data-driven prediction of future purchasing patterns Utilizing customer behavior data for predictive insights Evolution of business analytics Advancement from basic reporting to predictive analytics Incorporation of prescriptive analytics Transition towards advanced statistical and machine learning techniques Enhanced decision-making capabilities through analytics Proactive decision-making Enabling proactive decision-making through data-driven insights Risk assessment Facilitating risk assessment using predictive analytics Personalized marketing strategies Utilizing predictive analytics for personalized marketing strategies

Business Analytics

Transcript: Healthcatalyst.com Microsoft NextGen Microsoft... Again SSAS SSRS How Analytics Applies to CHSI Thank You! Risks/Challenges and Competing Technologies Data Repository – Servers and software used to store data. Example: Data warehouses Software tools – Applications and processes for statistical analysis, forecasting, predictive modeling, and optimization. Examples: Data mining process; forecasting software package Analytics Environment – Organizational environment that creates and sustains the use of analytics tool. Example: Reward systems that encourages the use of the analytics tools; willingness to test or experiment. Skilled Workforce – Workforce that has the training, experience, and capability to use the analytics tools. Examples: Caesars, Capital One, Amazon The use of quantitative and predictive models and fact-based management to drive decisions. A subset of business intelligence. Business intelligence (BI) the set of technologies and processes that use data to understand and analyze business performance. Benefits of Business Analytics Components of Business Analytics No executive support No management support/lack of widespread adoption of fact-based/data-based decision making Finding Skilled People Competing technologies? Other vendors Old ways Business Analytics Two Major Sides of our Business Patient Quality Improvement (QI) Internally Shared Data to Improve Population Health Practice How can we better leverage our resources? What makes us money? And What loses money? Where can we gain efficiencies? Also, We are non-profit Government funded State Federal Grant funded What is needed?: Executive Support Widespread adoption Move to fact-based, analytics-based decisions Collect and Integrate Data Capable Computers Hardware Software Capable People Questions? Mission - To improve the health and well-being of the indigent population in medically underserved communities by providing a dignified setting to allow those who could not otherwise afford such services, access to low or no cost quality health care by skilled and caring providers and staff. Healthcare Industry The data and the healthcare acronym ocean: Billing International Classification of Diseases (ICD) maintained by WHO Current Procedural Terminology (CPT) maintained by AMA EHR HITECH Act of 2009 Meaningful Use (MU) Interoperability Health Information Exchange (HIE) Health Level Seven International (HL7) Identify their most profitable customers Offer profitable customers the right price Accelerate product innovation Optimize supply chain Identify the true drivers of financial performance. Ultimately: Gains a competitive advantage for organizations June Estell & Joshua Mauldin February 9, 2015 BUAD683 datasciencecentral.com healthcatalyst.com References What is Business Analytics Community Health Systems, Inc. History & Evolution of Business Analytics Who uses Analytics? An approach to Healthcare Analytics Analytics Solutions for CHSI A Look at the History and Future of Predictive Analytics and Big Data. (n.d.). Retrieved February 1, 2015, from http://visual.ly/look-history-and-future-predictive-analytics-and-big-dataDavenport, Thomas H (2013). Analytics 3.0. Harvard Business Review, December, 65-72. Davenport, Thomas D, Harris, Jeanne, & Jeremy Shapiro (2010). Competing on talent analytics. Harvard Business Review, October, 52-58. Harris, J. G., & Davenport, T. H. (2007). Competing on Analytics: The New Science of Winning. Harvard Business School Press Books, 1. Healthcare Analytics Adoption Model. (n.d.). Retrieved February 1, 2015, from https://www.healthcatalyst.com/healthcare-analytics-adoption-model/ Payandeh, F. (2013, August 25). BI vs. Big Data vs. Data Analytics By Example. Retrieved February 1, 2015, from http://www.datasciencecentral.com/profiles/blogs/bi-vs-big-data-vs-data-analytics-by-example Pearlson, C.S. (2013). Managing & Using Information Systems. (5th Edition). NJ: John Wiley & Sons, Inc. 62 ECG bpm Knowledge Management Business Intelligence Business Analytics

Business Analytics

Transcript: Wrap up quickly Buisness Development Market changes Continously and could be Dramatically, so.. More Succsess... Data analysis skills !!!! More Valid Data With Right Handling. Data analysis definition. How to conduct an effective data analysis and how to visualize it. Take the right decision, but NEVER rely only on data. Key role : Take data informed decesions Take decisions based on experince and data. Differes from data driven decisions which rely only on data. Medical Rep Presented by Albair Magdy Thank You Data collection, cleaning, transforming and interpreting, to define trends and patterns to help in making right decision in the right time. Data Analysis Use both descriptive and predictive data. Conduct effecient data analysis. Make decision. Sales Data and PH feedback. Potential customer or a potential time frame, define gaps. Who to visit, what product needs more focus, what are the effective tools, when is the perfect time for an activity like a health day, which msg was effective, ETC.. Data Mangment and Visualization How to conduct effecient data analysis ? Mangment : organized arrangment ( Excel - Tableu ) Visualization : arrange the most important metrics in shapes and colors to make it easy to read and track ( like a dashboard ) 1. Data governance (clean and validate) 2. Identify goals 3. Define needed metrics ( MAT - MSG - PPG - ETC.. ) 4. Know what to investigate (value or boxes or RX) 5. conduct analysis (define patterns) Vanity Metrics : Gives fake or not real progress.

Business Analytics

Transcript: indistrial Perspective of Business Analytics Introduction I As indicated by IDC's Infobrief "The Next Steps in Digital Transformation" 47% of studied innovative organizations adopted business analytics or business intelligence software for data analytics in 2017. Business Analytics refers to the aptitudes, advances, applications and practices for the constant investigation of information to pick up understanding that drive business choices. Business Analytics is multi-faceted. It joins various types of investigation and applies the correct strategy to convey anticipated outcomes. Key components Data Overload Domain Experts: Genuine business analytics needs domain expertise. Very frequently, organizations purchase analytic software believing it's another bit of middleware, and are regularly baffled when results are hard to achieve. Knowledge Management: Obtaining information and skill from area specialists is just a large portion of the fight. Catching this learning and automating its application in a predictable way is the basic achievement factor. Data and Text Mining: New experiences are found in organized information and content by utilizing Data and content mining systems. Content Analytics: Over 80% of accessible knowledge dwells in content. Business analytics must incorporate the understanding from content to be compelling. Statistical Analysis and Predictive Models: The utilization of factual techniques to approve presumptions as well as anticipate future results Visual Analytics: Understanding from huge volumes of information is best spoken to outwardly. Reporting and Analysis: Conventional Business Intelligence. Financial Performance and Strategy Management: Budgeting and arranging, monetary combination, score-checking and methodology the executives, budgetary investigation and related announcing. There is a huge blast of information happening on various dimensions. An extensive number of CEOs depicted their associations as information rich, however understanding poor. estimates demonstrate the volume of unstructured information (email, sound, video, Web pages, and so forth.) doubles every three months. Most industry investigators estimates over 80% of the knowledge required to settle on more brilliant choices is contained in unstructured information or content. Elements II New and developing techniques help quicken time-to-knowledge. These new methodologies help us ingest knowledge from extensive volumes of information in fast style. The examination recognized as making the most incentive in two years are: Information perception Reproductions and situation advancement Investigation connected inside business forms Progressed measurable procedures Advanced Analytics Organizations keep on investing million dollars catching, storing and keeping up a wide range of business data to drive deals and income, streamline activities, oversee risks and guarantee compliance. New types of advanced analytic are required to address the business goals depicted before. The innovation enables clients to extract and analyze facts like who, what, where, when and why; at that point enables clients to penetrate down to comprehend individuals, spots and occasions and how they are connected. 1 Predictive analytics Predictive models exploit patterns found in recorded and value-based information to recognize dangers and openings. Models control basic leadership by catching connections among numerous variables to permit evaluation of risk or opportunity related with a specific arrangement of conditions. 2 It is showing numerous possibilities for analyzing data in motion. There is no better method to appreciate the intensity of developing logical innovation than viewing it with regards to life saving applications. Stream computing, data mining and predictive analytics are being utilized to help stroke exploited people. Stream computing 3 Mash-ups It will enable us to pull together all significant information for a specific decision, while social technologies enable us to extract intelligence from the group. In this manner, this collaborative procedure enables us to confidently decide the fitting next activity with regards to a given procedure. Enable Outcomes GPS-empowered route gadgets as of now superimpose constant traffic examples and cautions onto route maps and recommend the best courses to drivers. Analytic algorithms are utilized to estimate whittling down probabilities, pinpoint at-risk clients and suggest exact maintenance procedures. Dashboards that presently reflect last quarter sales will likewise demonstrate potential next quarter deals under a wide range of conditions – another media blend, a value change, or a bigger sales team Simulations assessing alternative situations will consequently suggest ideal methodologies Application III For a business, the best concern is putting forth valuable products and services to clients. To do this, they have to remain creative and in front of their competition. Generally,

Modern Business PowerPoint Template

Transcript: Best Practices for Business Presentations Implementing effective strategies to enhance presentation impact. Ongoing Maintain slide conciseness by limiting text and focusing on key messages. Final Thoughts and Customization This modern business PowerPoint template serves as a versatile framework that can enhance your presentations. The next steps include tailoring the template to reflect your brand's identity through color customization and layout adjustments, ensuring it meets your specific business needs. Use High-Quality Images Images should be high-quality and relevant to the topic to create a professional appearance and enhance understanding. Incorporate Meaningful Icons Incorporating Visual Elements Icons can simplify complex information and serve as visual cues to guide the audience through key points. Utilize Data Visualizations Charts and graphs can effectively convey data in a visually appealing way, making it easier for the audience to interpret and retain information. Modern Business PowerPoint Template Exploring a Sleek and Contemporary PowerPoint Template Design for Businesses Importance of Typography in Business Presentations Typography plays a vital role in ensuring readability and conveying brand identity. This template features contemporary font selections that enhance clarity and complement the chosen color palette, creating a cohesive look throughout the presentation. Introducing a Modern Business PowerPoint Template Text-Heavy Slides Image-Focused Slides Vibrant Teal: #00a180 Dark Gray: #313233 The primary color #00a180 is a vibrant teal, ideal for accents and highlights. It conveys freshness and modernity, making it suitable for business presentations. The dark gray #313233 serves as a strong background or text color, providing excellent contrast for readability and a professional appearance. The template provides a variety of slide layouts designed to cater to different content needs. Text-heavy slides focus on delivering detailed information clearly, while image-focused slides highlight visuals to enhance engagement. Comparison slides allow for direct juxtaposition of ideas or products, making it easier for the audience to understand differences or similarities. This template is designed with a focus on clarity and professionalism, incorporating a cohesive color palette that enhances visual appeal and supports effective communication. Its modern aesthetics aim to engage audiences while delivering content succinctly. In contrast, the flexibility of these layouts enables users to create presentations that are both informative and visually appealing. This adaptability ensures that the message is communicated effectively, regardless of the slide type used. Each layout is designed with modern aesthetics in mind, aligning with the overall professional tone of the template. Muted Gray: #7d7a77 White: #FFFFFF Overview of the Color Palette The muted gray #7d7a77 complements the palette by adding warmth and sophistication, suitable for secondary elements. White (#FFFFFF) is essential for creating space and contrast, ensuring that text and visuals stand out effectively. Black: #000000 Light Gray: #ADB5BD Black (#000000) is bold and authoritative, perfect for text and key design elements that require emphasis. Light gray #ADB5BD introduces a softer tone to the palette, balancing darker colors and enhancing overall aesthetics.

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