It is increasingly evident that, when it comes to data analysis, quality is preferable to quantity. Otherwise, companies run the risk of going through the so-called Analysis Paralysis, when managers are unable to make decisions because of too much information.
Efforts to gather higher-quality data are worth the investment, as, according to Gartner's estimate, the poor quality of these assets costs companies an average of $12.9 million a year.
This calculation takes into account the following:
- Direct impacts: resulting from decision-making based on incomplete or wrong information
- Indirect impacts: resulting from the systemic problem of unreliable data. Studies indicate that some professionals spend up to 50% of their working days checking original documents and correcting errors in databases.
Avoiding these expenses and their impacts on the company's finances involves adopting consistent practices to improve the data generated and used by your company.
But what is data quality anyway? Data will be considered of quality when it meets the demands of the user of that information. Therefore, it is essential to understand not only the intrinsic qualities of the data but also to what extent it is accessible and its application is well contextualized within the reality that it will need to represent.
In this text, you will understand four important parameters to evaluate these points and visualize how netLex, as a tool to manage documents and workflows, can help you put them into practice in your company.
1. Intrinsic Quality
To be considered of quality, the data must have some intrinsic characteristics. Among them are:
- Accuracy: the data is correct, free of errors
- Reliability: the source of the data adds confidence to the information
These two intrinsic qualities are exemplified in the following situation: data manually extracted from documents may have low reliability, since human errors are one of the leading causes of content inaccuracy. Information pulled automatically from systems and digital files is highly reliable, precisely because one of the sources of possible mistakes and, consequently, inaccuracies, is eliminated.
This extraction of data for the generation of intelligence is one of the steps of good management of the contract’s life cycle, also called CLM. Get more information about this approach at CLM: what is and how technology can benefit your company
2. Contextual Quality
In addition to data with intrinsic quality, it is also necessary to assess the context in which that information is expected to be used. This is because it does not matter that the data has all the characteristics indicated in the first item of this list if it needs to be contextually adequate.
To assess this requirement, it is important to consider factors such as:
- Generated value: having this data improves the analyzes carried out, generating benefits for users;
- Completeness: contains all the elements that guarantee its usefulness;
Some of the highest quality data to inform operational management, from a contextual point of view, are those extracted directly or indirectly from the company's activities.
When workflows are conducted on automated platforms, it is possible to identify relevant data for analysis and implementation of various management improvements. Thus, for example, you can map and act on the inefficient allocation of resources or even improve coordination between departments.
3. Representational Quality
The representational quality of data concerns its ability to reflect, correctly and adequately, the reality of that object. In the business context, this goes through a main characteristic:
- Consistency: every time that data is used, it will have the same value and format.
One way to ensure consistency of information is to have this data extracted at a single point in the workflow and automatically replicate it in the same system or another integrated program. This prevents the user from filling out identical categories differently and also ensures that any updates will have an impact on all the points at which that data is used.
4. Accessibility
One of the most important qualities of data is its accessibility, which cannot be interpreted without the counterpoint of security:
- Accessibility: condition for easy and quick access to data
- Security: permission control to maintain levels of information security within the institution.
These two qualities can be achieved when the company has a unified platform that makes data available for download or analysis in simplified dashboards. This system must also structure its accesses based on profiles corresponding to the individuals' permission levels based on the information security policies adopted by the company.
Improve your company's data quality with netLex
netLex is a document and workflow management platform that helps improve your company's data quality.
With it, you can extract information directly from your documents and workflows, ensuring reliability and accuracy. All this data will be contextualized and faithfully represent the company's operations. Finally, they will be available in an intuitive and simplified way for users depending on their respective permissions, ensuring, at the same time, accessibility and security.
To see how the platform works in practice, click here and talk to a netLex expert today!
