At the heart of things, an enterprise knowledge graph supports decision and process augmentation based on linked data. These models of knowledge domains are created by subject matter experts with the help of machine learning algorithms. They live as virtual data layers on top of existing databases or data sets to link both structured and unstructured data at scale and across disparate data silos. Introduce new data and the graph…

Ideally, a machine learning engineer would have both the skills of a software engineer and the experience of a data scientist and data engineer. However, data scientists and software engineers usually come from very different backgrounds, and data scientists should not be expected to be great programmers, nor should software engineers be expected to provide statistical summaries. Nonetheless, a background in machine learning algorithms and how they can be implemented…

Given the high volume of erroneous data floating across the enterprise, it is increasingly difficult for industry operators to make business decisions or plan any course of action based on such poor-quality data. Moreover, what’s the point of applying advanced analytics or BI technologies on data that is flawed? A whitepaper titled Getting Ahead of the Game: Proactive Data Governance reviews the role of Data Quality (DQ) in an enterprise…

The Internet of Things (IoT) is not, by itself, a technology. The IoT is the “concept” of connecting electrical devices to the internet. This includes smartphones, washing machines, coffee makers, lamps, and wearable devices. The IoT also applies to the components of machines, such as jet engines or the drills of oil rigs, and is used by smart cities and manufacturing plants. The use of the IoT is also changing…

With the rising concern surrounding the manipulation of data and misuse of statistical methods in Data Science, it is becoming imperative that strong Data Governance policies and practices are put in place to curb any degeneration of data and the scientific methods used to arrive at data-driven conclusions. Data Governance (DG) is expected to play a key role in future Data Science (DS) practices as it offers phased, validity checks…

Graph databases are distinguished by relationships. Users can query, for example, the connections that relate a customer to an account that he or she owns, or how many degrees of separation exist between Kevin Bacon and Audrey Hepburn. Ontologies are a key underpinning, providing a data model to describe things in a database. Metadata and data quality matter so that appropriate conclusions are reached — just which Audrey Hepburn are…