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These issues dont just hinder next-gen analytics and AI; they erode trust, delay transformation and diminish business value. Data quality is no longer a back-office concern. In this article, I am drawing from firsthand experience working with CIOs, CDOs, CTOs and transformation leaders across industries.
These tools empower analysts and data scientists to easily collaborate on the same data, with their choice of tools and analytic engines. No more lock-in, unnecessary datatransformations, or data movement across tools and clouds just to extract insights out of the data.
Keeping data quality high ensures that the insights your end-users pull are aligned with reality and can help them (and the company at large) make smarter, d ata-driven decisions , as well as pipe quality information to customer-facing apps. . What is data integrity? Process-driven data integrity: Getting data generation right.
Independent data products often only have value if you can connect them, join them, and correlate them to create a higher order data product that creates additional insights. A modern dataarchitecture is critical in order to become a data-driven organization. Mike is the author of two books and numerous articles.
I last wrote about the process of creating a Data Strategy back in 2014 and – with the many changes that the field has seen since then – am overdue publishing an update, so watch this space. I’ll be covering this area in greater detail in the forthcoming article I mentioned in the introductory paragraph. [3].
This article is not about Marketing professionals, it is about poorly researched journalism. Prelude… I recently came across an article in Marketing Week with the clickbait-worthy headline of Why the rise of the chief data officer will be short-lived (their choice of capitalisation). …and Fugue.
Harnessing the power of advanced APIs, automation, and AI, these tools simplify data compilation, organization, and visualization, empowering users to extract actionable insights effortlessly. These tools seamlessly connect and consolidate data from diverse sources, ensuring cleanliness, structure, and aggregation of data in various formats.
We could give many answers, but they all centre on the same root cause: most data leaders focus on flashy technology and symptomatic fixes instead of approaching datatransformation in a way that addresses the root causes of data problems and leads to tangible results and business success. It doesn’t have to be this way.
This article endeavors to alleviate those confusions. This adds an additional ETL step, making the data even more stale. Data lakehouse was created to solve these problems. The data warehouse storage layer is removed from lakehouse architectures. Metadata plays a key role here in discovering the data assets.
Everyone’s talking about data. Data is the key to unlocking insight— the secret sauce that will help you get predictive, the fuel for business intelligence. The transformative potential in AI? It relies on data. The good news is that data has never […].
The company started its New Analytics Era initiative by migrating its data from outdated SQL servers to a modern AWS data lake. It then built a cutting-edge cloud-based analytics platform, designed with an innovative dataarchitecture. It also crafted multiple machine learning and AI models to tackle business challenges.
Data Environment First off, the solutions you consider should be compatible with your current dataarchitecture. We have outlined the requirements that most providers ask for: Data Sources Strategic Objective Use native connectivity optimized for the data source. addresses).
Recently, NI embarked on a journey to transition their legacy data lake from Apache Hive to Apache Iceberg. Technical recap The AWS Glue Data Catalog served as the primary source of truth for schema and table updates, with Amazon EventBridge capturing Data Catalog events to trigger synchronization workflows.
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