Business analytics
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Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. In contrast, business intelligence traditionally focuses on using a consistent set of metrics to both measure past performance and guide business planning. In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription.[1]
Business
(OLAP), and "alerts".In other words, querying, reporting, and OLAP are alert tools that can answer questions such as what happened, how many, how often, where the problem is, and what actions are needed. Business analytics can answer questions like why is this happening, what if these trends continue, what will happen next (predict), and what is the best outcome that can happen (optimize).[3]
Examples of application
In healthcare, business analysis can be used to operate and manage clinical information systems. It can transform medical data from a bewildering array of analytical methods into useful information. Data analysis can also be used to generate contemporary reporting systems which include the patient's latest key indicators, historical trends and reference values.[4]
- Decision analytics: supports human decisions with visual analytics that the user models to reflect reasoning.[5]
- Descriptive analytics: gains insight from historical data with reporting, scorecards, clustering etc.
- Predictive analytics: employs predictive modelling using statistical and machine learning techniques
- Prescriptive analytics: recommends decisions using optimization, simulation, etc.
Basic domains within business analytics
- Behavioral analytics
- Cohort analysis
- Competitor analysis
- Customer journeyanalytics
- Cyber analytics
- Enterprise optimization
- Financial servicesanalytics
- Fraud analytics
- Health care analytics
- Key performance indicators(KPI's)
- Market Basket Analysis
- Marketing analytics
- Pricing analytics
- Retail salesanalytics
- Risk and credit analytics
- corporate strategy and supply chain execution.[7]
- Talent analytics
- Telecommunications
- Transportationanalytics
History
Analytics have been used in business since the management exercises were put into place by
In later years the business analytics have exploded with the introduction of computers. This change has brought analytics to a whole new level and has brought about endless possibilities. As far as analytics has come in history, and what the current field of analytics is today, many people would never think that analytics started in the early 1900s with Mr. Ford himself.
Challenges
Business analytics depends on sufficient volumes of high-quality data. The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available.[3]
Previously, analytics was considered a type of after-the-fact method of
Competing on analytics
Thomas Davenport, professor of information technology and management at Babson College argues that businesses can optimize a distinct business capability via analytics and thus better compete. He identifies these characteristics of an organization that are apt to compete on analytics:[3]
- One or more senior executives who strongly advocate fact-based decision making and, specifically, analytics
- Widespread use of not only optimizationtechniques
- Substantial use of analytics across multiple business functions or processes
- Movement toward an enterprise-level approach to managing analytical tools, data, and organizational skills and capabilities
See also
References
- ^ "Comparing Business Intelligence, Business Analytics and Data Analytics". Tableau. Retrieved 2021-03-06.
- ^ Galit Schmueli and Otto Koppius. "Predictive vs. Explanatory Modeling in IS Research" (PDF). Archived from the original (PDF) on 2010-10-11.
- ^ ISBN 978-1-4221-0332-6.
- PMID 25429161.
- ^ "Analytics List". Archived from the original on 7 April 2015. Retrieved 3 April 2015.
- ^ DeAngelis, S., The Growing Importance of Supply Chain Analytics, Enterra Solutions LLC, published 9 November 2011, accessed 4 December 2022
- ^ Westerveld, J., The Growing Importance of Supply Chain Analytics, Supply & Demand Chain Executive, published 28 August 2008, accessed 4 December 2022
- ^ "Choosing the Best Storage for Business Analytics". Dell.com. Archived from the original on 2012-07-18. Retrieved 2012-06-25.
Further reading
- ISBN 9781422103326.