Have you ever thought about how much data you can extract from the moment you wake up until the moment you go to sleep? In fact, during your sleep you can generate data. From the sound of the alarm clock, the temperature of the day, the video with the news in the newspaper, the photos and opinions published on social media, the conversation with your co-worker... The possibilities are countless.
Now, thinking about a company, how much data can it also generate? From the moment she starts her activities until the moment she finishes. We can also consider the factors that affect it outside this period, such as the relationship between employee satisfaction and commuting from their homes to work, environmental issues that can affect the logistics of its operation and government policies that impact the business.
It is true that the amount of data generated is increasing. Therefore, the process of carefully selecting this data to extract meaningful insights and make informed decisions becomes increasingly necessary. This requires professionals who work with data to also become increasingly qualified to be able to analyze trends related to data.
Analyzing the quality, relevance and applicability of data must be a cyclical process in a scenario that is constantly changing, whether due to internal or external changes.
Define the objectives.
The objective should answer a question. How do I increase sales? How do 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 which are the reliable internal and external data sources. From this stage, filter the updated and relevant data according to the objective set in the first step. Ensure that the data is consistent over time and across different sources.
3. Structure the data.
Using ETL (Extract, Transform, Load) techniques, organize the data in a logical and standardized manner, such as in tables or databases, to facilitate further analysis.
4. Use analysis tools.
Data analysis tools (such as Excel, Power BI, SQL, Python, etc.) can assist in the selection, manipulation, and 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 discuss the results with stakeholders.
6. Document the process.
Record the data sources, methodologies, and the criteria behind the decisions made during the data selection process to ensure transparency and facilitate future analyses.
7. Review and update regularly.
Reviewing is important to ensure that the data and analyses remain aligned with the company’s objectives and to make adjustments as external changes occur.
By ensuring effective data selection, your company can transform information into valuable decisions. Shall we discuss how to implement these practices?
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