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!Data Quality is STILL a fundamentally unsolved issue!
Having met a couple of customers in the banking sector this week in South East Asia, a real pain that have been constantly shared was data quality. That's due to incomplete information, inconsistency, lack of standardized entries and so on. Issues that we all probably know of. Not that's a surprise of any sort, but the fact that traditional ways are still used to tackle the ever existing and growing issue is what puzzles me. In an ever evolving world where we are witnessing advancements in AI and other areas at unprecedent pace, and seeing organizations still struggle with foundational challenge should not be the case. It goes without saying the importance of proper data foundation. Anything less than that would lead to improper analytics, entity resolution, decisioning, etc. I'll probably need to (and will) write a blog about this in the coming weeks. I'm keen to hear from anyone about their approach/vision in tackling data quality issues, and what are some of your most pronounced challenges? Cheers
Network Analysis of ICIJ data shows how to stop Russian oligarchs in their tracks
I thought this was really cool and wanted to share. Complex systems of secrecy: the offshore networks of oligarchs: and to really rein in Putin’s allies’ wealth, governments should target their financial enablers, a new study suggests, read more here: Analysis of ICIJ data shows how to stop Russian oligarchs in their tracks - ICIJ To really rein in Putin’s allies’ wealth, governments should target their financial enablers, a new study suggests.To use a Graph DB or not?
Graph databases (Graph DB) are gaining popularity as an alternative to traditional relational databases due to their ability to manage highly interconnected data. However, the decision of whether or not to use a Graph DB is dependent on several factors and is not straightforward. Some users of Graph DB technology have expressed frustration due to a lack of tangible business outcomes after investing in projects for two years or more. They are now questioning the need to continue using the technology and paying for licenses and maintenance. Some of the most complained about aspects were performance issues, complexity and flexibility. It is essential to note that Graph DB technology is not a "magic stick" and requires significant pre-work to create meaningful connections between data. Despite its advantages in managing complex data relationships, Graph DB is a data store after all, and has some shortcomings. If you have experience using Graph DB , I would love to hear about your experience and what use cases you've use/ed it for. Cheers
