Data Strategy
In today’s data world, there are so many options for an EL tool to avoid you developing your own extracting script and to help you gain a LOT of time.
Fivetran, Mage, and Airbyte to mention a few.
You don’t have to maintain custom scripts, these tools come with +300 connectors, basic scheduling, and error handling.
Jeremy • What I Learned After One Year of Building a Data Platform From Scratch
One of the problems with owning your own data is that it ignores the question of what format that data is in. Data formats are sometimes considered carefully, but they can also be hammered out fast to get a minimum viable product out the door.
Byrne Hobart • The Promise and Paradox of Decentralization
The amount of data we now access has grown far faster than the general level of data literacy in companies. Most companies have too much data, it’s stored in too many places, it’s not ‘clean’ and it’s often more confusing than helpful when it comes to what matters – making decisions with it. We need to ensure companies create centralized yet
... See moreTom Goodwin • Digital Darwinism: Survival of the Fittest in the Age of Business Disruption (Kogan Page Inspire)
Quality data is crucial for automation, pattern recognition, and training AI for advanced capabilities. A data-first mindset and a data-focused platform are essential for long-term success
oracle.com • Generative AI Changes Everything for the CFO and Finance
Also, eliminating irrelevant or sensitive information such as personally identifiable information (PII) is crucial to align with privacy standards
DataStax • Retrieval Augmented Generation (RAG) Explained: Understanding Key Concepts
Meta-data management efforts provide for the management and traceability of all components of the BI environment. Meta-data needs range from ensuring that definitions and business rules associated with BI applications are made available to business users to providing source data mapping information to manage the environment or answer questions
... See moreSteve Williams • The Profit Impact of Business Intelligence
Data quality management ensures data is fit for consumption and meets the needs of data consumers. To be of high quality, data must be consistent and unambiguous. You can measure data quality through dimensions including accuracy, completeness, consistency, integrity, reasonability, timelines, uniqueness, validity, and accessibility.
Astasia Myers • 4 Data Trends to Watch in 2020
The purpose of metadata is not simply to describe data and information assets, but rather to proactively answer questions that consumers might have about them. Where did this data come from? How up to date is it? How trustworthy is it? What business process(es) created it? What business process(es) use it? What transformations or filtering have... See more