business analytics

Your credibility as a data analyst depends so much on the way you manage data and report it to readers, who more often than not would be a key decision-making group in an organization. In the current environment where Big Data Analytics is ruling the roost, analysts are playing a huge role in effectively managing data with new age concepts in preparation, mining, and refining before going ahead with analytics. A foundational experience in data analytics courses enables a modern analyst group to find new ways in data cleansing, data standardization, and data reorganization / re-engineering. All these play a very important role in the data analytics journey before you can proceed with advanced programs in data warehouse management techniques.

If you are starting with a business analytics career only recently, focusing on an important aspect of problem solving would become an important subject of discussion in data analytics courses.

What is the Problem-solving approach in data science?

Problem solving approach is a highly revered data science skill in the industry that pertains to the many mathematical, statistical, and logical reasoning techniques used specifically to identify a problem and attempt to solve them using analysis, theorem, and analogies. Commonly used problem solving approaches include trial and error, A/B Testing, null hypotheses, means-ends analysis, regression analysis, and difference reduction. Recently, we have seen a massive influx of Big Data analytics concepts in the problem solving techniques powered by AI and machine learning capabilities. From real-time analysis to unidirectional problem identification, there is a lot happening with data and analysis pertaining to people and tools.

Common problem-solving opportunities where data science is applied

In the last 5 years, I have witnessed an enormous level of innovation happening in the world of data science applied directly to problem solving techniques. A majority of these are applied in the fields of Internet marketing and advertising, email promotions, virtual chats, and video analysis. If you are in the content marketing industry, you are more likely to come across various Google products that provide analytics for various problem-solving cases. For example, the use of analytics to identify engagement levels across various geographical regions, devices (mobile, tablets, desktop, etc.), and source of engagement (physical store, search engine, or email).

A marketing team is likely to use this information to solve problems associated with revenue generation and customer lifetime value. The same set of analytics could be used by financial analysts to identify potential opportunities in new markets where services could be expanded via mergers and acquisitions, new hiring, and so on.

Different problems require a different set of data analytics approaches. However, a trained analyst would always use data to advantage and apply alternative approaches to balance outcomes. These could involve data organization around data warehousing, inclusion, and presentation. As data warehouses become more and more simplified, we are seeing the rise of “readymade data analytics” and customized data analysis concepts that are tailor made to counter problems with rational and clear approaches.

Leave a Reply

Your email address will not be published. Required fields are marked *