Information: value and risk

Generating value with data

All businesses are based on data Y, today, most companies collect them, they manage and use for two basic purposes:

• Operational, necessary for the normal operation of the company: would be the purpose behind the Operational data.
• Interpretative. The analytical use of data supports the goal of improving the business and its prospects.

When analytical aspects manage to permeate the level of business operation, the company is able to exploit the knowledge generated and create value to provide feedback. Acquire this continuous flow of data and ensuring their follow-up means minimizing deviations and consolidating strengths.

Both operational and analytical use scenarios should have a high data quality handled. This suggests the need to establish processes that ensure these levels are achieved and expectations are met., such as:

• Operation processes, analysis and value creation.
• Own self data life cycle.
• The different challenges that organizations face in terms of information and its management.

Variables that directly influence the value of the information

The law of conservation of energy states that “energy is neither created nor destroyed, it just transforms”. Applying this premise to the business world and data management, It's easy to see that Data is not self-generated, rather they are the product of the conjunction of elements and events such as sales, purchases, etc. processes in which different variables intervene (endogenous and exogenous) that invariably affect your creation.

A transactionThe "transaction" refers to the process by which an exchange of goods takes place, services or money between two or more parties. This concept is fundamental in the economic and legal field, since it involves mutual agreement and consideration of specific terms. Transactions can be formal, as contracts, or informal, and are essential for the functioning of markets and businesses.... o evento en el mundo real da lugar a datos, composed of characteristics and qualitative elements and / or quantitative.. These dates, once conceived, will go through their own life cycle and will be adapted to different information requirements as necessary for the business, living as its relevance dictates.

During this trajectory will be mixed with other data, interacting to generate valuable information without losing its original essence, but it will never die in time. A data can be destroyed, but not without first affecting the facts and the environment of the business context that originated it.. Therefore, you can also say that data is not self-generated or destroyed, but they transform.

The great challenge for organizations today is ensuring that their data maintains integrity from its source, ensuring they are accurate and reliable. Data that does not contain these qualities can be mixed with others to generate information that, at the same time, will lack reliability and therefore, will result in a solution of Business intelligence high risk for decision making.

The three most common factors that can lead to a data with low integrity or completeness son:

1. The human factor: that during registration, copying or transferring data can inject errors.
2. Inconsistency in business operations: due to lag problems or the way used to process and / or update the data.
3. Defects when integrating and reconciling data from different sources: Due to its original incompatibility or due to subsequent errors during the integration process.

That is why, When it comes to guaranteeing the integrity of the data, it is essential to ensure the foundations for a correct integration, orientada a Business Intelligence.