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Data governance

Data profiling: Dating your data

It’s no coincidence that “data” and “dating” share the same roots. Your relationships with both databases and another human can be looked at in a similar way! Getting to know either in the beginning stages of a relationship can be daunting, and you may feel like you don't know where to begin. However, just as it is with dating, when you know the right questions to ask, and the right ways to better understand your data, the easier it is to move forward, and all of the unknowns turn into a positive and predictable relationship.

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The Data Quality Improvement Assessment

Data is arguably the most important asset to your organization. In an ideal world, data connects the technical and non-technical users in the business through common definitions and processes that can then be used to make critical business decisions. Unfortunately, very few companies are able to leverage their data in this way today.

Organizations are beginning to recognize the need for a centralized data management strategy, but oftentimes they aren’t sure how their data management initiatives fare today and how they could get started on the path to an optimized data strategy for the future.

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CIOs face increasing responsibility for data management

Data is one of the most strategic assets businesses hold today. When used appropriately, it can allow for a better customer experience, improved business decisions, operational efficiency and much more. Organizations that are struggling to use data for analytics are falling behind in more than just technology, they are failing to utilize everything at their disposal to run an effective company.

While data continues to grow in importance for businesses, the methods for managing information are just now starting to catch-up. In years past, information was managed primarily by individual departments with vastly different technologies and processes. While that is still the case today in a wide number of businesses, more are starting to realize that data across the organization needs to be consistent and accurate. That means having a big responsibility for data at a higher level, centralized governance and regulation around data, and processes to keep information entry and management consistent.

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Experian Pandora: Bringing a step change in Data Governance oversight

In my blog, “The role of technology in empowering the CIO and CDO office”, I explain the role the Chief Information Officer can play in helping the Chief Data Officer function through technology supporting data preparation, monitoring and data governance. Technology is not the answer to all of these, however it can make data practitioners lives much easier, providing the necessary productivity boost.

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BCBS 239 is abuzz in NYC

David Bresnick Data governance

At NYC’s Data Governance Financial Services Conference this week, presenters and attendees alike were buzzing about BCBS 239. As they should! While the internationally recognized Basel Committee on Banking Supervision (BCBS) published their guidelines on Principles for effective risk data aggregation and risk reporting back in January 2013, the three year head start to comply is looming! These principles really are a positive force in the industry, aimed at mitigating systemic risk across large banks, a factor contributing to the 2008 financial collapse. Both Global and Domestic Systemically Important Banks (G-SIBs & D-SIBs, respectively) are in the crosshairs and expected to follow suit by the time the ball drops in Times Square (Jan 2016).

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Trends from AAMVA's Annual International Conference

We hit the road and headed to The Hawkeye State this week for the American Association of Motor Vehicle Administrators (AAMVA) annual international conference. AAMVA showcases the latest trends in the motor vehicle and law enforcement community and provides a forum for chief administrators to learn and grow from fellow jurisdiction executives. This year the event was held in Des Moines, Iowa and there was no shortage of mid-western flare! Attendees were treated to a “night at the state fair” in the expo hall, including dunk tanks, games and a corn-eating contest. 

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The chief data officer and business of data management

Today we presented a webinar to introduce our new research study, The Chief Data Officer: Bridging the gap between data and decision-making.

More than 250 CIOs and CDOs in the U.S. took part in our research study. All participants were from companies with over 500 employees from a variety of industries, including manufacturing, professional services, financial services, retail, utilities, healthcare and more. 

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Top 10 stats from our Chief Data Officer research study

Organizations across the globe are beginning to understand that they should start seeing their data as a competitive advantage as opposed to as a challenge or an obstacle. With the centralization of data management and the rise of the Chief Data Officer position, companies are able to truly leverage their most valuable asset: their data.

Experian Data Quality recently conducted a new research study of more than 250 CIOs from companies with over 500 employees in a variety of industries, including manufacturing, professional services, financial services, retail, utilities, healthcare and more. Survey participants were asked about the emerging role of the Chief Data Officer (CDO), the state of their data quality, and the data management practices at their organization. Here are 10 key takeaways from the research.

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Insights from Big Data

Becoming a data-driven company. It is something many of us want to achieve today because it elicits thoughts of ultimate insight. One rarely thinks of data as being sexy, but the concept of being able to use data to accurately predict your company’s next move is pretty cool.

The race is on. Companies are working as quickly as possible to leverage their data assets to drive action and derive value. Terms like big data are common and the c-suite has a desire to pinpoint insight in these mass stores of data.

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Getting to a better data quality framework

To make best use of your data asset across your organization, it is essential to have a solid data quality framework to ensure that your data is accurate and complete. But all data quality management models are not created equal: different organizations have different levels of data quality maturity, depending on organizational priorities and needs.

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