We are surrounded by data all the time, whether in our personal routine or in the business world. But how can we transform this vast amount of information into something useful and strategic? Selecting the right data, analyzing it critically, and using it to make smarter decisions can make all the difference.
Have you ever thought about how much data you can extract from the moment you wake up until you go to sleep? In fact, even while you sleep, you can generate data. From the sound of your alarm, the temperature of the day, the news video on TV, the photos and opinions posted on social media, to the conversation with your colleague at work... The possibilities are endless.
Now, thinking about a company, how much data can it generate? From the moment it starts its activities until it finishes them. We can also consider factors affecting the company outside of these hours, such as employee satisfaction with their commute to work, environmental issues impacting logistics, and government policies influencing the business.
It’s clear that the amount of data generated is growing every day. This makes the careful selection of data to extract meaningful insights and make informed decisions increasingly necessary. It requires data professionals to continually enhance their skills in order to analyze data trends.
Analyzing the quality, relevance, and applicability of data should be a cyclical process, given the constantly changing environment, whether due to internal or external changes.
1) Define the objectives.
The objective must answer a question. How can I increase sales? How can I reduce costs? Where should we expand? If the data is not useful in solving a problem, it is not relevant to the business.
2) Identify data sources.
Identify reliable internal and external data sources. From this step, filter updated and relevant data according to the objective defined in the first step. Ensure the data remains consistent over time and across different sources.
3) Structure the data.
Using ETL techniques (Extract, Transform, Load), organize the data in a logical and standardized manner, such as in tables or databases, to facilitate later analysis.
4) Use analysis tools.
Data analysis tools such as Excel, Power BI, SQL, Python, etc., can assist in the process of selecting, manipulating, and conducting exploratory analysis of the data.
5) Conduct tests.
Validate whether the selected data is suitable to answer the questions. Revisit and adjust the data selection as new insights or needs arise, and engage with stakeholders to review the results obtained from the process.
6) Document the process.
Record the data sources, methodologies, and decision criteria used during the data selection process to ensure transparency and facilitate future analyses.
7) Review and update regularly.
Regular reviews are important to ensure that the data and analyses remain aligned with the company’s objectives, making adjustments as changes in the external environment occur.
By ensuring effective data selection, your company can transform information into valuable decisions. Shall we talk about how to implement these practices?
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