The power of business data: how to amplify it thanks to Business Analysis
As the Head of Data Science
Put yourself in the shoes of a Head of Data Science who has just started his journey in a new company. He is full of ideas and desire to bring innovation.
Imagine wanting to embrace the challenge of a Real Time Marketing strategy through
the implementation of models such as Churn Prediction and Recommendation Systems. Models that can suggest to those who relate directly with customers the Next Best Action to follow.
Then imagine not having any kind of documentation available. Nothing that describes the business data assets or no clear vision of the characteristics of its customer segments.
The Business Analyst comes into play
This is where the Business Analyst comes into play and its core competencies already covered in part in this blog. On the one hand, the Business Analyst elicits the collection of requirements for the implementation of the models and involves the SMEs to identify the expected output. On the other hand, he immerses itself completely in the research and rationalization of all corporate data sources.
The Data Reply Business Analyst, combining his functional skills in interacting with business stakeholders and his data driven know-how, is able to map and document through standardized and easily consultable templates one of the most precious properties of a company: the data of its customers and its processes.
However, the documentation of data sources, their processing and the technical-functional relationships between them is only the first step of the Business Analyst’s activity.
In fact, the analyst does not limit himself to documenting the information. He uses it to tell the path that leads the client to make certain decisions and the context that influences him, too.
Thanks to EDA (Exploratory Data Analysis) it can also identify deficiencies in the data in order to act directly on the phase of its collection and increase its value.
Ultimately, the Business Analyst, thanks to data story telling, can develop an in-depth knowledge of the customer and his needs. Thus providing the company with new and powerful tools to support the decision-making process. Guiding the implementation of the right use cases of Artificial Intelligence and Machine Learning.