Digital transformation efforts are placing a sharp focus on disparate data sources. As companies aim to speed business value, they’re realizing the need for data agility.
But they’ve got a problem: Most data sits in segmented silos, warehouses, data lakes, databases, and even spreadsheets.
“Databases and data lakes are more like data vaults,” Isaac Sacolick, president of StarCIO and author of Driving Digital, told CIO. “Business people know there’s value in the vault and often make deposits, but they don’t have adequate tools to know when, where, and how to get insights from it.”
At the same time, it is becoming increasingly complex to manage disconnected data sources, especially when budgets are constrained and data teams must do more with less.
Meanwhile, the volume of data is rising every day; in 2020, IDC forecast that 59 zettabytes would be created and consumed by the end of the year.
The analyst firm reported that COVID-19 was contributing to an increase in the consumption of replicated data and “changing the mix of data being created to a richer set of data that includes video communication.”
The first step toward addressing these new challenges: solve the data integration issue.
Data integration, not application integration
Organizations need the ability to integrate all data sources—clouds, applications, servers, data warehouses, etc. Doing so maximizes the value of existing infrastructure investments, streamlines the data warehouse, reduces workloads and IT backlogs, and, crucially, puts valuable data at business users’ fingertips.
Enterprises may try to resolve the data integration issue through application integration and system orchestration. Yet in terms of achieving speed to insight, or integrating data for the purpose of answering business questions, this is not an optimal solution.
Given the world’s increasing demand for flexibility, modularity, and composability, deep application integrations are not ideal for this. It is also time-consuming, can cause business disruption, and may add complexity to data warehouses with constant updating of analytical cubes and new operational processes.
A cloud-based analytics platform, with the right controls and governance in place, puts the right data into the right users’ hands in a single environment. This approach integrates data into automated pipelines, delivering data value while also achieving governance, control, and actionable insights.
Cloud-scale integration
Domo seamlessly integrates data from any application, system, or source across the entire business and beyond, transforming it to be used more effectively across every endpoint. The secret sauce is Domo’s Integration Cloud, which turns data integration into a one-stop shop that provides:
- Integration and automation. Domo prevents dark data by automating the ingestion, cleaning, and blending of data from any source—cloud, data warehouse, legacy systems, user desktops—without the need to build new data warehouse. Data can also be fed into any business intelligence or artificial intelligence/machine learning platform for further analysis.
- Governance and control. IT can give users the data they need for analysis—in the tools they want to use—with granular access rights. Plus, IT retains control with access and data-flow monitoring capabilities built to the best in security standards and compliance certifications.
- Performance. Ensure that data and processes flow at speed with Domo’s massive parallel processing functionality. Also, provide users with self-service capabilities to quickly request dataset access with a few clicks.
With Domo’s Integration Cloud, organizations can dynamically integrate data from thousands of sources and systems. With a single source of truth, users can finally glean value from a vault’s worth of data.
To learn more about how Domo’s Integration Cloud helps address the data integration issue, click here.
NOTE: This post was written by a representative of International Data Group, Inc. (IDG) and originally appeared on CIO.com as part of a Domo-sponsored marketing campaign.
