Business basics for Data Scientists

Data scientists are equipped with the knowledge and experience in machine and deep learning. They thrive on maths and statistics and pretty confident in using SQL and python. However, that doesn't make them a perfect candidate without being familiar to business basics. An employer today looks for a candidate who is skilled and also understands that data is business.

If you are data scientists trying to break new ground, here are a few basics you need to well versed with.

Understanding business objectives and data-based information needs

Data scientists should take the business strategy into consideration during data collection and interpretation process. This helps you provide analytics of a competitive edge. Also, always keep your audience in mind as each audience has different needs, even if the overall strategic objective is the same. Once the audience is identified, it becomes easy to generate data that is relevant to each audience.

Collecting the right data

A data scientist should ensure that the team collects the data that is relevant and useful. A company usually has two types of data, quantitative for numerical data or qualitative for non-numerical data. Usually, the quantitative data is collected automatically from operations, or via surveys and questionnaires which is easy to analyze and represent visually. Qualitative data uncovers the factors influencing certain behaviour. Focus groups and peer-to-peer evaluation are some of the methods for collecting qualitative data.

Obtaining the right data greatly affects the company's decisions and implementation of competitive strategies.

Analyzing the data to gain relevant insights

A data scientist should interpret and contextualize the data in the best way possible to extract valuable information. OLAP, data mining, text mining are a few tools that help extract, analyze, and summarize information from large text datasets. There is also multidimensional technology which helps view data from different perspectives. Take advantage of these tools to reach relevant and actionable insights.

Communicating the data effectively to inform decision making

Now that you have gained the data, focus on presenting it a clear and compelling format that suits the specific needs of the decision-makers. To make the presentation truly informative and engaging, use graphs and narrative together. Keep the report engaging enough for your audience to see the big picture and enable them to derive business value from the collected data.

Though the role of a data scientist technically ends here, it is recommended you to understand how decision-making works. Hence, make sure that the insights you provide become the basis of development. Create a strong impact on the company's desire to learn and improve.

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