Digitalization, one of the most important challenges for companies and public bodies of the current historical period. Many people talk about it, some understand it, few exploit its real potential.
With Covid-19, digitalization processes have undergone an important acceleration both for companies and the Public Administration. The result in most cases was to adopt new technologies, collect a huge amount of digital data, before transforming it into something more.
Digitalization: what is it?
Digitalization means going beyond the transformation of analog data into digital data. This change involves the introduction of new technological tools, new processes, new interactions, new roles and company functions.
What are the benefits? Simplified and more efficient working methods through the optimization of time and resources.
The role of data in Digitalization
The importance of data is transversal to all sectors, whether it is public administration, law enforcement agencies or private companies. By introducing online services for citizens or customers, bringing innovation to the surveillance system, opening an e-commerce or making use of a CRM, data is collected.
Back in 2006, Clive Humby, a British data scientist and mathematician, defined data as “the new oil”. Thanks to data, that we are able to have the necessary knowledge to improve strategies, predict market trends, find new business opportunities, understand the sentiment of citizens and consumers to improve services, etc.
How much attention are you paying to the data?
Answer some self-assessment questions about the importance you are giving to the data in your organization:
- Have you implemented a data collection method that avoids duplication, typos and unstructured data?
- Have you foreseen Data Quality operations?
Poor data quality undermines business value. Recent research shows that the estimated average cost of poor data quality is approximately $ 10.8 million annually. With the increase in digitalization there is also a increase in waste data due to poor data quality which further increase the economic waste of a company.
Let’s follow up with the questions:
- What is the purpose of your data collection?
- Are you able to extract the information you need from the data?
- Is there any possibility of analyzing your data in a simple way?
Storing data should not be a “side effect” of the use of digitalization. Data is the basis to improve company processes and strategies. Implementing a technology or switching from analog to digital data does not magically solve all problems. For this it is essential to adopt the most suitable tools to collect and analyze the data that will help us achieve the desired objectives.
Are you looking for a partner for the Digitalization of your company?
Pragma Etimos proposes itself as a technological and innovative partner for Artificial Intelligence, Data Intelligence, Business Intelligence, Computer Vision and Audio Recognition solutions.
High flexibility, design skills and consolidated experience allow us to develop custom-made solutions, thus offering targeted advice, which builds on the 30 years of experience of our founders and the innovative spirit of the young people in our team.
- The advantages of working with us:
- Solutions tailored to the needs of your organization
- Support also post development for maintenance and updates
- Quality actionable data
- You will be able to create strategies based on consistent and comprehensive data analysis
- You will reduce time and costs of business processes
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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.
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.