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  • Business analytics assignment help

Business analytics assignment help

Business analytics is the process of using statistical models and iterative techniques to collate, sort, process, and study business data and transform it into useful insights. The purpose of business analytics is to determine which sets of data are useful and how they can be manipulated to solve operational issues and increase productivity, efficiency, and revenue. Sophisticated data, mathematical models, and qualitative analysis are all applied by business analysts to develop solutions for data-driven issues. These individuals use information systems, statistics, operations research, and computer science to enhance their understanding of complex sets of data. They also employ neural networks, artificial intelligence, and deep learning to micro-segment the available data sets and identify patterns. The information obtained can then be used to make accurate future predictions related to market trends or customer action and recommend strategies that can drive more revenue and help the business achieve its goals. Students who just enrolled in a business analytics class and those who are not quite familiar with the subject’s core concepts can have a hard time working on assignments issued on this unit. Luckily, with business analytics assignment help services readily available on the web, students who are struggling with the subject’s homework can get the support they need to complete their papers effectively. Statistics Assignment Experts is one of the places where students can obtain reliable help with business analytics homework. Just let us know which topic your assignment is derived from, what type of writing it is and when you need the finished document sent to you and we will be at your service.

Components of business analytics

There are many elements that makeup business analytics. Here are the most essential:
  • Data aggregation: Before analyzing data, one has to collect, centralize, and clean it to avoid duplicates and filter it to get rid of unusable, incomplete, and inaccurate data. Data can be aggregated from:
  • Volunteered data: Information sent via digital or paper form to the company by a consumer or third party
  • Transaction records: Banking records, shipping records, sales records, and any other type of data that is part of a larger data provided by the company for analysis
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  • Data mining: Data models can be built by mining through large amounts of data to identify and reveal previously unrecognized patterns and trends. Data mining applies several statistical methods to achieve the desired results including:
  • Classification: Employed when the demographics such as age, gender, income, etc. are known and can be used to organize or sort a data set.
  • Regression: Used to estimate continuous numeric values based on previous patterns
  • Clustering: Applied when the elements used to classify data are not present, which means, patterns must be recognized to find out which variables exist in a data set.
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  • Association and sequence identification: In most cases, customers perform similar actions simultaneously or predictable actions sequentially. Data obtained from customers behavior can reveal patterns and trends such as:
  • Association: For instance, two different items being bought frequently in the same transaction like a toothpaste and toothbrush, or a fork and spoon
  • Sequencing: For instance, a customer buying a plane ticket, which is followed by reserving a car or booking a hotel room.
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  • Text mining: Companies can collect textual data from emails, call center scripts, social media platforms, blog comments etc. to identify meaningful relationship indicators. The data obtained from these platforms can then be used to:
  • Improve customer service and experience
  • Develop products that are on demand
  • Review competitor performance
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  • Forecasting: Businesses can analyze the events that occur during a given period of time to predict future behaviors and/or events.
  • Predictive analysis: Predictive scoring models can be created to help a business proactively address issues such as:
  • Equipment failure
  • Reduction in number of customers buying a certain product
  • Data visualization: Insights and information obtained from a data set can be presented and communicated through highly interactive graphics to display:
  • The modeling output
  • Exploratory data analysis
  • Statistical predictions
Data visualization techniques allow businesses to leverage their data in order to inform and achieve new goals, improve customer relations, and increase revenue. To pay for business analytics assignment writing on projects derived from this topic, contact our customer service representatives and they will guide you through the process.

Types of business analytics

There are four major types of business analytics that can be implemented today to help organizations create fruitful solutions for their problems. These include:
  • Descriptive analytics: This type of analysis summarizes and analyzes the company’s existing data to know what has happened in the past and what is happening currently. It applies data mining and aggregation techniques to identify strengths and weaknesses in customer or employee behavior.
  • Diagnostic analytics: In diagnostic analytics, data analysts try to understand the “why” and “how” of past events. This technique applies statistical methods such as data recovery, drill down, correlations, and data mining to identify the root causes of events.
  • Predicative analytics: This technique employs machine learning methods and statistical models to predict the likelihood of future events to occur.
  • Prescriptive analysis: This one goes a step beyond the predictive analysis, offering suggestions for the most appropriate causes of action and enabling possible manipulation of events to provide better outcomes.
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Importance of business analytics

  • Business analytics is a great tool for organizations to make informed decisions. When implemented into a business, it impacts its entire functioning and operations. It can therefore help a company improve its profits, increase revenue and market share, and offer better returns to the shareholders.
  • Business analytics facilitates a better comprehension of the current primary and secondary data. This again affects the operations of the business and improves the efficiency of various departments.
  • Business analytics enables a company to stand out from its competitors, or rather gain a better competitive edge against its competitors. Since companies are able to collect information from various sources like social media, emails, website comments, etc. they are can find out what consumers want and make changes that ensure their satisfaction. It is how the information the business obtains is utilized that makes the organization competitive. Employing business analytics helps an organization create well thought out models that improve its decision making process.
  • Business analytics converts all company’s data into useful information. This information can be manipulated in different angles and presented in any required format to the management for accurate and effective decision making.

Challenges in business analytics

There are a number of reasons why some companies have still not fully embraced business analytics. Read on!
  • Executive ownership: Business analytics requires a buy in from the management and senior leadership and a very clear organization strategy to integrate predictive models.
  • Technology involvement: Information technology tools and infrastructure must be available and able to handle large data and business analytics processes.
  • Cleansed modeling data versus production data: Some technology infrastructure will restrict data for historical modeling. An organization must have an infrastructure that does not restrict data and that allows effective manipulation of all of its previous data.
  • Project management office: There must be an effective project management structure for predictive models to be implemented and adopted in a business.
  • Change management: Companies must be ready to cope with the changes that the business analytics brings to the existing business operations and technology structure.
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