In a new survey, executives say senior-leader involvement and the right organizational structure are critical factors in how successful a company’s analytics efforts are, even more important than its technical capabilities or tools.
Jean-Michel Franco's insight:
Good read. Shows that analytics success requires more that do yourself environment. Leadership, motivated and skilled people and proper organizational structure are key. There are no silver bullet for the organizational structure, though.
Can “self-service” be truly autonomous? Can today’s self-service products be used to perform data unification, preparation and analysis to successfully glean relevant, unambiguous business insights in the absence of IT involvement?
Jean-Michel Franco's insight:
A very good read from Lakshmi Randall on the need for collaboration between IT and Lines of Business to turn the promise of self service data -> Need to evolve from a control to empowerment culture in the IT department ; need to evolve from a do it yourself to a managed self service approach in the Lines of Business
While big data is more prevalent in the enterprise than ever, there is still potential to use that data more efficiently. Learn 5 things you should consider when building big data analytics for the masses.
Next week I’ll be discussing the possibilities and positive developments in the educational webinar, “Democratizing the Data Lake: The State of Big Data Management in the Enterprise.” Set for Tuesday, April 26 at 1pm ET/10am PT, this webinar will delve into data access, data cataloging and metadata management options for Hadoop as well as big data integration and data-prep options. We’ll also discuss Apache Spark and its role in data processing, stream processing and data analysis in the context of Hadoop.
Disaster recovery and development and testing are obvious starting points, but there are many other hybrid-cloud DW use cases as well as pitfalls to avoid. We’ve already witnessed a seismic shift of mainstream corporate workloads into the cloud, but the movement has been slower to take off where data and analytics are concerned, and with…
The BI and analytics platform market has transitioned during the past several years as the balance of power steadily shifted from IT to the business. This multiyear shift of focus from IT-led reporting to business-led self-service analytics passed the tipping point in 2016, which has forced a new perspective on the definition of a BI and analytics platform, and consequently, has significantly re-ordered the vendor landscape as represented in the Magic Quadrant. This webinar explores the key characteristics of the new modern BI and analytics platform and explains the key impacts to this year's Magic Quadrant resulting from this change. Inclusion and evaluation criteria for the Magic Quadrant and an overview of new companion research are discussed, and are critical for buyers in this market to understand and consider in future purchase decisions.
A company’s success is increasingly based on the speed and quality of business users’ decisions. The key is to strike the right balance between empowering business users and maintaining IT control over strong data governance policies. Learn how.
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