Master data management is one of those practices that everyone in business applauds. But anyone who has been exposed to the process realizes that MDM often comes with a price. Too often what began as a seemingly well thought out and adequately funded project begins accumulating unexpected costs and missing important milestones.
In recent years, the Internet and e-commerce have revolutionised the retail industry, in the same way, for example, as the appearance of supermarkets. Beyond ease of purchase and the ability to consult the opinion of other consumers, e-commerce has overwhelmingly changed the way in which information about a customer's journey to purchase is captured.
What is a Container? Cloud and SOA Converge in API Management (Container Architecture Series Part 2)
People talk about the impact of the “digital transformation” and how companies are moving to becoming “data-driven,” but what does it mean in practice? It may help to provide a couple of examples of data-driven companies. Netflix is often cited as a great example of a data-driven company.
With all the hype and interest in Big Data lately, open source ETL tools seem to have taken a back seat. MapReduce, Yarn, Spark, and Storm are gaining significant attention, but it also should be noted that Talend’s ETL business and our thousands of ETL customers are thriving. In fact, the data integration market has a healthy growth rate with Gartner recently reporting that this market is forecasted to grow 10.3% in 2014 to $3.6 billion!
I recently attended a Gartner presentation on the convergence of Application and Data Integration at their Application Architecture, Development and Integration conference. During the talk they stressed that “chasms exist between application- and data-oriented people and tools” and that digital businesses have to break down these barriers in order to succeed. Gartner research shows that more and more companies are recognizing this problem – in fact, 47% of respondents to a recent survey indicated they plan to create integrated teams in the next 2-3 years.
Big data has monopolized media coverage in the past few years. While many articles have covered the benefits of big data to organizations, in terms of customer knowledge, process optimization or improvements in predictive capabilities, few have detailed methods for how these benefits can be realized.
Gartner has just released its annual “Magic Quadrant for Data Quality Tools.”
While everyone’s first priority might be to check out the various recognitions, I would also recommend taking the time to review the market overview section. I found the views shared by analysts Saul Judah and Ted Friedman on the overall data quality market and major trends both interesting and inspiring.
Hence this blog post to share my takeaways.