Data is a critical asset to organizations today and the growing volume of diverse data generation is making data management and governance increasingly important and challenging. Managing the ever-increasing complexity in data sets has given rise to the concept of big data, but what exactly is big data, and why is it important for businesses?
Big data can be explained with the three Vs—volume, velocity, and variety. Big data sets are massive in volume with disparate sources that make processing through traditional systems cumbersome. Big data velocity is the speed at which data is being generated these days, which is another factor making organizations move toward adopting distributed processing systems. Replacing such traditional systems, big data processing and distribution software and big data analytics software have been adding value to an organization’s tech stack by delivering data-driven critical business insights and improving operational efficiencies.
Tech giants use big data tools for data warehouse optimization, predictive support, and customer sentiment and behavioral analytics. E-commerce giants like Amazon have been focusing on improving customer experience through strong recommendation engine build using big data. They heavily invest in big data tools that support their shipping and pricing models by helping them predict purchase orders and further optimize warehouse storage systems. They can also track and analyze user activities, order history, and product availability to enhance the customer experience.
Data-driven organizations often fail to reach their maximum potential due to data mismanagement. Most organizations, realizing the importance of data, have worked on building robust systems to collect and access data which has resulted in the formation of large pools of raw data. While the value exists in the raw data, it is difficult to discover where the data came from, how to search for specific or required data, whether the data quality can be trusted, and what exactly the data means. To prevent these data assets from turning into liabilities, organizations are now developing and implementing data governance software. These systems help users understand data and set data quality benchmarks that ensure the usability, value, and integrity of the data they possess.
How can organizations make data governance work for them?
Organizations, specifically enterprises, generate several gigabytes of data every day. Although this data is mostly in unprocessed and unstructured form, it can become a very valuable asset if structured properly. This high-quality data can then be used to gain critical insights to facilitate better decision making, reducing the risks involved in new product development and providing a competitive advantage. This, in turn, increases overall revenue.
What is Data Governance?
Data governance is a formal and systematic design of processes, technology, and people that allows organizations to leverage data as an enterprise asset. It offers businesses the data structure and data literacy that they require to turn all the raw data into valuable insights.
Data governance allows this type of transformation irrespective of the data environments like data warehouses, traditional databases, and more. Businesses use data governance tools to maximize operational efficiency and profitability. However, big data environments like data lakes are most susceptible to the systemic problems that lie around data lineage and data catalogs when data is in a poorly structured format.
Role of data governance in big data environments
Data governance is a diverse concept, it is not a simple task, but rather a comprehensive framework that helps businesses perform better and make improved decisions. Data governance tools typically include a data dictionary, data lineage (to define the data flow path), business glossary and data usage, sources, relationships, and dependency. The software also assigns proper ownership among data owners, stakeholders, and stewards, and establishes accountability. Additionally, there is a mechanism for solving issues and managing the inquiries that might arise.
Historically, data governance has been associated with regulatory compliance, but the actual role of data governance extends far beyond ensuring compliance. Metadata helps organizations get analytical insights, and its management is an important component of data governance. Data governance also has a prominent role in data quality improvement as organizations evaluate how they can improve, assess, and report on the overall quality of their data.
Challenges like data silos, diversity in data, data stewardship, data security, and more that exist in organizations today are resolved with the help of established data governance. Different elements like data usability, cataloging, quality, and accessibility can directly improve through data governance. Big data environments have a lot of potential for gathering important insights, but without the presence of proper data governance, organizational collaboration, support, and accountability, they’re simply black holes of data that go unused.
The principal element required to govern these big data environments is to be able to define and manage the data throughout the data supply chain. This process starts with data moving into the organization and penetrating the internal environments which could be a data lake or a data warehouse, and then it proceeds through and moves along the entire data lifecycle.
|Some significant concerns regarding data supply chain include:|
A comprehensive data governance program has the answers to all of these questions and it offers a fitting framework to make organizational data reliable, usable, and understandable, without which business decisions would be based on incomplete, inconsistent, and unreliable data. However, data governance is beneficial for more than just data management.
Data governance is not limited to data management
Data governance is gaining a lot of attention with the emergence of big data environments and the demand for the democratization of data. Increased data usage and demand for insight-fueling data are the main reasons why data governance is so important in the age of big data environments. Well-planned data governance requires a centralized and business-oriented model of governance that focuses on understanding all the data assets across the entire organization. When all of this is combined with the proper tools, enterprises can rest assured of a holistic understanding of their data.
Big data has the potential to drive real business insights and results, but only if organizations are able to effectively govern and extract value from the data. The current prevalence of big data environments should drive increased adoption of comprehensive data governance frameworks and tools over the next few years.