The Internet of Things (IoT), devices that produce and consume data through the internet, will likely comprise over 207 billion devices by the end of 2024. These widgets generate, consume, and send vast data over business networks. As a result, organizations must include IoT in their Data Governance programs to ensure better integration and legal compliance. Without effective governance, firms […]
How to Become a Data Engineer
The work of data engineers is extremely technical. They are responsible for designing and maintaining the architecture of data systems, which incorporates concepts ranging from analytic infrastructures to data warehouses. A data engineer needs to have a solid understanding of commonly used scripting languages and is expected to support the steady evolution of improved Data Quality, […]
A Brief History of Generative AI
Generative AI has a fairly short history, with the technology being initially introduced during the 1960s, in the form of chatbots. It is a form of artificial intelligence that can currently produce high-quality text, images, videos, audio, and synthetic data in seconds. However, it wasn’t until 2014, when the concept of the generative adversarial network […]
The Impact of Data Silos (and How to Prevent Them)
Data silos often develop unintentionally within businesses, catching leaders by surprise. They hinder cross-departmental collaboration while giving rise to inconsistent data quality, communication gaps, reduced visibility, and increased expenses. The gravity of impact can be gauged from a report by Forrester research, which finds that knowledge workers spend an average of 12 hours a week “chasing data.” […]
What Is Data Completeness and Why Is It Important?
Data completeness is an important aspect of Data Quality. Data Quality is a reference to how accurate and reliable the data is overall. Data completeness specifically focuses on missing data or how complete the data is, rather than concerns of inaccurate or duplicated data. A lack of data completeness is normally the result of information […]
Common Master Data Management (MDM) Pitfalls
Leaders need to trust data within the organization to make sound business decisions. So, many turn to master data management (MDM), a solution to get and keep uniform and accurate data that increases business value. Yet, according to Gartner, 75% of all MDM programs across organizations fail to meet business objectives. Moreover, this trend has worsened since 2015, […]
AI Governance Best Practices
AI governance is meant to promote the responsible use of artificial intelligence for the betterment of humankind. Artificial intelligence has proven itself quite useful in completing a large variety of tasks quickly and efficiently. Unfortunately, it can also be used to support criminal behavior or to create and distribute misinformation. AI governance is an effort […]
Fundamentals of Data Virtualization
Organizations are increasingly employing innovative technology called “data virtualization” (DV) to tackle high volumes of data from varied sources. Data virtualization is widely used in enterprise resource planning (ERP), customer relationship management (CRM), and sales force automation (SFA) systems to collect and aggregate multi-source data. From multi-sourced data acquisition to advanced analytics, this technology seems […]
3 Key Benefits of Pragmatic Data Modeling
In 2024, companies have developed a renewed interest in the benefits of Data Modeling, engaging in pragmatic planning and activities around diagramming requirements. Organizations want to document data architectures to get good Data Quality and overcome challenges. Notably, the resolution to each data incident has risen significantly by 15 hours between 2022 and 2023. Furthermore, 80% of data executives and business leaders say cultural impediments […]
What Is Data Mesh and Why Is It Important?
A data mesh challenges the traditional centralized Data Architecture by advocating a distributed and domain-oriented architecture. Data mesh promotes the idea of treating “data as a product,” where each domain or business unit becomes responsible for its own data products. By doing so, individual domains gain autonomy over their data needs and can make faster […]