A Modern Take on Data Management: The Data Hub

Learn how Data Hubs are changing the way businesses manage, store, and analyze their data 

A Modern Take on Data Management: The Data Hub
Lepakshi Poonamallee


In the era of Big Data, data management is a big business. Enterprises across all industries are investing in their data management. In fact, B2B researchers from MarketsandMarkets predict that the global master data management market size will grow from USD 11.3 billion in 2020 to USD 27.9 billion by 2025, at a compound annual growth rate of 19.8% during the forecasted period. These figures help make it easy to see why businesses need to invest in their data to stay on top of market trends. A modern data management structure that Adaptive Pulse is helping B2B SaaS enterprises invest in is the Data Hub. 


 A Data Hub is a data-centric storage architecture that leverages a combination of technologies including data warehouse, data lakes, data engineering and data science. Data Warehouses and Data Lakes are popular data storage architectures that offer high level data management and differ in the advantages they provide to their users. Both Data Lakes and Data Warehouses allow their users to store and manage data at a high level. The article Data Lakes, Data Warehouses, Data Hubs: What's the Difference, dives deeper into the different uses for both these systems. Many businesses have begun to invest in combining the two architectures to maximize their data management capability which has contributed to the rise in popularity of Data Hubs.


Data Hubs help enterprises consolidate data from various sources and use it to power analytics and AI workloads. For a business, data is produced from various sources: their customers, employees, sales, etc. We can think of these sources as data producers, and the business’ analytics team as the consumers of the data. A Data Hub streamlines data sharing by connecting these producers of data with the enterprise's consumers of data. Data flows into and out of the hub through endpoints while the hub itself is a mediation point that allows businesses to see data flows across their organization in real time. Data hubs allow for large quantities of data, unstructured or structured, to be quickly processed and standardized. This facilitates agile data transformation: a business can apply BI tools, machine learning models and AI to reveal actionable insights from their real time data.


Adaptive Pulse hopes to spearhead this cutting edge transformation process. The Data Hub by Adaptive Pulse is a management platform for curation, cleaning, and governance. It’s intelligent, platform agnostic and flexibly connects to all their clients’ data sources. Aside from AI and Machine Learning models that other data analytic enterprises are also implementing, Adaptive Pulse is now leveraging Natural Language Processing (NLP) to power their data models. 


NLP refers to a special branch of artificial intelligence that aims to computerize the human ability to understand text and spoken words. Adaptive Pulse’s NLP and machine learning models help process human language in the form of text or voice data and apply sentiment analysis. CEO Jennifer Huynh claims that this is one of the most important aspects of their Data Hub services. By using NLP to analyze unstructured data like customer email and chat transcripts, they are able to reveal deeper insights for their client’s customer success teams to better understand their client’s customer base. You can learn more about how Adaptive Pulse has combined their Data Hub with their AI Engine to optimize predictive analytics and retention intelligence here.


Lepakshi Poonamallee

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