*By Cesar Ripari
Data is an organization's most valuable asset and decisions based on inaccurate information can harm businesses. Therefore, it is crucial to ensure the reliability of data before sharing it with those who use it to make strategic decisions for the business. Data quality is assessed by its ability to meet the specific needs of users within the company.
Organizations are surrounded by countless challenges when it comes to data, from its exponential volume and countless sources to conflicting types and structures, as well as regulatory compliance requirements related to this data. But to be truly data-driven, solving these challenges is not enough. Without high-quality data, the dashboards and analytics organizations rely on for their decision-making processes become incomplete, outdated, or simply wrong.
In the current business scenario, in which information is an essential resource and strategies are increasingly guided by it, data quality and curation stand out as fundamental pillars for the success of any organization. For businesspeople and executives from companies of all sizes, the ability to make assertive decisions directly depends on the reliability and relevance of the data used in analyzing their businesses.
Data quality is a crucial asset in the era of digital transformation, as reliable information drives effective strategies and generates competitive advantages. Although it may seem subjective, quality data can be measured based on five main dimensions: completeness (how complete that data is); accuracy (the guarantee that the data is correct); updating (how much that data reflects the current moment, or as close as possible); consistency (how much that data adheres to its original format and remains consistent between databases) and accessibility (the data must be easily accessible by the people who need to use it, without compromising compliance requirements). Therefore, thinking about data quality is fundamental to a winning business strategy.
Corporations increasingly rely on business data and the insights it provides. This data must be accurate and accessible to all functions, wherever and whenever needed, to make truly data-driven decisions. Additionally, companies also need healthy, accessible data to fuel modernization initiatives such as Artificial Intelligence (AI) programs and machine learning, which entirely depend on data quality to produce results.
Data curation (or data stewardship) is the practice of ensuring that an organization's information is accessible, reliable, usable and secure. It serves to oversee all stages of the data lifecycle: creation, preparation, use, storage, archiving and deletion of duplicates, in accordance with the data governance principles established by the organization to promote the quality and integrity of information.
Data curators, responsible for inventorying corporate data, how it can be accessed and where it is needed, are typically tasked with ensuring its accuracy and availability. But these experts' responsibilities may also include helping to identify and communicate ways in which this business data can create a competitive advantage in the marketplace. The benefits of data curation also include more effective user analytical enablement; clarity in data processes and policies; better quality and reliability of information; fewer errors in decision-making and data-driven initiatives; more widespread use of data for decision-making processes; better documentation and tracking of data and reduction in security risks related to them.
In a dynamic and digital environment like the current one, data curation becomes even more important due to the increase in volume and the need to effectively prepare data for correct processing and analysis. This work demands a deep understanding of business requirements and the ability to provide reliable and relevant data, thus fueling companies' operations, their strategic definitions and their continuous growth.
Thus, curation is responsible for executing data usage and security policies, as determined through enterprise data governance initiatives, acting as a link between the IT department and the business side of an organization. Data stewardship responsibilities largely revolve around risk management, information governance, and data security.
There is no doubt that data is the backbone of business decisions, making quality and curation take on even more strategic importance. These practices ensure that definitions are based on reliable information and extracted accurately. Companies that do not immediately carry out structured analytics and data management work will compromise their success and miss the opportunity to build a future full of strategic insights.
*Cesar Ripari, Presales Director for Latin America at Qlik
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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