Alation
JANUARY 27, 2022
People might not understand the data, the data they chose might not be ideal for their application, or there might be better, more current, or more accurate data available. An effective data governance program ensures data consistency and trustworthiness. It can also help prevent data misuse.
Cloudera
JUNE 29, 2023
To improve the way they model and manage risk, institutions must modernize their data management and data governance practices. Implementing a modern data architecture makes it possible for financial institutions to break down legacy data silos, simplifying data management, governance, and integration — and driving down costs.
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IBM Big Data Hub
JUNE 8, 2023
Companies with a modern data architecture and robust BI adoption not only gain immediate competitive advantage, they are positioned to move even further ahead by adopting real-time decisioning practices and predictive analytics, the next steps in digital transformation.
IBM Big Data Hub
JUNE 29, 2023
Integrated planning incorporates supply chain planning, demand planning, and demand forecasts so the company can quickly assess the impact on inventory levels, supply chain logistics, production plans, and customer service capacity. A retail company experiences a sudden surge in online sales due to a viral social media campaign.
Cloudera
AUGUST 3, 2021
In summary, predicting future supply chain demands using last year’s data, just doesn’t work. Accurate demand forecasting can’t rely upon last year’s data based upon dated consumer preferences, lifestyle and demand patterns that just don’t exist today – the world has changed.
FineReport
APRIL 9, 2024
Challenges in Data Management Data Security and Compliance The protection of sensitive patient information and adherence to regulatory standards pose significant challenges in healthcare data management. This proactive stance safeguards against erroneous insights or decisions driven by flawed or incomplete datasets.
CIO Business Intelligence
FEBRUARY 16, 2023
Selling the value of data transformation Iyengar and his team are 18 months into a three- to five-year journey that started by building out the data layer — corralling data sources such as ERP, CRM, and legacy databases into data warehouses for structured data and data lakes for unstructured data.
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