Data governance: The human resources analogy

Explaining data governance to a business community is difficult. Even more so when you need to convince business folks that they are pivotal to data governance success. Data governance demands not just business attention but business commitment. Policies and processes are not just tick boxes on a corporate charter; they are […]

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Data modeling for data policy management

Operationalizing data governance means putting processes and tools in place for defining, enforcing and reporting on compliance with data quality and validation standards. There is a life cycle associated with a data policy, which is typically motivated by an externally mandated business policy or expectation, such as regulatory compliance. tags: […]

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Struggling with data governance alignment? Look to history.

If your organization is large enough, it probably has multiple data-related initiatives going on at any given time. Perhaps a new data warehouse is planned, an ERP upgrade is imminent or a data quality project is underway. Whatever the initiative, it may raise questions around data governance – closely followed by discussions about the […]

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ESP can determine if big data is eventful

Many recent posts on this blog have discussed various aspects of event stream processing (ESP) where data is continuously analyzed while it’s still in motion, within what are referred to as event streams. This differs from traditional data analytics where data is not analyzed until after it has stopped moving and has […]

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Event stream processing – Tip 1: Don’t be overwhelmed

I believe most people become overwhelmed when considering the data that can be created during event processing. Number one, it is A LOT of data – and number two, the data needs real-time analysis. For the past few years, most of us have been analyzing data after we collected it, […]

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Embedding event stream analytics

In my last two posts, I introduced some opportunities that arise from integrating event stream processing (ESP) within the nodes of a distributed network. We considered one type of deployment that includes the emergent Internet of Things (IoT) model in which there are numerous end nodes that monitor a set of sensors, […]

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Three things that need to get real – real-time, that is

In my previous post, I discussed the similarities, differences and overlap between event stream processing (ESP) and real-time processing (RTP). In this post, I want to highlight three things that need to get real. In other words, three things that should be enhanced with real-time capabilities, whether it’s ESP, RTP or […]

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Pushing event analytics to the edge

In my last post, we examined the growing importance of event stream processing to predictive and prescriptive analytics. In the example we discussed, we looked at how all the event streams from point-of-sale systems from multiple retail locations are absorbed at a centralized point for analysis. Yet the beneficiaries of those […]

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Data management for analysis – Feeding the analytical monster more than once

(Otherwise known as Truncate – Load – Analyze – Repeat!) After you’ve prepared data for analysis and then analyzed it, how do you complete this process again?  And again? And again? Most analytical applications are created to truncate the prior data, load new data for analysis, analyze it and repeat […]

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