Moving Away from Spreadsheets: How to Know When It’s Time

whitePaper | November 24, 2022

Without a data-literate team, your company doesn’t have a chance of becoming data-driven. “Speaking data” is one of the biggest skills in the coming years as more technical skills (think SQL or R programming) become embedded in the tools themselves, demonstrating what we’ve always known — that tools will only take you so far. Your real resource is people.

whitePaper | October 13, 2022

Generally, data-driven companies mainly struggle with data quality issues. Massive amounts of data, changing data storage requirements due to changing company regulations, and the various types of data sources and formats do make data management quite difficult for companies. Hence, the need for robust DataOps took to automate and simplify data life-cycle phases is of utmost importance at this point. Here are, some of the most important DataOps took that professionals can use in 2022.

whitePaper | November 25, 2022

In our contemporary environment, data and inherent data strategies constitute vital sources in pursuing and developing customer-centric business models while supporting collaboration between different organizations in creating customer value.

whitePaper | May 1, 2023

This document is intended to provide a high-level overview of the proposed data architecture for PulseNet 2.0. If the details come across as vague, that merely reflects where we are in the development phase. Further questions are welcome and can be directed to pulsenet@cdc.gov with “PulseNet 2.0” in the subject line.

whitePaper | September 1, 2022

Artificial Intelligence (AI) is not a recently discovered field. Since the beginning of the computer science discipline, in the late 1950s, AI has drawn a lot of attention in the international scientific community and since then it has represented a field of study which has triggered diverse and numerous research activities.

whitePaper | October 17, 2022

Today, massive amounts of data are collected, processed, and stored for a range of analytical purposes around the world. Every customer, device, transaction, email, and image leaves a data trail. At present, this data is growing too big, changing too fast, and becoming hyper distributed. The traditional ways of doing integration and analytics are no longer viable or scalable. It is not feasible to create millions of data pipelines and to continue moving large amounts of raw data to a data lake or a centralized data warehouse.