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Difference between revisions of "IS480 Team wiki: 2018T1 analyteaka research"

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Revision as of 15:41, 14 August 2018

Analyteaka Header.png


HOME

ABOUT US

PROJECT OVERVIEW

PROJECT MANAGEMENT

DOCUMENTATION




Analyteaka Research.png

Data Analytics is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software.

Typical mechanisms: Database (only Data)

Typical timeframe: Offline

The outcome of analytics is informed business decisions to verify or disprove scientific models, theories and hypotheses. The typical goals is to improve efficiency, optimize processes, increase revenues etc.

The hardest part of analytics project is asking the question. As Robert Half once mentioned, "Asking the right questions takes as much skill as giving the right answers." - Robert Half

Descriptive analytics Insight into the past:
  • Uses data aggregation and data mining to provide insight into the past and answer: “What has happened?”.
  • It is typically used to summarise raw data and make it interpretable by humans.

Data operations:

  • Report card of data, used for spotting potential issues when you need to understand at an aggregate level what is going on
  • When you want to summarize and describe different aspects of your business.
Predictive analytics Understanding the future:
  • Uses statistical models and forecasts techniques to understand the future and answer: “What could happen?”
  • Provides estimates about the likelihood of a future outcome
  • Foundation of predictive analytics is based on probabilities
Prescriptive analytics Advise on possible outcomes:
  • Uses optimization and simulation algorithms to advice on possible outcomes and answer: “What should we do?”
  • Attempts to quantify the effect of future decisions in order to advise on possible outcomes before the decisions are actually made
  • Uses a combination of techniques and tools such as business rules, algorithms, machine learning and computational modelling procedures
  • Anytime you need to provide users with advice on what action to take.

Based on the above details our modules are split into the respective section



Descriptive:

  • Customer Profile module
  • Store Profile
  • Staff profile

Predictive:

  • Machine Learning

Prescriptive:

  • Data visualisation module
  • Analytics and reporting module


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