Business Intelligence Designer Beginning Income – Data analytics is the process of analyzing raw data to extract meaningful insights – insights that are used to drive intelligent business decisions.
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Business Intelligence Designer Beginning Income
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Data analytics is the process of transforming raw data into meaningful, actionable insights. You can think of it as a form of business intelligence, used to solve specific problems and challenges within an organization. It’s all about finding patterns in a dataset that can tell you something useful and important about a particular area of the business—how certain customer groups behave, for example, or why sales have declined over a given time period.
A data analyst takes the raw data and analyzes it to extract useful insights. They then present these insights in the form of visualizations, such as graphs and charts, so that stakeholders can understand and act on them. The types of insights gleaned from the data depend on the type of analysis performed. There are four main types of analysis used by data scientists:
Descriptive analysis looks at what happened in the past, while diagnostic analysis looks at why it might have happened. Predictive and prescriptive analysis considers what is likely to happen in the future and, based on these predictions, what the best course of action might be.
All in all, data analysis helps you understand 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 data access, 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.
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Any organization collecting data can use data analytics, and how it is used varies according to the context. Broadly speaking, data analytics is used to drive smarter business decisions. This helps reduce overall business costs, develop more efficient products and services, and optimize processes and operations across an organization.
In more specific terms, data analysis could be used to predict future sales and purchasing behaviors, for example by identifying trends from the past. It could be used for security purposes, for example to detect, predict and prevent fraud, especially within the insurance and financial industries. It can be used to evaluate the effectiveness of marketing campaigns, and drive more precise audience targeting and personalization. In the healthcare sector, data analysis can be used to make faster, more accurate diagnoses and to identify the most appropriate treatment or care for each individual patient. Data analytics is also used to optimize overall business operations, for example by identifying and removing bottlenecks within 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 about the personalized recommendations you get from the likes of Netflix and Spotify; it all depends on data analysis. You can learn more about how data analytics is applied in the real world here.
The data analysis process can be divided into five steps: Define the question, collect the data, clean the data, analyze it and create visualizations and share insights.
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The first step in the process is to define a clear goal. Before delving into the data, you’ll come up with a hypothesis you want to test or a specific question you want answered. 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 or question will inform what data you’re analyzing, where you’re pulling it from, and the type of analysis you’re doing.
With a clear goal in mind, the next step is to collect the relevant data. You might 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 crucial step.
This is where you start extracting insights from your data. How you analyze the data depends on the question you’re asking and the type of data you’re working with, and there are many different techniques at your disposal—such as regression analysis, cluster analysis, and time series analysis (to name just a few).
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The final step is where data is transformed 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 clarify what the data is telling you in relation to your original question. You will find a complete guide to data visualization in this guide.
Most companies collect reams of data all the time – but, in its raw form, this data doesn’t really mean anything. A data analyst basically 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 crucial role in any organization, using their insights to drive smarter business decisions.
Data analysts are employed across a variety of industries, and the role can vary quite considerably 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 at an insurance brokerage. This variety is part of what makes data analysis such an interesting career.
That said, most data analysts are responsible for collecting data, performing analyses, creating visualizations, and presenting their findings.
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Ultimately, data analysts help organizations understand the data they collect and how it can be used to make informed decisions. You can learn more about what it’s like to work as a data analyst in this daily account.
Data analysts tend to have an affinity for numbers and a passion for problem solving. In addition to these intrinsic qualities, the key hard and soft skills needed to become a data analyst can all be learned and transferred—you don’t need a specific degree or particular background.
If you are thinking of becoming a data analyst, there are several things you will need to do. First and foremost, you will need to master the necessary hard skills and industrial tools. This includes getting to know Excel, data visualization tools like Tableau, and in some cases, querying and programming languages like SQL and Python. You will need to learn about the different types of data analysis and how to apply them, and you will need to be well-versed in data analysis—from defining a problem statement to presenting your insights to key stakeholders. .
At the same time, you will need to start building your professional data analytics portfolio. Your portfolio shows projects you’ve worked on and provides information about how you work as a data analyst. This is important to show employers that you have acquired the necessary knowledge and skills to work in the field.
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Data analysts are in high demand, and a career in the field is diverse, financially rewarding, and highly fulfilling—your work as a data analyst will have a real, tangible impact on the business or organization. One of the most effective ways into the industry is through a dedicated program or course. With a structured, project-based curriculum, the guidance of a mentor, and the support of fellow career changers, anyone can retrain as a data analyst. If you’re thinking of becoming a data analyst, check out this comparison of the best data analytics certification programs on the market right now.
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