Transforming Data into Knowledge: Data Acquisition and Storage as a First Step
Any organization or company is capable of making better decisions as long as those decisions are based on data. For this, information capture or data acquisition and its subsequent storage are essenti...
Any organization or company is capable of making better decisions as long as those decisions are based on data. For this, information capture or data acquisition and its subsequent storage are essential. Being able to take advantage of data can be the key to creating and developing successful strategies for any business.
But, What is Data Acquisition?
Data acquisition is the collection of relevant information for any organization, either manually or automatically. We can consider it as a process of transforming information from different sources into data that can be analyzed. Thus, data acquisition and its subsequent analysis becomes a fundamental source of information for making decisions and drawing up strategies.
We have mentioned that data acquisition can be manual or automatic. When we focus on massive data acquisition processes, we must focus on intelligent and automated capture to avoid excessive slowness, high costs, or errors during acquisition.
Automated acquisition is the process by which different technological systems are used to make the entire procedure more agile and efficient, minimizing the possibilities of error. Thanks to the use of these systems, human intervention in data acquisition is, therefore, reduced or completely eliminated.
And once the Data is Acquired, What is Done with it?
Once the data has been acquired, it is necessary to classify, organize, and store the data, and in this sense, database models, database management systems, and query languages have been developed.
The best-known and current technologies and systems for storing and processing large amounts of data would be relational database management systems or RDBMS with SQL as the access and query language for structured data, and non-relational database management systems (not based on schemas) such as NoSQL (Not only SQL) that provide an unstructured or semi-structured approach to data and with the potential for parallel data processing.
Having Reached this Point, What Can We Do to Make Sense of our Data?
Using data as a valuable and fundamental strategic asset is, as we have seen, the most intelligent way to take advantage of the information we have. If we have quality information, we can carry out different types of analysis:
- Descriptive analysis: reflects what has been happening so far. It allows detecting trends and future problems or opportunities. Thanks to this analysis, companies can design strategies taking into account these trends or threats with the aim of anticipating them and making the most of these situations.
- Diagnostic analysis: this type of analysis delves into the descriptive ones. It pursues the use of data to try to discover in detail the reason why something happened. Diagnostic analysis can help us detect and explain the reasons why something happens and try to avoid it in the future or enhance it.
- Prescriptive analysis: possible situations are studied and interpreted if specific measures were taken. It suggests action plans derived from the information extracted from the data collected so that the results can be optimized.
- Predictive analysis: this analysis allows us to prepare for actions that are likely to occur in the future. It is a type of analysis that performs the study of forecasts through probabilities. As tools, it uses techniques such as regression and progression analysis or pattern matching, as well as various types of statistics.
Conclusion
All organizations with their production processes, operations, and supply chains have to become more efficient, agile, and achieve greater productivity, better uptime, and product or service quality.
This task can only be carried out by taking advantage of the data and the digital interconnection of all aspects of the value chain; from product development, through connected manufacturing and supply chains to connected products and services.
To take advantage of the data, we must have the ability to capture, ingest, process, store, analyze, and model any type of data (structured, unstructured, or semi-structured data) and in any location.
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