In today’s evolving digital landscape, organizations are under immense pressure to innovate and adapt swiftly. Digital transformation is no longer a luxury but a necessity for businesses aiming to stay competitive. At the heart of this transformation lies data – a critical asset that, when managed effectively, can drive innovation, enhance customer experiences, and open […]
Building a Modern Data Platform with Data Fabric Architecture
In today’s data-driven landscape, organizations face the challenge of integrating diverse data sources efficiently. Whether due to mergers and acquisitions (M&A) or the need for advanced insights, a robust data platform with streamlined data operations are essential. Shift in Mindset Data fabric is a design concept for integrating and managing data. Through flexible, reusable, augmented, and […]
Why Selecting the Appropriate Data Governance Operating Model Is Crucial
To embark on data governance in an enterprise that spans divisions and diverse stakeholders, a well-defined operating model plays a vital role in achieving expected business benefits. Data mesh is a new concept that encourages data democratization within an organization in a decentralized way by promoting data products. Unlike the vintage architecture, which is centralized, data mesh […]
The Emergence of Corporate Data Governance
Within corporations, the theme of governance has been one of the most important themes for decades. Scholars have discussed and debated how corporations can be governed in order to protect shareholder value and resolve agency issues. Companies have implemented a variety of corporate governance mechanisms to ensure effective decision-making and risk management. These include board […]
Do Enterprise Data Contracts Require Data Democratization?
Data contracts are formal agreements between a data provider and a data consumer. They abstractly describe the data as well as the exchange structure, format, and characteristics and schema of information. Why is a data contract required? Data contracts establish guidelines and rules for data sharing, storage, deletion, or archival. Moreover, they ensure that the data […]
Data Governance: How Can It Be a Crucial Enabler for Managing Data Risks?
In July 2021, one of the world’s leading banks revealed a loss of $5.5 billion due to a default by one of its customers. The bank identified the “failure of management and controls” in its investment banking arm as the fundamental cause of this loss. This incident reinforces the importance of a robust governance framework […]
Finding the Right Data Governance Model
Data Governance has received much attention in both the academic and practitioner communities over the past 15 years. Compared to the even more recent concept of Data Governance, IT governance has evolved from the initial concept of corporate governance. In the past year, the interest has increased with the evolution of the modern data stack and cloud adoption, […]
Managing Customer Preferences Using Graph Databases
Managing customer preferences is one of the many aspects of managing customer journeys. Customers interact with organizations across a myriad of digital channels, and being mindful of preferences can increase the trust associated with customers. Read on for how graph databases can help manage these customer preferences. Data privacy and protection policies across geographies make it […]
Best Practices for Managing a Data Fabric
When data is not viable for integration across systems and processes, business users will seldom have the right coverage of data. If people lack knowledge about data and its importance logically, it often becomes a challenge, which leads to less impactful decisions. A data fabric is an architectural capability that can give organizations a “data […]
Data Quality Dimensions Are Crucial for AI
As organizations digitize customer journeys, the implications of low-quality data are multiplied manyfold. This is a result of new processes and products that are springing up. Since the data from such processes is growing, data controls may not be strong enough to ensure the data is qualitative. That’s where Data Quality dimensions come into play. […]