The Analytics World

The Analytics World

The Analytics World has wonderful historical moments and aspects to think and reflect about. There are multiple things to use in the current demand of solutions based on analytics in data-drive organizations. These examples of the historical experience show part of the analytics evolution:

  • Brownian Motion was described by Brown R. (1827) used by Einstein (1905), Wiener (1961) and Black and Scholes (1973)
  • Von Neumann and Morgenstern (1944) wrote “There is no point in using exact methods where there is no clarity in the concepts and issues to which they are to be applied.”
  • Norbert Wiener (1961) wrote “ The theory of linear prediction and of non-linear prediction both involve some criteria of the goodness of the fit of the prediction. The simplest criterion, although by no means the only usable one, is that of minimizing the mean square of the error. This is used in a particular form in connection with the functionals of the Brownian motion which I employ for the construction of non-linear apparatus,…”

Aspects to consider in a Data Analytics Journey to add value to organizations-society:

1.Everyone can learn and apply Business Analytics, the journey for personal development in this discipline requires several aspects to consider: discipline, motivation and desire to keep a lifelong self-learning process.

2.Business analytics is not to press buttons and to get magic solutions. It is a systematic process that requires a lot of research motivation and capacity to connect models with reality.

3. Big Data Analytics is the application of the Analytics Process to a particular type of data that is Big Data. You cannot go through Big Data Analytics if you do not understand Analytics and Business Analytics processes.

 4. “The goal for analytics is to make better decisions. However, while many organizations collect data and some organizations engage in analytics, few organizations link their analytics activities to their decision making.” How to make better decisions and get better results, Tom Davenport February 2010 Harvard Business Review

Your interest and development in the field of Data Analytics is related to

  • quantitative disciplines for business decision-making support,  in understanding the role of data/technology in organizational problem-solving processes.
  • self-learning and capacity to dig deeper every topic in an ongoing way to follow to enhance expertise. Every topic is very wide/deep and requires extra effort to master it.
  • dealing with data, programming, statistics, modeling process and information systems.
  • the search of the answer to the questions why/what/where/HOW regarding  problems resolution.
  • testing several options to solve problems and experiment with tools and concepts. Maintaining curiosity and engagement in looking for alternatives to find solutions to problems. A problem can have several ways to tackle it.
  • a commitment to try, try, try,… in order to refine the answers in every opportunity that you have new data, algorithms, methods, techniques.

 The learning curve can be different for everyone, it depends on individual and organization background in:

 1. Data manipulation and work in quantitative problem resolution.

 2. Courses in statistics/applied math/computer science/business.

 3. Levels of using specialized tools for modeling purposes and exposure to a programing language.

 4. Self-learning and problem solution orientation.

 5. Self –driven to find solutions to problems based on the search of using the best possible method and technique available.

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