Download slides here>> Taking the right architectural approach to data governance can build a strong foundation for trusted data throughout the enterprise. An agile Data modeling management is proactive standardize increment schema designs, preventing data governance costly. Integrate data modeling and development, landing business glossary to data model, review data model in each iteration and […]
Sisense Demo: The Dashboard Paradox – Breaking the Adoption Barrier with Infused Insights
Download slides here>> It’s time to think differently about data and analytics strategies. While many organizations have progressed from relying on static reports generated by IT teams to using some version of a self-service model, most still struggle to reap the world-changing benefits long-promised by experts. Unfortunately, each evolution has delivered only incremental improvements over […]
Innovative Systems Demo: Achieving High-Quality Enterprise Data in a Fraction of the Time and Effort
Download slides here>> An organization’s data typically grows at a rate of 40% to 60% per year, with myriad data-oriented initiatives to go with it. Proper data quality is the bedrock of any successful data-oriented business initiative, from data governance and analytics to digital transformations and migrations. It can be a daunting challenge to understand […]
Ataccama Demo: Preparing for Enterprise Data Fabric – Metadata Driven Approach to Data Quality
Download slides here>> With the emergence of Enterprise Data Fabrics and Data Meshes, Data quality is becoming more important than ever before. This opens new unique challenges and opportunities. Ataccama has perfected massively scalable Data Quality implementations for the last decade and developed metadata-driven methodologies to address the challenges emerging from ever-changing and growing data […]
Data Architecture Trends in 2022
The primary purpose of implementing a Data Architecture is to standardize the methods and protocols, as well as the systems for acquiring, storing, managing, and sharing data across the enterprise for improved decision-making. In modern businesses, most decisions are made in real time, and to facilitate an efficient and real-time friendly Data Management infrastructure, data […]
Forthcoming AI Regulation Makes Data Management Imperative
Although algorithmic decision-making has become increasingly vital for many businesses, there are growing concerns related to transparency and fairness. To put it mildly, the concern is warranted. Not only has there been documentation of racial bias in facial recognition systems, but algorithmic decision-making has also played a role in denying minorities home loans, prioritizing men during hiring, […]
A Brief History of Data Management
Data Management is the organization of data, the steps used to achieve efficiency, and gather business intelligence from that data. Data Management, as a concept, began in the 1960s, with ADAPSO (the Association of Data Processing Service Organizations) forwarding Data Management advice, with an emphasis on professional training and quality assurance metrics. Data management has […]
Data Quality Dimensions
Data Quality dimensions are useful concepts for improving the quality of data assets. Although Data Quality dimensions have been promoted for many years, descriptions of how to actually use them have often been somewhat vague. Data that is considered to be of high quality is consistent and unambiguous. Poor Data Quality results in inconsistent and […]
Overcoming the Data Decision Gap in 2022
As organizations enter a new year, leaders across industries are increasingly collecting more data to drive innovative growth strategies. Yet to move forward effectively, these organizations need greater context around their data to make accurate and streamlined decisions. A recent Data in Context research study found that more than 95% of organizations suffer from a data decision […]
Better Data Modeling with Lean Methodology
The process used today in systems development started with principles developed for assembly lines in the 1950s, when manufacturers wanted a more disciplined approach to producing goods and services.Products would come off an assembly line, they’d be inspected, defects would be found, and would be sent back to rework or start from scratch. This process […]