This article, authored by Cory Popescu, SIP Writers Forum, is for the IT / Internet professional, but it is equally applicable to anyone, as we use computer and mobile devices in all aspects of our lives regardless of our profession.
For some time, businesses have been looking to build tools to understand their customers better, to improve operational processes, to provide higher quality deliveries. To accomplish this, businesses start using the services of data scientists mining into large databases, and seek upgrades to insight analysts' skills.
The labour dedicated to building business intelligence (BI) mechanisms to incorporate in current businesses is involved at higher degree while creating those tools and to a much lower extent when these devices become functional as expected. When employing the newly built devices embedding machine learning, the organizations gain awareness and powerful observations while decreasing time to respond to change.
Developing intelligent devices require an unconventional thought process to obtain enhanced results since the outcomes of data processing are not known or may only be vaguely imagined at the time of starting the application software. Some of the thought process aspects described below show significant differences between approaching development for traditional versus predictive BI.
A crucial aspect of the thought processes used to create applications involving machine learning is represented by building models with a proactive mindset. The modeller asks questions based on predictive thinking which relate to various business facets, for instance: what benefits emerge from using this data, how to quantify them, how to resolve frequent business issues, such as manufacturing faulty parts.
A predictive approach to modelling also formulates questions regarding the requirement for and the degree of technical skills involved when using the applications based on machine learning, how to present outcome in clear format, how to create groups of users who participate in building the models and what flexibility the predictive models have to generate and validate new models on demand.
Another aspect related to the thought process involved in machine learning refers to calculating for example the number of product offerings versus deriving a solution which predicts the payback on each product offering. Supplying a better view of the customer base, the predicted dollar payback along with tracking key financial variables provide opportunity for increasing company's efficiency and improving their bottom line.
Predicting events and trends become a necessity of the economy with large, expanded datasets which contain relationships and patterns extremely difficult to categorize and make assumptions based on the data. The use of hypothesis and predefined routes represent the foundation of conventional application software development and requires excessive efforts to apply to existing elaborated and complex databases. Leaving this data untapped by the machine learning applications built on the new thought processes would be a total oversight.
References:
University of California, Berkeley: Department of Statistics: Statistical Science: To Explain or to Predict? by Galit Shmueli
Smart Vision Europe: Top ten predictive analytics questions by Rachel Clinton
Cory Popescu
Your comments are welcomed
Click on the links below to read the other articles by Cory Popescu:
Health Concerns of IT Professionals
How Internet Helps Machine Learning
Can IT Professionals Become Savvy Networkers?
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