Achieving a ‘single version of the truth’ is critical to any MDM, DW, or data integration initiative. But have you ever tried to get people to agree on a single definition of “customer”? Or to get Sales, Marketing, and IT to agree...

Data Governance Programs and Agile Data Projects are known to conflict when it comes to how the information and data is managed. Senior leadership has come to expect both the formal governance of data and data projects to be delivered quickly and effectively. These two requirements continue to cause problems...

Despite its immaterial nature, data has a tendency to pile up as time goes on, and can quickly be rendered unusable or obsolete without careful maintenance and streamlining of processes for its management...

Google “citizen data scientist” today and you will see about 1M results. That number is data. It may be interesting, but it is meaningless without context. Sometimes it appears that we are drowning in data from systems and sensors but starving for insights...

Data analysis can be divided into descriptive, prescriptive and predictive analytics. Descriptive analytics aims to help uncover valuable insight from the data being analyzed. Prescriptive analytics suggests conclusions or actions...

Nationwide Mutual Insurance Company’s Finance Data Governance journey began with a Data Management focused organization, concentrated on processing changes and production support. Through a multi-year Process Optimization effort, the team addressed pain points in Stewardship, Data Quality...

Business Intelligence (BI) is a valuable way to use information to show the overall health and performance of the organization. At its core is quality, well-structured data that allows for successful reporting and analytics...

Non-Invasive Data Governance is summarized as the practice of formalizing accountability for data and the application of governance to process. Non-Invasive Data Governance describes how data governance is applied to the organization rather than being forced into the environment. A NIDG framework will be introduced in this webinar.

Data architecture is foundational to an information-based operational environment. Without proper structure and efficiency in organization, data assets cannot be utilized to their full potential, which in turn harms bottom-line business value. When designed well and used effectively...

This webinar will provide an overview of the standard architecture components needed to perform analytics and derive data insights from within each of the three common database environments. This will include the sandbox environment for initial data assessment and data science modeling...

This case study reviews TIAA’s approach to developing and establishing best practices within their Finance and Actuarial functions to implement Data Governance Policy and Standards...

The Agile development methodology is here to stay. Data Governance is not going away any time soon. These two discipline share some common ground but often compete when it comes to the “right” thing to do when it comes to managing the data...

The difficulty of implementing a new data strategy often goes underappreciated, particularly the multi-faceted procedural challenges that need to be met while doing so. Deficiencies in organizational readiness and core competence represent clearly visible problems faced by data managers...

No (successful) business is an island. For decades, business schools have taught strategies for improving competitiveness by evaluating strengths, weaknesses, opportunities and threats (SWOT), and considering market forces represented by competitors, consumers, and suppliers...

Data modeling has traditionally focused on relational database systems. But in the age of the internet, technologies such as XML and JSON have evolved to provide structure and definition to “data in motion”...

December is traditionally a time to start to look into next year. Trends are derived, and lessons learned applied. Join Kelle and John while we ask several of our peers and CDOs to look ahead at what might be new, and look back at what has worked and not worked...

Evidence suggests that the track record of MDM (Master Data Management) initiatives is not very good. Traditional MDM is often a costly, time-consuming, IT-driven activity that is disconnected from business goals and stakeholders...

The CDO is a relatively new and evolving role. Many CDO job descriptions detail specific Data Governance responsibilities. Some CDO job descriptions read all-data-governance and all-the-time. It has become obvious...

Leading edge AI applications have always been resource-intensive and known for stretching the limits of conventional (von Neumann architecture) computer performance. Specialized hardware, purpose built to optimize AI applications, is not new...

In order to find value in your organization’s data assets, heroic data stewards are tasked with saving the day- every single day! These heroes adhere to a data governance framework and work to ensure that data is..

This webinar will focus on practical approaches to implementing a governance structure for Big Data...

Mastering your customer data is on the critical path for any business undertaking the transformation to a data centric approach.  Whether it is to enable effective CRM to enhance day to day operations or leverage in depth customer analytics for strategic planning...

Metadata management is critical for organizations looking to understand the context, definition and lineage of key data assets. Data models play a key role in metadata management, as many of the key structural and business definitions are stored within the models themselves...

This month’s Real-World Data Governance webinar with Bob Seiner addresses how to improve data quality through the application of Data Governance practices. Quality starts with a plan...

It has been five years since IBM’s Watson soundly defeated the all time human champions of the television game show Jeopardy! Watson has evolved since then, and other modern AI/cognitive computing technologies and platforms have emerged...

Big Data projects have been about as successful as other IT projects – about 29% successful according to the 2015 Standish Group Chaos Report. Second, Data Scientists are generally assessed to be about 20% productive...

Very often we hear things like “This is a lot more work than we thought.” Or “MDM is too ambitious, we need to start smaller.” These are common reactions to initiating Data Governance and EIM programs...

Data integration is just plain hard and there is no magic bullet. That said, three new data integration techniques do ameliorate the misery, making silo-busting possible, if not trivial. The three approaches – data lakes, virtual databases(aka federated databases), and data hubs...

Join Basho technologies and Databricks, creators of Apache Spark, as we share lessons learned by both organizations in building scalable applications for IoT and time series use cases...

Discover practical ways to build an effective governance program that makes a difference within your organization. Clearly identifying an organization’s business objectives is a good first step, but being able to align that focus within your current state IT ecosystem can be challenging...