Business Understanding, The first stage of Data Science

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Business Understanding

The first stage of Data Science

By Prof. Jonathan Acuña-Solano, M. Ed.
Head of Curriculum Development Academic Department Centro Cultural Costarricense-Norteamericano	Senior Language Professor School of English Faculty of Social Sciences Universidad Latina de Costa Rica
Sunday, August 18, 2019 Post 338 / DS Log 7

Data Science is quite peculiar when it comes to the steps one has to follow in order to start the process of comprehending what exactly needs to be addressed when a company’s problem needs to be explored, understood, and then resolved. But why is it so essential to establish the “business understanding” at the start of the Data Science methodology? Let’s explore some potential answers to this question especially when linked to a very specific problem.

“Business understanding is the first stage of the data science methodology; it provides clarity about the problem to be solved and the data that should be used” (Laureate Education Inc., 2018). When a data scientist is given a problem that needs to be explained and then solved, that query posed by company stakeholders can be misunderstood if this process is not carried out; it is a matter of perception in the end. The company need to explain the problem they have in full to the scientist(s); details cannot be omitted. The scientist(s) must comprehend the problem to help the company formulate the right questions to obtain the right guidance to gather data. With these discussions among the company and the scientist(s), business understanding will help determine that kind of methodological approach needed: descriptive or predictive.

Business Problem Sample:	1) The scientist has to sit with the school’s academic stakeholders to be explained what exactly the problem is and its current implications for the institution, its reputation, and how graduate students are perceived in the work market in the country.
A language school whose students are not obtaining the expected CEFR outcome at the end of its program
2) Understanding how the school is being affected by a poor performance of their graduating students will help stakehoders to formulate the right question(s) to ask to set the data requirements to gather information.	3) This open and sincere discussion with the school academic stakeholders will provide room to comprehend why the problem needs to be given a solution, the reasons why the current state of affairs has to be amended.

“To establish business understanding, structured discussions with different stakeholders must happen so that the research focus (goals and objectives) can be classified” (Laureate Education Inc., 2018). It is crucial to clarify at this point that these “discussions” cannot just be held to ask company contributors what they want in terms of the enterprise’s problem; consultations have to be organized to get to the gist of the research focus needed to find solutions to the problem. Team members must come from different areas in a company; business understanding cannot just be provided by one single individual. And through all these deliberations goals and objectives also need to be clearly stated in the minds of company’s stakeholders and the data scientist(s). Forgetting this simple step in business understanding may trigger the wrong results.

Business Problem Sample:	1) Rounds of discussions must be organized with the school academic stakeholders to fully clarify what the gist of the project and its scope are. The research focus needs to be clearly stated because results can be wrong.
A language school whose students are not obtaining the expected CEFR outcome at the end of its program
2) The group of academic contributors must come from different areas of the department. This is not just about an academic director’s perception; it has to come from all areas that constituted the department.	3) All members of the academic team must have clearly stated -in their mind- the goals and objectives of finding the reasons why students are not achieving the correct CEFR level. Everyone has to embrace the project.

“Once a business understanding is in place, key business partners can remain engaged and provide support and guidance to project members” (Laureate Education Inc., 2018). Company’s team members must remain part of the project; they cannot detach themselves from the data scientist(s) assigned to the business strategy to find solutions to a problem. As active members in the process, company’s partners are present to support the data analysts by providing them with any other vital information (such as databases) to walk the right track towards the finding of (an) answer(s) to the stakeholders’ question(s). The engagement we are talking about is manifested in the establishment of a business understanding and in the guidance needed when scientists may be missing a piece of the puzzle to comprehend the company’s problem.

Business Problem Sample:	1) Though there are structured conversations among the academic stakeholders, they need to remain part of the project and not just stay aside and wait for results. These people help in discovering solutions.
A language school whose students are not obtaining the expected CEFR outcome at the end of its program
2) Academic collaborators support the data scientist(s) when they contribute with information to consolidate the business understanding here linked to the poor CEFR performance of the school’s language learners.	3) Academic engagement will be present all across the process since as team players, they can provide the data science group with guidance especially when a piece of the puzzle is missing in its right position.

As a first stage in Data Science, business understanding is decisive and imperative. Lack of understanding among all participants in a data science team can lead to formulating wrong questions and obtaining inaccurate answers. In the example used in this presentation of facts associated to the language school, there are plenty of people involved in the search for an answer as to why their learners are not achieving the mastery of the CEFR level the program aims at. Their participation in the process to find answers to the questions they pose as central will determine the goals, objectives, and scope of the possible answers they can obtain. As stakeholders they can make better decisions in regards to what needs to me done to help their students become competent English speakers.

References

Laureate Education Inc. (2018). Asking Questions with Data Science. Retrieved from One Faculty: https://dtl.laureate.net/webapps/blackboard/content/listContent.jsp?course_id=_165016_1&content_id=_801203_1&mode=reset

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