The hardest part about implementing data products is fostering a product mindset among the people responsible for defining, governing, building, and shipping data products. It’s also important that an organization establish processes to facilitate the work of the product team and review boards. Published at: https://www.eckerson.com/articles/data-products-part-ii-data-products-require-product-thinking
Many organizations abandoned data modeling as they embraced big data and NoSQL. Now they find that data modeling continues to be important, perhaps more important today than ever before. With a fresh look you’ll see that today’s data modeling is different from past practices – much more than physical design for relational data. Published at: https://www.eckerson.com/articles/a-fresh-look-at-data-modeling-part-1-the-what-and-why-of-data-modeling
Data democratization is the buzzword to describe empowering enterprise stakeholders with data. While there have been advances in data management, governance, and analytics, something keeps getting in the way of achieving data democratization. Published at: https://www.eckerson.com/articles/data-democratization-and-the-duties-of-data-citizenship
Our industry’s breathless hype about generative AI tends to overlook the stubborn challenge of data governance. Data catalogs address this challenge by evaluating and controlling the accuracy, explainability, privacy, IP friendliness, and fairness of GenAI inputs. Published at: https://www.eckerson.com/articles/generative-ai-needs-vigilant-data-cataloging-and-governance
The need for an independent semantic layer continues to rise as data science gains traction in the enterprise. Its five primary elements—metrics, caching, metadata management, APIs, and access controls—support AI/ML use cases as part of data science projects. Published at: https://www.eckerson.com/articles/why-and-how-to-enable-data-science-with-an-independent-semantic-layer
Business leaders can address AI bias and use it to have rational discussions about management and human bias. Published at: https://www.eckerson.com/articles/weighing-the-risk-and-reward-of-ai-a-non-technical-guide-for-business-leaders
Most organizations view data as an asset to be actively managed with standards, controls, and discipline. Yet, they are passive and casual about metadata. Data is managed. Metadata happens. As data management becomes more complex, metadata management is becoming an essential discipline. It is time to think about metadata management from an architectural perspective. Published at: https://www.eckerson.com/articles/an-architectural-view-of-metadata-management
Kevin Petrie, the Vice President of Research at Eckerson Group, and Dan O’Brien, research analyst, discussed large language models (LLMs), which are neural networks that analyze text to predict the next word or phrase. These models use training data, often from the internet, to understand word relationships and provide accurate answers to natural language questions.
Generative AI initiatives require new data pipelines that prepare text files for querying by language models. Data engineers, scientists, and other stakeholders collaborate to design and implement these pipelines, which span text sources, tokens, vectors, vector databases, and LMs. Published at: https://www.eckerson.com/articles/the-new-data-pipeline-for-generative-ai-where-and-how-it-works
Dan and Wayne discussed the concept of data and analytics operating models, which refers to how organizations organize their data and analytics resources for alignment and efficiency.
This final blog in our series on the ROI of master data management recommends ways for data teams to iterate their MDM initiatives based on the successes and failures of their first project. Published: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-iii-project-iteration
Responsible AI ethical principles provide a clear, unifying purpose for the technological, business, and social goals of AI initiatives. Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-iii-responsible-ai-ethics
Most definitions of a data product conflate it with a data asset. The only way to turn a data asset into a data product is to publish it in a data store along with metadata about subscription and delivery options, and terms of service that specify a bidirectional contract between data consumer and producer. Published at: https://www.eckerson.com/articles/let-s-be-clear-a-data-asset-is-not-a-data-product
Responsible AI can help data leaders comply with the fast-evolving regulatory environment of data and artificial intelligence. Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-ii-how-responsible-ai-assists-compliance
US frontier history had races, risks, and rewards. Generative AI's future will follow a similar path. Published at: https://www.eckerson.com/articles/enterprise-data-and-the-taming-of-the-generative-ai-frontier
Dan and Jay discussed the concept of Data Fabric, an automated and AI-driven approach to managing modern data environments.
Simba Khadder and Kevin Petrie discuss strategies to overcome technical debt in implementation, the pivotal role of data in the success of ML projects, navigating regulatory compliance in machine learning, and the future of AI governance.
Learn how to attain an optimal return on investment (ROI) with MDM by choosing the appropriate architectural strategy and evaluating progress during the initial project implementation. Published at: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-ii-your-first-project
As organizations strive to meet the ever-growing demand for data, they are adopting data products to streamline delivery and ensure solutions provide value to business stakeholders. Learn about four traps that can disrupt data product development and how to avoid falling into them. Published at: https://www.eckerson.com/articles/four-traps-to-avoid-when-developing-data-products
Generative AI brings a promise to improve lives in a blistering innovation race, but also a threat to people, corporations, and even nations. Data analytics leaders must understand the risks of generative AI, both societal and business-related, to use it positively and avoid the destructive consequences seen with nuclear energy development. Published at: https://www.eckerson.com/articles/the-opportunity-and-risk-of-generative-ai-part-i-a-nuclear-explosion
The unbundling of the data ecosystem is causing organizations to “duct tape” products and frameworks together to build their solutions and data delivery processes. Organizations fail to build and deploy end-to-end, automated, repeatable data-driven systems, ignoring data engineering & dataops principles as well as best practices. Published at: https://www.eckerson.com/articles/dataops-in-data-engineering
This blog recommends guiding principles for successful implementation of language models to assist data engineering. Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-iv-guiding-principles-for-success-with-language-models-and-data-engineering
An emerging approach to generative AI will help data engineering teams achieve much-needed productivity gains while controlling risk. Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-iii-the-emergence-of-small-language-models-for-data-engineering
Dan O'Brien and Kevin Petrie discuss FinOps, which is a cost governance discipline for cloud-based analytics and operational projects.
An annual assessment of the positioning strategies of the leading 21 BI vendors finds a lack of differentiation that makes it difficult for buyers to compare products. In the BI market’s sea of sameness, Qlik is the only vendor that stands out with this clever, memorable position. Published at: https://www.eckerson.com/articles/independent-study-bi-vendor-messaging-shows-lack-of-differentiation
MDM creates business value in three ways: it streamlines infrastructure, streamlines processes, and reduces risk. Published at: https://www.eckerson.com/articles/driving-roi-with-master-data-management-part-1-build-your-business-case
“Universal” semantic layer tools introduced in recent years promise to standardize business metrics across the data stack, and eliminate silos of metrics trapped in semantic layers that are limited to specific data sources or BI platforms. This post offers considerations for adopting a universal semantic layer. Published at: https://www.eckerson.com/articles/the-universal-semantic-layer-more-than-enough
An Analytics Center of Excellence empowers business teams to meet their own data needs by changing the role of IT from developer to facilitator. The reality, however, is that IT needs be both a facilitator and a developer. Published at: https://www.eckerson.com/articles/analytics-center-of-excellence-part-i-how-to-shape-the-organization
Traditional companies must balance new and old technologies as part of an ever-modernizing data stack. This blog explores how companies strike the right balance to navigate economic uncertainty, AI disruption, and the need for tool consolidation. Published at: https://www.eckerson.com/articles/the-modernizing-data-stack-three-ways-to-balance-new-and-old
LLMs are hugely popular with data engineers because they boost productivity. But companies must adapt their data governance programs to control risks related to data quality, privacy, intellectual property, fai-Datarness, and explainability. Published at: https://www.eckerson.com/articles/should-ai-bots-build-your-data-pipelines-part-ii-risks-and-governance-approaches-for-data-engineers-to-use-large-language-models
