Are You Juggling Too Many Big Data Projects?
Past experience has taught most data managers that the number of big data projects on their lists will only continue to grow.Read More
MDM for Business Intelligence Success
For business intelligence to produce reliable results, you need high-quality data.Read More
Seven Myths of Data Analytics Debunked
The rapid growth and success of the data analytics field has a dark side.Read More
The Role of Multi-domain MDM in Digital Transformation
Digital transformation is on the agenda at almost every enterprise. Let’s take a closer look at how different master data domains in your MDM strategy contribute to digital transformation:Read More
Risks CIOs Face When Poor Data Quality Leads to False Assumptions
Bad data is more than an inconvenience.Read More
Data Quality Trends Making Waves in Data Management
With stats flying around every day about how much new data is being generated by Internet of Things (IoT), social media, mobile devices and the cloud, it is no wonder that we feel swept up in this digital wave and feel there is no easy way to swim out.Read More
When Hype Doesn’t Match Reality: Data and Analytics Failures
Gartner researchers estimate that over 80% of big data projects failed in 2017.Read More
You Ask, We Answer - CRM Is Not Enough
CRM solutions are advertised to provide a single source of truth for customer information. So why do I STILL need a data management strategy when implementing or migrating my CRM solution?Read More
What the Data Manager Wishes the Business Understood about Becoming a Data-Driven Business
Every C-level executive talks about the value of a data-driven business. That’s all well and good, but they may not understand that a comprehensive data management plan sets the foundation for this. So, they don’t always actively promote the use of or changes in how the company manages master data.Read More
5 Ways Data Management Addresses Data Quality Issues
Data managers play defensive tackle in the fight against poor-quality master data. They’re trying to fill data gaps and keep bad data from making it to the end zone, so to speak. They have as tough a job in the data management trenches as athletes do on the gridiron.Read More
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