*By Vitor Conde
Having a data-driven culture is a basic premise of digital transformation and this has already become clear to executives in all sectors. This way of operating is what generates new sources of revenue, delivers a superior customer experience and positions an organization ahead of its competitors.
However, data in an organization needs to be of high quality, relevant and readily available, which requires good data governance. This is the set of practices and policies that guarantee the quality, security and effectiveness of data use in an orchestrated manner. This is a crucial approach for data to generate good business insights.
Evolving on this topic also contributes to addressing the paradox faced by many companies that, on the one hand, make intensive use of data to drive their business models and, on the other, face major challenges in managing their own operational data. These obstacles include poor data quality and a lack of data governance, which need to be overcome to promote more disciplined and efficient growth.
From a technical point of view, when we think about the components of an effective data governance policy, it is necessary to have well-structured processes. This includes organizing teams, as well as managing and controlling the data lifecycle. This is what will guarantee access, as well as the extraction of relevant insights for the business.
On another related point, it is also necessary to define roles and responsibilities. Here, the decision can be for a centralized, decentralized or hybrid approach. Defining who owns the data from the entry point to decision-making based on that data is essential. This effective management guarantees robust data governance, transforming data into valuable assets for the company.
From an architectural point of view, defining the technologies that will be used for data storage and processing is important, as are clear policies for the data lifecycle, including purge procedures.
Data quality and metadata management are also vital aspects of effective governance. Metadata provides an overview of available data and helps with quality control, ensuring accurate and reliable data.
As with any technology initiative, effective data governance needs to be aligned with company objectives. To achieve this, the support and involvement of “sponsors”, that is, leaders in the organization who understand the importance of this approach, is fundamental. Without this alignment, data governance may not deliver its full potential, or even fail.
Furthermore, other aspects relevant to leadership include organizational culture. To ensure the success of a data governance strategy, leaders need to communicate data-related strategies and goals as clearly as possible.
Finally, it is also necessary to implement an education pillar for all employees in an organization, so that everyone knows how and where to extract the data they need. At this point is one of the main challenges to be overcome by companies that want to evolve in data governance. An antidote to this scenario is to have continuous training on data and maintain effective communication on this topic between areas – which requires strong leadership and a continuous commitment to a data culture.
These technical and management nuances discussed here show that data governance can be a complex but essential field that requires an integrated and strategic approach. A holistic view that considers this front from a technical, organizational and cultural perspective can help companies manage their data effectively, thus improving decision-making and boosting business success.
*Vitor Conde is a customer advisor at SAS.
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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