Data Governance is a topic mistakenly overlooked by many companies. Today, it represents an indispensable prerequisite for any organization that aims to remain competitive in the market in the coming years. In fact, not having a proper data governance framework means relying on business management based on ideas and intuitions not supported by real data, precluding oneself from the innovations of Business Intelligence & Advanced Analytics, remaining a step behind, or worse, making choices based on incorrect, incomplete, and fallacious data.
What is Data Governance?
Data governance is the process of defining “decision rights and accountability framework to ensure appropriate behavior in the valuation, creation, consumption, and control of data and analytics.” (Gartner Glossary).
It refers to a system of managing information (data) capable of generating value, applicable both economically and in terms of knowledge. The fundamental issue is the reliability and validity of the data: the collection, manipulation, and extraction of information must ensure the availability of meaningful data that can be directly used to make the best decisions.
With a good data governance system, organizations can base their decisions on valid and certain information, comply with security standards, and adhere to specific regulations in data management (consider the security criteria that must be guaranteed by a system handling health and sensitive data to comply with legal standards).
Data governance e data management: differenze
According to the definition provided by the Big Data & BA Observatory, “Data Management is the development, execution, and supervision of plans, policies, programs, and procedures that ensure, control, protect, and enhance the value of data assets and information throughout their lifecycle“.
Data governance, on the other hand, designs the data treatment model to meet the aforementioned points: efficiency, effectiveness, security, regulatory compliance, etc. This is why it is important to discuss frameworks, roles, data security standards, access management, and segregation.
Data Governance and Business
Every organization has an information asset of data that holds enormous economic and competitive value. However, not all are able to extract the right information; poorly digitized and structured companies risk drowning in a sea of unusable data, mistakenly viewing it as a hassle to manage rather than a resource to be valued.
It is therefore important to have data management and treatment processes that allow for the transformation of elementary information into meaningful business insights. Equally crucial is ensuring access to high-quality data to avoid the risks associated with poor data management. Consider the consequences for a company basing its strategic assets on inaccurate, if not entirely wrong, information due to errors and flaws in the data governance system!
Data Governance Framework
Data governance defines specific control rules through a framework. While similar requirements and needs exist for companies operating in certain sectors (think of banks, insurance companies, or scientific research sectors, which are legally required to meet specific management and security protocols), every organization can and must implement its own data governance framework based on its needs.
It is important to establish:
- Roles and responsibilities: Involves strategic and operational choices.
- Security requirements: Often dictated by government regulations, also involves technical and infrastructure choices.
- Management and access control: Determine access policies and operational margins.
- Information segregation: Through the design of specific interfaces.
The advantages of Data Governance
Not all companies feel the need for a data governance system to best achieve their objectives, thinking it is a prerogative of highly structured businesses or specific corporate departments.
A valid data governance framework allows for extracting and generating value from all business processes (finance, sales, production, HR, IT, etc.), thereby optimizing resources, increasing revenues, and making a market difference.
The benefits of implementing data governance protocols in a company include:
- Revenue increase
- Effectiveness of Business Intelligence & Data Analytics projects
- Risk control through the implementation of predictive and prescriptive models
- Resource optimization, waste reduction, and valuing the information assets.
A correct data governance framework is thus the foundation of any Business Intelligence and Machine Learning model, an essential requirement for companies aiming to be on the virtuous side of the Analytics Divide. Leveraging AI algorithms is a significant competitive advantage today and will be the key determinant of which companies will dominate the market in a few years and which will disappear.
Implementing Data Governance in a Company
To implement a data governance framework in a company, consider:
- Available budget
- Prioritize business units where data management efficiency will have an immediate economic return
- Scalability of models, tools, and infrastructure.
There are also fundamental prerequisites for a company that decides to use data to achieve its goals:
- Spread a shared data-driven culture within the company at every level.
- Equip the company with IT solutions and digital processes capable of creating an environment suitable for data collection and the introduction of BI models.
- Have competent figures who can contribute to the development of highly innovative projects (e.g., data scientists and data engineers).
Define your data governance framework by:
- Defining business needs and the role of data governance in achieving goals
- Involving various departments for project success: IT, HR, strategic and operational roles
- Sharing a new operational model that clearly defines roles and responsibilities at every corporate level.
Tools and technologies for Data Governance
There are numerous tools available for effective data governance (SAS, SAP, IBM, in addition to the previously listed platforms). They all manage the three main functions of data governance:
- Data Preparation: Collecting, processing, and transforming raw or elementary data into immediately usable information of high strategic value.
- Data Visualization: Exploring and graphically representing data independently (self-service analysis), a prerogative of Business Intelligence, Business Analytics, and Data Visualization systems (e.g., Qlik, Dataiku).
- Data Catalog: Collecting output data to make it available again and feed other systems, such as predictive and prescriptive analysis models implemented through Artificial Intelligence and Machine Learning.
Choosing the right data governance system for the company is not always simple as it is necessary to compare the features of various tools based on specific needs and business goals. Blue BI’s Comparative Benchmarks are indispensable in this regard as they consider data governance aspects inseparable from Business Intelligence & Analytics solutions and models.
Data governance: the company figures to involve
Data Owner:
Responsible for the quality, security, and governance of data in an organization. He defines the rules for data usage and ensures compliance.
Data Steward:
Supports the Data Owner in the operational management of data, ensuring that it is accurate, consistent, and well-documented.
CDAO (Chief Data & Analytics Officer):
The CDAO is a leadership figure who guides the company’s data and analytics strategy, aligning it with business objectives.
Data Custodian:
Takes care of the technical aspect of data management, ensuring its integrity, security, and accessibility in IT systems.
Data Architect:
Defines the structure and architecture of data within the organization and designs data models, ensuring integration between various systems. He works with the IT team to implement scalable solutions.
Data Protection Officer (DPO):
Ensures compliance with data protection regulations, such as the GDPR, monitoring security policies and intervening in case of violations. He coordinates risk assessment activities in the data field.
We realize Business Intelligence & Advanced Analytics solutions to transform simple data into information of freat strategic value.
