Home > Resources > Whitepapers > Considerations for Building a Real-time Data Warehouse

Considerations for Building a Real-time Data Warehouse

March 5, 2019

In today’s fiercely competitive marketplace, companies have an insatiable need for information. Key to maintaining a competitive advantage is understanding what your customers want, what they need and the manner in which they want to receive your products or services. It is becoming increasingly clear that companies poised to experience the greatest success will be those firms that can effectively leverage their data to meet organizational needs, build solid relationships with stakeholders and above all, meet the demands of today’s customers (Schroeck, 2000). The global economy of today demands that organizations adhere to the constantly changing needs of the customer. Additionally, the speed and dynamic nature of business often negates the time required for long-term planning and time-consuming implementations in order to stay ahead. Because of this, organizations must implement solutions that can be deployed quickly and in a cost-effective manner (Zicker, 1998). So, how does an organization meet these ever-changing, complex requirements? An effective real-time business intelligence infrastructure that leverages the power of a data warehouse can deliver value by helping companies enhance their customer experiences. Furthermore, a real-time data warehouse eliminates the data availability gap and enables organizations to concentrate on processing their valuable customer data. By designing a data warehouse with the end user in mind, you multiply your chances of better understanding what your customer needs and what you need to help that customer achieve his or her goals (Haisten, 2000).

Spotlight

Snorkel Ai

Snorkel AI is a technology startup that empowers data scientists and developers to turn data into accurate and adaptable AI applications fast with Snorkel Flow, a first-of-its-kind data-centric development platform, powered by programmatic labeling. Snorkel Flow reduces the time, cost, and friction of labeling training data so data science and development teams can more easily build and scale AI models to deploy more meaningful applications. Incorporating human judgment into the AI process through subject-matter experts is made more efficient and scalable, leading to more ethical, responsible outcomes. Five out of the top ten US banks, several government agencies and Fortune 500 companies use Snorkel Flow. Snorkel’s core research was developed at Stanford AI lab and is deployed at Google, Intel, Apple, IBM, DARPA, and other trailblazing organizations.

OTHER WHITEPAPERS

Accountability and Traceability White Paper & Research Roadmap

whitePaper | April 18, 2023

The MIT Future of Data Initiative is leading a multi-disciplinary research agenda to design and stimulate the deployment of consumer-empowering and accountable systems to provide trusted, traceable uses of personal data on an ecosystem-wide scale. The Initiative has gathered together computer science and Internet policy researchers as well as leading commercial enterprises in financial services, payment technology, cloud platforms, insurance and other sectors to discuss current challenges and opportunities in privacy and data governance. Today’s modern privacy laws place appropriately high expectations on organizations processing personal data. At the same time, consumers report declining trust in those who handle their personal data and regulators around the world struggle with the scale of the enforcement challenge. We aim to identify and put into service technical infrastructure for enterprises seeking to handle personal data in a trustworthy and lawful manner with guardrails to enable the traceable, accountable, and scalable use of data.

Tackling climate change with data science and AI

whitePaper | April 2, 2023

In this white paper, we share how The Alan Turing Institute’s AI for science and government (ASG) programme has been using collaborative and multidisciplinary data science and AI to help tackle climate change.

Enhancing data mesh: How distributed ledger solutions empower decentralized governance

whitePaper | June 27, 2023

Data mesh is an innovative approach to data management that offers a solution to the most common challenges organizations face when dealing with large-scale data management. Data mesh concept consists of four key principles for large-scale data management in a multi-tiered, multi-domain organizational structure: domainoriented ownership, data as product, federated computational governance and a self-service data platform. These all promote a more agile, efficient and scalable data architecture. However, one of the central challenges to implementing data mesh is designing decentralized governance mechanisms that align with its core principles.

SAP Datashpere

whitePaper | March 29, 2023

On the 8th of March 2023 SAP announced the evolution of SAP Data Warehouse Cloud (DWC) into SAP Datasphere during its ‘Data Unleashed’-digital event. Although this whitepaper has been updated to reflect all the changes and new developments, some of the images may still carry SAP Datasphere’s old name (Data Warehouse Cloud), in cases where no similar visualizations with the new namesake were available. If you have read this whitepaper before, you might just be interested in the topics that SAP highlighted in the days preceding and during the March event. In the section below you will find the most important takeaways in a nutshell, but we encourage you to read on if you want to know more!

7 Trends for Data Science in 2021

whitePaper | May 5, 2021

Over the last decade, interest in the field of data science has not only increased enormously, but has also changed and developed considerably. In view of the new technological advances and the constant growth of data, we expect Data Science to continue to develop strongly in 2021. We have identified 7 trends that will be relevant for the coming year.

Vida for Retail: Rebooting Retail withthe Power of Data Analytics

whitePaper | January 11, 2023

There used to be a time when purchase decisions were based on recommendations of the neighborhood grocery manager. Today, these purchase decisions are driven by technology, bringing together the collective wisdom of consumers of the same product from across the world.

Considerations for Building a Real-time Data Warehouse