Covid-19 has dramatically accelerated the pace of digital transformation in organizations. The result was an increase in the digital data collected. But remember that computers also process senseless incoming data, subject to human error. In a short time, huge dumps of “junk data” emerge.
Data and Digital Transformation
In 2022, investments in digital transformation are projected to amount to $ 1.78 trillion. By 2023, transformed organizations are expected to contribute more than half of global GDP.
Digital pollution: where are we?
Digital transformation affects corporate culture, pushing it towards more agile ways of working, based on the use of technologies, including Artificial Intelligence.
It is common to believe that AI can surpass human intelligence. Instead, we must keep in mind that Artificial Intelligence was created to shorten the processing time of the human mind, but as a machine it will remain “stupid”, limited to the tasks for which it was created.
As previously written, there is data that computers process even if it is foolish. Thus, we create the Garbage-In, Garbage-Out (GI.GO) effect. Unfortunately, most organizations accumulate data without wondering what it will do, if it is correct, usable. They are stored in server farms or clouds and will remain there without ever seeing the light of day. In a short time, huge dumps of data emerge.
How to avoid data dumps?
The data is the recording of an event in a specific period of time. From a set of data we can carry out analyzes, predictions and build more effective strategies for the future.
The success of a company today is connected to the use it makes of the data available, to how it creates value from them. But collecting them (or rather, accumulating them) is not enough. There is an urgent need to carry out a census within organizations to understand which data must be discarded, which updated, understand its obsolescence, the state of use and which new data must be collected to create innovation.
It is necessary to transform the GI.GO effect into GI.VO (Garbage-In, Value-Out).
Let’s save the future of digital with Green Data
In the article “Big Data and Green Data: the digital foundations for Sustainability” we extensively explained the connection between data and a sustainable future. This time we see more closely how the concept of Green Data contributes to always having valuable data.
We assume that data dumps will continue to grow, polluting the “digital world”, if we don’t act now with the right methods and tools. Green Data arises precisely from the need to do “digital cleaning”. It is a method of data collection that starts from the design and management of the birth and growth phase of data to ensure that it is transformed into useful information to create value for organizations.
Having clean, structured and classified data means:
- Avoid the main errors encountered in the event of inconsistent data.
- Reduce costs
- Increase team efficiency
- Improve customer care and customer satisfaction
- Develop more efficient strategies
The solutions of Pragma Etimos
At Pragma Etimos we develop structured data models and classifications (Intelligence Data Table). These are the fruit of 30 years of work and we use them as a basis for the construction of neuronal models, territorial links and semantic analyzes.
OUR SERVICES
- Classification and intelligent correlation of the data acquired in specific information domains
- Semantic clustering
- Sentiment analysis
BENEFITS
- Quality actionable data
- Enriched database
- Strategies based on consistent and comprehensive data
- Reduce time and costs
MORE TO EXPLORE …
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It is a short sentence. Data is vital energy. I hope to create a great news.
In conclusion Semantic Clustering is cool. I talk about it t. As a result, it fell over.
Data Intelligence is very important. Today we talk about Semantic Clustering.
I enjoy his company because he always tells interesting stories. For example about Data Cleansing.
Data Cleansing is Data Quality. Infact, they clean data and transform them in quality data.
This article is usefull? Great! In this paragraph, I’m going to discuss a few reasons why practice is important to ICT skills.
Fantastic!
Whats the name of V of Big Data?
Velocity, Value, Vericity, etc. For example, yuppy. Moreover, that number rises to as much as 90% when you put theory to practice. In conclusion, following up explanation with practice is key to mastering a skill.
The passive voice is a monter, moreover. Firstly, the only way to truly learn a skill is by actually doing what you’ll have to do in the real world. Secondly, I think practice can be a fun way of putting in the necessary hours.
Data intelligent is on the table. Are you sure? Yes, I, am. It is fantastic! I’m tired. Therefore, I’m going to bed.
It is a branch of LNP.