Bridging boundaries between operations and analytics isn’t a new goal for organizations, but the latest whitepaper from Ventana Research explores how to do so with master data management and the role of an Intelligent Data Hub.
Seeing value from analytics and emerging technologies such as AI begins with trust in the data. That trust relies on how data is collected, shared, protected and used. The annual Data and Analytics Global Executive Study with MIT Sloan Management Review looks at how 2,400 global business leaders make decisions based on analytics insights – and what steps are needed to get trustworthy data.
For the past few years, on the trails of GAFA (Google, Apple, Facebook, and Amazon), data is perceived as a crucial asset for enterprises. This asset is enhanced by digital services and new uses that disrupt our daily lives and weaken more traditional businesses.
With all that Azure Machine Learning provides, you’ll no longer be forced to work in inefficient ways or be burdened with mundane tasks. Instead, you’ll have the means to streamline and automate the end-to-end ML life cycle and tie it into existing DevOps processes, so you can collaborate with app developers and work at the same cadence when building ML-infused apps
Every business today depends on data, making smart data management a required core competency inside every organization. Businesses need to manage data assets with the same discipline and rigor as financial assets, and they need tools that do not require deep technical knowledge.
Behind the hype of artificial intelligence lies the foundation of data science and the technical gurus who make sense of massive amounts of data with machine learning algorithms. The Harvard Business Review’s AI Adoption Insight Center focuses on the the human faces behind the computational and automation advancements that are carrying our world into exciting, uncharted territory.
In this second edition, we decided to tackle the organization of this new, agile data governance, and its scaling process using this same mindset. We start by analyzing the different governing bodies that we come across in traditional organizations today.
In today’s data-driven world, organizations like yours are embracing efforts to maximize data investments that make the greatest business impact possible. Boost your return by unifying your data, delivering data insights to everyone, connecting data analysis and profitability, and innovating through the cloud.
MDM solutions have been instrumental in solving core data quality issues in a traditional way, focusing primarily on simple master data entities such as customer or product. Organizations now face new challenges with broader and deeper data requirements to succeed in their digital transformation.
Analytics needs to take a page from application development (DevOps) and embrace ModelOps – a practice that puts in place the culture, process and technology to operationalize analytics faster and more efficiently. ModelOps ensures maximum business impact from analytics, automates repeatable tasks, builds collaboration between stakeholders and streamlines the flow of analytics into decision making processes.
Maximize your business intelligence investments by bringing data together from all your sources with Azure Synapse Analytics. Put your data to work to gain a deeper understanding of your business, and move from reactive reporting to predictive, actionable insights.