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Big data is transforming and powering decision-making everywhere. From large enterprises to higher education and government agencies, data from a plethora of sources is helping organizations expand their reach, boost sales, operate more efficiently, and launch new products or services. In order to make sense of all this data and use it to be more competitive, companies must apply both business analytics and data analytics. There’s often confusion about these two areas, which can seem interchangeable. In this article, we'll examine the goals of each function and compare roles and responsibilities to help you decide which path is right for you. Business analytics vs. data analytics: An overviewBoth business analytics and data analytics involve working with and manipulating data, extracting insights from data, and using that information to enhance business performance. So, what are the fundamental differences between these two functions? Business analytics focuses on the larger business implications of data and the actions that should result from them, such as whether a company should develop a new product line or prioritize one project over another. The term business analytics refers to a combination of skills, tools, and applications that allows businesses to measure and improve the effectiveness of core business functions such as marketing, customer service, sales, or IT. Data analytics involves combing through massive datasets to reveal patterns and trends, draw conclusions about hypotheses, and support business decisions with data-based insights. Data analysis attempts to answer questions such as, “What is the influence of geography or seasonal factors on customer preferences?” or “What is the likelihood a customer will defect to a competitor?”. The practice of data analytics encompasses many diverse techniques and approaches and is also frequently referred to as data science, data mining, data modeling, or big data analytics. An intro to business analyticsBusiness analytics (BA) is the iterative exploration of an organization’s data, with a focus on applying statistical analysis techniques to reveal information that can help drive innovation and financial performance. Analytics-driven organizations treat big data as a valuable corporate asset that fuels business planning and supports future strategies, and business analytics helps them get maximum value from this goldmine of insights. There are three main kinds of business analytics — descriptive, predictive and prescriptive. These are usually implemented in stages and together can answer or solve just about any question or problem a company may have.
Organizations may use any or all of these techniques, though not necessarily in this order. Business analytics can be implemented in any department, from sales to product development to customer service, thanks to readily available tools with intuitive interfaces and deep integration with many data sources. Many of these solutions offer users the ability to apply advanced analytic models without the help of a data scientist, creating new opportunities to find hidden insights in large datasets. Business analytics requires adequate volumes of high-quality data, so organizations seeking accurate outcomes must integrate and reconcile data across different systems, then determine what subsets of data to make available to the business. An intro to data analyticsData analytics is the process of collecting and examining raw data in order to draw conclusions about it. Every business collects massive volumes of data, including sales figures, market research, logistics, or transactional data. The real value of data analysis lies in its ability to recognize patterns in a dataset that may indicate trends, risks, or opportunities. Data analytics allows businesses to modify their processes based on these learnings to make better decisions. This could mean figuring what new products to bring to market, developing strategies to retain valuable customers, or evaluating the effectiveness of new medical treatments. Most commonly-used data analysis techniques have been automated to speed the analytical process. Thanks to the widespread availability of powerful analytics platforms, data analysts can sort through huge amounts of data in minutes or hours instead of days or weeks using:
As more organizations move their critical business applications to the cloud, they are gaining the ability to innovate faster with big data. Cloud technologies create a fast-moving, innovative environment where data analytics teams can store more data and access and explore it more easily, resulting in faster time to value for new solutions. Business analytics vs. data analytics: A comparisonMost people agree that business and data analytics share the same end goal of applying technology and data to improve business performance. In a data-driven world where the volume of information available to organizations continues to grow exponentially, the two functions can even work in tandem to maximize efficiency, reveal useful insights, and help businesses succeed. This side-by-side comparison should help clear up some of the confusion between business and data analytics. Business analyst vs. data analyst: A comparison of rolesBusiness analysts and data analysts both work with data. The difference is what they do with it. Business analysts use data to make strategic business decisions. Data analysts gather data, manipulate it, identify useful information from it, and transform their findings into digestible insights. Analyzing data is their end goal. People in either role need to have a love of all things data, possess an analytical mind, have good problem-solving skills, and the ability to see and work towards the bigger picture. But if you’re trying to decide between these two career paths, it’s equally important to understand how they differ.
Additional required abilities of each roleAside from technical and role-specific skills, business and data analysts each need some additional abilities to be successful. A business analyst needs to be able to:
A data analyst needs to be able to:
Getting started with business or data analyticsFrom the newest startups to established global enterprises, every organization needs to leverage data for innovation and business growth. The practices of data analytics and business analytics share a common goal of optimizing data to improve efficiency and solve problems, but with some fundamental differences. Whichever path you choose, you’ll need to gather relevant, trusted data from many sources quickly, easily, and securely. Talend Data Fabric speeds the analytics process by providing a single suite of cloud-based self-service applications for data integration and integrity. Because when you’re confident in your data’s quality, your stakeholders will be confident they’re making the right business decisions every time. Try Talend Data Fabric today to begin making data-driven decisions. Ready to get started with Talend?More related articles
Which phase involves gathering data from various sources?L2 Following the data life cycle:
In the data life cycle, which phase involves gathering data from various sources and bringing it into the organization? Q3. A data analyst finishes using a dataset, so they erase or shred the files in order to protect private information. This is called archiving.
Which stage of the data life cycle does a business decide what kind of data it needs how the data will be managed and who will be responsible for?The manage stage of the data life cycle is when a business decides what kind of data it needs, how the data will be handled, and who will be responsible for it.
What are the phases involved in data analysis?The data analysis process, or alternately, data analysis steps, involves gathering all the information, processing it, exploring the data, and using it to find patterns and other insights.
During which phase of data analysis would a data analyst?During which phase would a data analyst use spreadsheets or query languages to transform data in order to draw conclusions? The analyze step involves using data analytics tools such as spreadsheets and query languages to transform data in order to draw conclusions and make informed decisions.
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