A Guide to Data Product Management
A data product manager (DPM) is the person who looks over the development and success of data-driven products within an organization. These products can range from data platforms to analytics dashboards, and their primary goal is to help teams make better, faster, and more informed decisions. Unlike traditional product managers, who focus on physical or software products, DPMs concentrate on data as a product, making sure that it’s high quality and accessible and that it serves a business need. Chris Ebube Roland’s guide to data product management explains the role of a data product manager and how Acceldata can help.
A Comprehensive Definition of Data Provenance
Understanding where your data comes from, how it’s processed, and where it goes is crucial. This is where the concept of data provenance comes into play. It’s more than just tracking data — it’s about ensuring the integrity, security, and transparency of your data throughout its life cycle. This post on Acceldata from Peace Aisosa Osarenren explores the full definition of data provenance, its key components, techniques used to track it, and the challenges and benefits of implementing effective provenance systems.
Understanding Data ROI
In today’s data-driven world, businesses that harness the power of data are miles ahead of those that don’t. But simply gathering data isn’t enough. Companies need to assess whether their investment in data initiatives brings tangible value. This is where the concept of data ROI (return on investment) comes into play. Understanding data ROI allows organizations to evaluate how well their data strategies perform and whether their allocated resources generate measurable results. Without a clear ROI, businesses can lose sight of costs, mismanage their data assets, and miss growth opportunities. In this post on Acceldata, Juan Reyes explores data ROI, why it’s essential, and the key components that contribute to it.
An Introduction to Data Compliance
Data compliance is the way you store, process, organize, and manage sensitive data (like customer details) and personal data (like personally identifiable information) such that it adheres to industry standards and regulatory requirements. It helps maintain the availability and integrity of data and prevents it from misuse, theft, and loss. Data compliance ensures that businesses abide by the rules and regulations set forth to protect data, ensure privacy, reduce penalties for noncompliance, and prevent data breaches. You can find out more in Nimra Ahmed’s post on Acceldata.
A Guide to Data Completeness
In this age of big data, where information is the lifeblood of organizations, data completeness has become crucial in ensuring data quality. Recent studies show that organizations lose an average of $12.9 million annually due to poor data quality, including issues related to data completeness.This post from James Ajayi on Acceldata’s blog explores data completeness from its importance to techniques.
Data Validation: The What, Why, and How
Data validation is a data quality management technique that ensures the data you import or feed into a system meets specific quality and integrity standards. Ideally, you perform a series of documented tests validating data against code lists, data types, or other set thresholds. Data validation enhances data reliability and accuracy by preventing the input of invalid data. You can find out more in Caroline Wanjiru’s post on Acceldata’s blog.
Gitpod vs. Codespaces: How to Choose
Developers are always looking for ways to simplify their software development workflows and increase productivity. Cloud development environment (CDE) platforms offer a better way by providing preconfigured, remote environments accessible from anywhere, removing the need for local setup. This article from Jethro Magaji on DevZero’s blog will compare two popular platforms, Gitpod and GitHub Codespaces, and introduce DevZero as a better alternative.
What Is Multimodal AI? A Complete Introduction
How do you get more context for decision making? By looking at more, and varied, types of information and data. Lately, we have seen artificial intelligence (AI) evolve so, so quickly. Multimodal AI is among the latest developments. Unlike traditional AI, multimodal AI can handle multiple data inputs (modalities), resulting in a more accurate output. In this article on Splunk’s blog that was updated by Arnab Chowdhury, you can learn what multimodal AI is and how it works.