Mapping Your Pay Scale: Business Intelligence Salary Insights – Each structure consists of ten (10) pay grades in which similarly functioning jobs are placed. In each structure, there are yearly and hourly charts. The assignment of each chart is based on:
These charts are not interchangeable. To determine the annual salary for non-exempt or hourly positions, the formula is:
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We are pleased to announce that, effective October 1, 2021, the minimum wage for all staff will increase from the current $10.82 per hour to $15 per hour.
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The decision sets Tulane’s minimum wage at more than double the federal/Louisiana minimum. The university affirms its responsibility as an academic leader in the region and New Orleans’ largest private employer to evaluate and improve compensation packages that allow us to attract, hire, develop and retain the most talented professionals in the widest array of fields. Solidly competitive pay and generous benefits, we strive to honor the critical and essential role our workforce plays in creating, maintaining and growing our mission as one of the nation’s leading institutions of higher education and research.
To maintain consistency in compensation for student positions, Tulane will also standardize student wages across the university, increasing the minimum hourly rate from $7.25 to $10.00 effective October 1, 2021.
Increases will be made to staff pay ranges in keeping with our commitment to continuously review our pay rates relative to the market, and appropriate adjustments will be made to ensure that all staff are paid within the new range minimum. Determine monthly paycheck deductions for medical insurance premiums will be updated to align with the revised compensation structure.
Additional adjustments will be considered for jobs in the same classifications, as well as immediate levels above, to relieve pay compression. The Office of Human Resources and Institutional Equity will begin working with leaders across all divisions whose staff will benefit from this change so they can notify employees who qualify for an increase in salary on or before October 1.
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Pay Grade: Define the different levels of pay for jobs relative to their internal job evaluation and external market. Data analytics is the process of analyzing raw data to draw out meaningful insights – insights that are used to drive smart business decisions.
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Data analytics is the process of turning raw data into meaningful, actionable insights. You can think of it as a form of business intelligence, used to solve specific problems and challenges in an organization. It’s all about finding patterns in a dataset that can tell you something useful and relevant about a particular area of the business – how certain customer groups behave, for example, or why sales dipped at a given time.
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A data analyst takes the raw data and analyzes it to gain useful insights. Then they present the insights in the form of visualizations, such as graphs and charts, so that stakeholders can understand and act on them. The types of insights from the data depends on the type of analysis. There are four main types of analysis used by data experts:
Descriptive analytics looks at what happened in the past, while diagnostic analytics looks at what might have happened. Predictive and prescriptive analytics consider what is likely to happen in the future and, based on those predictions, what the best course of action might be.
All in all, data analytics helps you make sense of the past and predict future trends and behaviors. So, rather than basing your decisions and strategies on guesswork, you make informed choices based on what the data tells you. With a data-driven approach, businesses and organizations are able to develop a much deeper understanding of their audience, their industry and their company as a whole – and, as a result, are much better equipped to make decisions, plan ahead and compete in Their chosen market.
Any organization that collects data can use data analytics, and how it is used will vary depending on the context. Broadly speaking, data analytics is used to drive smarter business decisions. This helps to reduce overall business costs, to develop more efficient products and services, and to optimize processes and operations across an organization.
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In more specific terms, data analytics may be used to predict future sales and purchasing behaviors, for example by identifying trends from the past. It may be used for security purposes, for example to detect, predict and prevent fraud, especially in the insurance and finance industries. It can be used to evaluate the effectiveness of marketing campaigns and to conduct more accurate audience targeting and personalization. In the healthcare sector, data analytics can be used to make faster, more accurate diagnoses and to identify the most suitable treatment or care for each individual patient. Data analytics is also used to optimize general business operations, for example by identifying and eliminating bottlenecks in certain processes.
Data analytics is used in almost every industry – from marketing and advertising to education, healthcare, travel, transport and logistics, finance, insurance, media and entertainment. Think of the personalized recommendations you get from the likes of Netflix and Spotify; That’s all down to data analytics. You can learn more about how data analytics is applied in the real world here.
The data analysis process can be broken down into five steps: defining the question, collecting the data, cleaning the data, analyzing it, and creating visualizations and sharing insights.
The first step in this process is to define a clear objective. Before delving into the data, you’ll find a hypothesis you want to test, or a specific question you want to answer. For example, you might want to investigate why so many customers unsubscribed from your email newsletter in the first quarter of the year. Your problem statement or question will inform what data you are analyzing, where you are drawing it from, and the type of analysis you are performing.
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With a clear objective in mind, the next step is to collect the relevant data. You can get your data from an internal database or from an external source – it all depends on your goals.
Next, you’ll prepare the data for analysis, removing anything that might distort how the data is interpreted—such as duplicates, anomalies, or missing data points. This can be a time-consuming task, but it is a critical step.
This is where you start to draw insights from your data. How you analyze the data depends on the question you’re asking and the kind of data you’re working with, and there are many different techniques at your disposal—like regression analysis, cluster analysis, and time-series analysis (to name just a few).
The last step is where data is turned into valuable insights and action points. You will present your findings in the form of charts and graphs, for example, and share them with key stakeholders. At this stage, it is important to explain what the data tells you in relation to your original question. You will find a complete guide to data visualization in this guide.
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Most companies are collecting loads of data all the time – but, in its raw form, the data doesn’t really mean anything. A data analyst essentially translates raw data into something meaningful and presents it in a way that is easy for everyone to understand. As such, data analysts have a critical role to play in any organization, using their insights to drive smarter business decisions.
Data analysts are employed in a wide variety of industries, and the role can vary quite significantly from one company to the next. For example, the typical day of a data analyst working in the medical sector will be very different from that of an analyst in an insurance brokerage. The variety is part of what makes data analytics such an interesting career path.
With that said, most data analysts are responsible for collecting data, performing analyses, creating visualizations, and presenting their findings.
Ultimately, data analysts help organizations understand the data they have
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