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Business Analytics Vs Data Science

by gbaf mag
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Business analytics is the collective term for several business analytics methods, which are designed to improve a business’s efficiency by uncovering the business processes that are operating inefficiently or are not efficiently aligned with strategic objectives. It also encompasses software applications that provide users with timely, accurate, and comprehensive information about the operation of the business. In today’s business environment, many businesses need to employ advanced analytical techniques to make information about their operations and processes accessible to key decision makers. Business analytics identifies these key decision makers and the business processes they execute.

One type of business analytics is data mining, which leverages technology to extract the predictive and prescient information about business trends. Data mining is a subset of business intelligence or data analysis that uses algorithms to search large databases for patterns and trends. Once the databases are identified, business intelligence and data management teams then select and analyze the information in order to generate insightful reports. This subset of business analytics is sometimes referred to as “big data.”

Another popular type of business analytics is data visualization. Using data visualization, business intelligence and data management teams to create visual representations of business trends so those managers who make important business decisions can make informed decisions. These visual representations may be web-based or more traditionally, accessed via proprietary software applications. Data visualization creates intuitive interpretations of complex patterns and trends.

Historically, what is business analytics most commonly associated with is statistical analysis, especially with respect to financial statistics. The most common example is that of analyzing company earnings, inventory, sales, or customer loyalty by using major statistical methods, such as time series analysis, data analysis, or regression analysis. While this is an important aspect of business analytics, statistical techniques are only one component of what makes up a successful analytics strategy.

The problem is that traditional approaches to business analytics are very limited in their predictive ability, because they are only able to predict what might already have happened, or what might occur in the future. In short, business analytics can only forecast future trends. The difficulty in doing this comes from the fact that it is difficult to construct descriptive and predictive sets of metrics from historical data. In many cases, no matter how carefully and extensively these models are constructed, there will still be problems with missing or invalid data, which renders the model worthless as a guide for making predictions. This is why business operations professionals are now turning to the previously mentioned subset of business analytics known as “key performance indicators” or KPI.

Key performance indicators, or KPI, are a subset of business analytics that are derived from captured customer or organizational data. Typically, KPI are used alongside traditional Machine Learning algorithms to make predictive predictions about what will happen in the future. One drawback to using data mining and key performance indicators in business analytics is that the accuracy of these algorithms is not yet mathematically guaranteed. This is because data mining requires collecting large amounts of unstructured data, whereas KPI requires only a small amount of structured data in order to generate its predictions.

The biggest difference between business analytics vs data science is the speed at which business operations professionals are able to use analytics to make strategic decisions. With data mining, it may take weeks, months, or even years before you are able to generate a working strategic model from the raw data that you have. This delay causes a great deal of indecision between executives, because they are unsure of the validity of the information that they are being given.

On the other hand, business analysts have a much shorter time-frame to generate working models from a collection of unstructured data sets. In most cases, they will generate a working model in a day or two. In addition, business analysts typically use more sophisticated mathematical programs for generating their predictive statistical analysis models than computer science graduate students. As a result, the predictive accuracy of their statistical models is usually much higher than the accuracy levels of most graduate student applications. Finally, business analysts often use these programs as learning tools, as many of them can be adjusted and personalized for different business needs.


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