We all know that AI and BI are distinctly different but often synergize in Industrial applications.
AI refers to modelling data in replicating human behavior while BI refers to streamlining business data to assist with human decision making. AI
How are AI and BI used together?
AI collects data and stores it on its database and servers thus in order to apply visually advanced Analytics than what the automated AI workflow provides. We can export the data from chatbots and AI servers that can be in structured or unstructured and unstructured formats depending on the AI use cases.
AI Data Collection?
Data on AI devices and systems is collected through various means in the form of structured and unstructured.
Where Structured data is recorded in the forms of computer logs and measured activity further fitted and categorized within AI systems through Machine Learning Modelling such as all the data collected throughs sensors in vehicles, homes and the data collected from the activity on Phone Applications. Which can later on be exported in tabular form on sql servers and Excel in tabular form.
While Unstructured data can be recorded through Computer vision on AI devices such as Drones, Rovers, Advanced security cameras in the forms of JSON files, images and videos. Which can be further analyzed and exported as structured data through Deep Learning algorithms, Ensemble techniques and even unsupervised learning algorithms and nosql databases.
What to be done once all data collected is in structured form?
Once all the data generated is in structured form then we can convert it in tabular form on either excel or sql servers and then load it on Business Intelligence software's like PowerBI, Tableau, IBM Cognos, SAS Business Intelligence or any type of relevant Business Intelligence software that suits one’s skills and capabilities.
Once all the previous steps are done then we can generate visualizations like ones below:
Before we decide on initiating any visualization first carefully study the data and the industry of the firms data you are using as we need to know how the industry operates in generating the type of data you possess for example: when measuring the data generated by air-conditioner’s you need to know if the electricity consumed on average by an air-conditioner exceeds the industry average or not to see if the air-conditioner of the particular company is useful to consumers or not.
Story telling the data visualizations you made:
When presenting the visualizations to all relevant stakeholders of it including the company board you need to especially study the data needs of making more profits and improving marketing and sales plans or client operations of the company as the main need for these visualization's comes onto the point of how it can make the company or client more money than what they are earning in the present.
Hey Guys I'm currently working as a Data Specialist in Unilever Pakistan Ltd and as a freelancer in Analytics on Upwork. If you have any reviews, critics or any need of advice for any analytics based project. Feel free to reach out to me on LinkedIn and use my Github/Kaggle repository of python code templates and already made visualization's for implementation or reference. :D