Recording for The Power of “Contextually Connected Entities” for Maximising Decision Intelligence
Hi everyone! If you've missed the webinar on The Power of “Contextually Connected Entities” for Maximising Decision Intelligence, you can now watch it on demand using the below link! https://info.quantexa.com/decision-intelligence-webinar-apac-deloitte We're keen to hear your thoughts on the topic, so please leave it below! CheersWhat is Entity Quality?
Most organizations are concerned with data quality. What's usually neglected is the Entity Quality. But what exactly is Entity Quality? Entity Quality, or EQ for short, is a way to determine the quality of a resolved entity. Say we get a resolved entity based on matching 3 different records, based on some matching logic. The question that arises here is: do these 3 records actually represent the same real-world thing/entity? How confident are we that they do? Entity Quality is about that. It's a way to give end users confidence, via a calculated/aggregated score. At Quantexa, we provide Entity Quality Scoring, or EQS, a way to provide confidence scores for resolved entities. It determines over/under linking in entities. Check out a nice short blog on the entity quality overlinking tool that has been developed based on this functionality. That said, this functionality is embedded in the product, and users can view the generated scores easily, and investigate any entity (if/when needed). Do you measure entity quality at your organization? If yes, how? If no, do you think this can be useful? Please share your thoughts!Exploring the Challenges of Achieving a Single View in Datawarehousing
When it comes to achieving a single view of individuals or businesses in Datawarehousing, several key insights emerge: 1️⃣ Data Integration: Integration is a critical aspect. Organizations often struggle with merging data from disparate sources such as customer databases, transaction systems, and marketing platforms. Ensuring seamless data integration is essential for a unified view. 2️⃣ Data Quality: Data quality plays a vital role in establishing a reliable single view. Inaccurate, incomplete, or inconsistent data can hinder decision-making and analysis. Implementing data cleansing processes and validation mechanisms are crucial steps towards maintaining high-quality data. 3️⃣ Data Silos: Data silos, where information is isolated within different systems or departments, pose a significant challenge. Overcoming these silos requires breaking down barriers, implementing data governance practices, and establishing data sharing mechanisms. 4️⃣ Business Context: Contextual understanding is crucial for creating a comprehensive view. Data needs to be interpreted within the specific business context to derive meaningful insights. Adapting to evolving business requirements and aligning data consolidation efforts accordingly is vital. Questions I always have and continue to ask are: 🔸 How do you address the complexities of data integration when combining data from diverse sources? 🔸 What approaches have you found effective in ensuring data quality throughout the process? 🔸 Have you encountered challenges in breaking down data silos? How did you overcome them? 🔸 How do you incorporate the business context into your data consolidation efforts? 🔸 Are there any specific tools or technologies you recommend for achieving a single view in Datawarehousing? Please share your thoughts and let's learn from one another!
