Jim Harris is a recognized data quality thought leader with 25 years of enterprise data management industry experience. Jim is an independent consultant, speaker, and freelance writer. Jim is the Blogger-in-Chief at Obsessive-Compulsive Data Quality, an independent blog offering a vendor-neutral perspective on data quality and its related disciplines, including data governance, master data management, and business intelligence.
Like hiking without GPS directions, Jim Harris says businesses that don't use streaming data analytics will be lost in a crowd of competitors.
Jim Harris says the GDPR may cause seismic changes in how people think of privacy and personal data.
Don't be a data hoarder. Jim Harris shares guidelines for a data retention strategy.
Jim Harris asks: Do you retain and maintain data, or do you have a data retention strategy?
Jim Harris says streaming data analytics can drive better decisions and faster adaptation to changing conditions.
Jim Harris considers the technical infrastructure challenges of data preparation for streaming data.
Jim Harris says a data-driven business can make decisions faster, using better data, with more transparency about results.
Jim Harris shows how data-driven businesses incorporate three aspects of data governance to guide their decisions.
Jim Harris says more reusable data quality processes mean less reliance on IT and higher productivity across the board.
Get faster value out of your data by empowering business users to work with data on their own.
Get on with your day faster by taking a self-service approach to data preparation.
Jim Harris considers whether we can save private data in the age of big data.
In part 2, Jim Harris explains more about why you should address data quality and governance issues on the way to data lakes and Hadoop.
Jim Harris advocates addressing data quality and governance issues on the way to data lakes and Hadoop.
Jim Harris explains why data quality is such a fundamental aspect of master data management.
Do you know how master data management and data warehouses are different? Jim Harris explains.
Jim Harris discusses how the lines between data management and analytics are fading.
Via streaming data, Jim Harris says machines can learn some amazing things without being programmed with domain knowledge.
Like getting into good shape, Jim Harris says we must carefully measure adherence to regulatory compliance – using both internal and external measures.
Platform and strategy are core to compliance, but Jim Harris says commitment from people across the organization is just as important and harder to achieve.
Corporate compliance with an increasing number of industry regulations intended to protect personally identifiable information (PII) has made data privacy a frequent and public discussion. An inherent challenge to data privacy is, as Tamara Dull explained, “data, in and of itself, has no country, respects no law, and travels freely across borders. In the
The term compliance is most often associated with control. It evokes visions of restrictions, regulations and security protecting something which is to remain private. The term open is most often associated with access, and it evokes visions of an absence of restrictions, regulations and security – making something available which is
Streaming technologies have been around for years, but as Felix Liao recently blogged, the numbers and types of use cases that can take advantage of these technologies have now increased exponentially. I've blogged about why streaming is the most effective way to handle the volume, variety and velocity of big data. That's
Historically, before data was managed it was moved to a central location. For a long time that central location was the staging area for an enterprise data warehouse (EDW). While EDWs and their staging areas are still in use – especially for structured, transactional and internally generated data – big
Our world is now so awash in data that many organizations have an embarrassment of riches when it comes to available data to support operational, tactical and strategic activities of the enterprise. Such a data-rich environment is highly susceptible to poor-quality data. This is especially true when swimming in data lakes –
Most enterprises employ multiple analytical models in their business intelligence applications and decision-making processes. These analytical models include descriptive analytics that help the organization understand what has happened and what is happening now, predictive analytics that determine the probability of what will happen next, and prescriptive analytics that focus on
Data governance plays an integral role in many enterprise information initiatives, such as data quality, master data management and analytics. It requires coordinating a complex combination of factors, including executive sponsorship, funding, decision rights, arbitration of conflicting priorities, policy definition, policy implementation, data stewardship and change management. With so much overhead involved in
Data governance has been the topic of many of the recent posts here on the Data Roundtable. And rightfully so, since data governance plays such an integral role in the success of many enterprise information initiatives – such as data quality, master data management and analytics. These posts can help you prepare for discussing
As I've previously written, data analytics historically analyzed data after it stopped moving and was stored, often in a data warehouse. But in the era of big data, data needs to be continuously analyzed while it’s still in motion – that is, while it’s streaming. This allows for capturing the real-time value of data
Lately I've been binge-watching a lot of police procedural television shows. The standard format for almost every episode is the same. It starts with the commission or discovery of a crime, followed by forensic investigation of the crime scene, analysis of the collected evidence, and interviews or interrogations with potential suspects. It ends
