Overcoming Obstacles: Guidelines for Data Management

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In just a year or two of work, we have accumulated a valuable amount of data analysis that could drive our growth in the use of better tools, but how do we turn that chaos of information into strategic clarity?

Challenge: The information paradox
At first, intuition and recent experiences guide our decisions. However, we soon realize that we lack the historical information necessary to identify patterns of behavior in our company.

We are at a critical point: we are ready hungary telegram data to make decisions based on real data, but our records are full of inconsistencies and errors.




In other related articles, we have already talked about the importance of having up-to-date information and quality data sources. Now, we are going to focus specifically on the contribution that data analysis can provide to your SME.


Standardization and consistency: Establishing a clear and consistent structure for data collection and storage is essential. From the use of enterprise resource planning (ERP) systems to the adoption of uniform formats in spreadsheets, standardization ensures consistency of information.
Data Cleaning: Before performing any data analysis, it is important to clean and validate the data. This involves identifying and correcting duplicates, formatting errors, and missing data. Tools such as Automatic Replication and Field Validation can streamline this process.
Smart Segmentation: Divide your data into meaningful categories that reflect your operation. This can include customer, product, or location segments. Smart segmentation makes it easy to identify specific trends and opportunities within your business.
Process Automation: Leverage technology to automate data collection and processing. From system integration to the use of machine learning algorithms, automation enables you to gain real-time insights and make agile decisions.