Information Data Processing

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Applied Innovation

Let's find out how to process information through a process that acquires data to be associated with products, services and company assets

Information Data Processing: about it and what it consists of

Information Data Processing is an innovative process by which companies are able to obtain useful data and information. The processing of this data takes place step by step and each step serves to reach the final goal. I.e. to manage the information obtained to improve the various “assets” related to the company (e.g. machines, goods, etc.), to obtain more conversions from leads or even to better manage administrative activities (e.g. accounting). All information and data obtained will be analyzed and processed following a specific process. The first phase is data acquisition, followed by the management of the same and its storage. Then comes the last phase, “delivery” (final extrapolation of the processed data).

What is Information Data Processing?

“Information Data Processing” is a process that allows the acquisition of a lot of information and data related to a specific asset, product, service, activity and so on. The acquisition of information will take place in an analysable and extractable (or, rather, recoverable) format and will serve purely to improve activities, products or services using “raw” information that is rendered usable by the Information Data Processing process, which will have made the info and data collected usable, after having entered them into a specific context.

The key activities (in order of execution)
for proper processing are as follows:



















What is Information Data Processing for?

The main purpose of Information Data Processing is to obtain as much information as possible on certain elements. The same will then prove to be fundamental in making positive changes to the asset analyzed using this methodology. All the information and data acquired will be processed, selected, and then extrapolated. It will then be archived and made available to employees or members of the company. It can be used in both the administrative field (e.g. accounting, wages, inventory, warehouse, etc.) and the commercial field (sales, marketing, promotions, etc.).

The difference between Data Processing and Information Data Processing

The difference between Data Processing and Information Data Processing lies in the quality of the data collected.
In the case of Data Processing, it involves raw data, consisting of numbers, characters, declarations, comments and so on. If taken individually, it is unable to provide relevant information, which is why it is collected in groups and then processed. It will then be placed in a specific context, with the aim of being able to obtain data and information that is actually useful.
Information Data Processing is a sort of evolution of simple Data Processing. In this case, the extrapolated data is processed, selected and organized.
However, please note that the extraction of useful and crucial information (Information Data Processing) will always require the collection of raw data (Data Processing).

The 6 phases of Information Data Processing

Data Collection

The first phase is Data Collection. All the information is extrapolated from the sources available (better if reliable and of quality), including those relating to "Data Leak" and "Data WareHouse". These are two tools for Big Data storage, the first being a kind of raw data aggregator whose purpose has not yet been defined.The second tool mentioned, Data Warehouse, works as a sort of repository of data that has already been filtered and organized to achieve a specific purpose.

Data Preparation

The second phase is Data Preparation. In this phase, the raw data collected will first be pre-processed (cleaned and organized for the next step) and then checked. The check phase serves to eliminate incomplete data, errors, and information that is of no use. In this process, the system will create quality data.

Data Input

After the data has been cleaned up, it is the turn of Data Input. This is a simple step, but nevertheless essential for the successful extraction of useful data. Indeed, in this phase the clean data is uploaded directly to their destination (mainly specific CRMs, such as SalesForce). Consequently, they will be "translated" into the language of the chosen Customer Relationship Management platform. From this step onwards, the data begins to take shape.


The Processing phase, or rather data processing, is used to process data and info previously extrapolated from the various sources. This is done through the use of machine learning algorithms, which in turn appear under different guises, depending on the source (e.g. data leaks, social networks, devices, etc.) and the purpose of the data (diagnosis, understanding Client needs, product improvement, production process improvement and so on).

Data Output/Interpretation

Now for the penultimate step, which involves the use of the Data Input process. In this phase, the data will finally be rendered usable and will be understandable by everyone, even non-data scientists. Employees and members of the company can then access the data and proceed to manage it. Then, it will be possible to analyze the data and use it for their own projects.

Data Storage

The last step, which is certainly no less important than the others, is Data Storage. In this phase. The data collected will be stored and made available for future use.

The data must be stored in such a way as to allow employees and members of the company to be able to access it simply, quickly and, above all, whenever the need arises to consult this "clean", useful information.

To facilitate everything, it would be wise to use an Enterprise Content Management (ECM) solution. I.e. a software that allows the management of data, documents and strategic information owned by any organization (company, public entities, associations, consortia and so on).

Making positive changes to company assets from the collection and analysis of data