The age-old conversations around data integration and data governance are still as relevant to today’s data world as it’s ever been. Though its applications—enabling AI and improving self-service analytics—may differ in form, the sentiment remains the same: Reliable, actionable data is crucial toward enabling better decision making and maximizing operational efficiency.
Yet, with data increasingly spread across different locations, file systems, databases, and applications—let alone across on-prem, cloud, and multicloud environments—the continuing challenges of data quality, governance, and integration remain pertinent.
To shed light on modernizing business with data integration and governance at its core, experts joined DBTA’s webinar, Data Integration and Governance for the Hybrid and Multicloud World, detailing the latest technologies and best practices that can turn the tide toward true proprietary efficiency.
Bharath Vasudevan, vice president of product management and marketing at erwin by Quest, explained that the push toward AI has put a spotlight on today’s pervasive data challenges.
“If AI is built on data, how do you trust your data?” posed Vasudevan. “For far too long, organizations think they have the best data—they go to make decisions and strategies built on it, and it turns out there’s flaws in the data.”
Ensuring that all data is being used, is of good quality, and is accessible to the broader organization is the ambition of many enterprises and their CDOs. To help achieve this standard, Vasudevan introduced erwin by Quest’s seven steps to maximize data value, which are listed as the following:
- Model: Design the data architecture.
- Catalog: Search and find data easily.
- Curate: Enrich data with business context.
- Govern: Apply business rules and policies.
- Observe: Raise data visibility and integrate data quality.
- Score: Automate data value scoring.
- Shop: Make trusted, governed data widely accessible.
For many enterprises, data analysts are responsible for using internal datasets to produce data that is only consumable by executives, built on perceived “good” data. Vasudevan argued that, by following erwin by Quest’s guide, this process can be transformed to unify internal, external, and synthetic datasets, paired with best-in-class lineage, data value scoring, and an easy-to-use marketplace, to democratize trusted data throughout the business—and establish a robust data foundation for AI.
Jerod Johnson, senior technology evangelist at CData Software, explained the variety of use cases that CData addresses, including:
- Consolidating operational data into a central repository to provide faster, more direct access to data for operational users
- Moving data from on-prem to cloud by modernizing underlying infrastructures for efficiency and effectiveness
- Deploying new data fabric, mesh, or governance strategies to create a user-centric approach to data management
- Scaling IT infrastructure for a growing business by eliminating manual data handling to deliver data and insights faster
- Connecting data between specific systems to ensure data flow
Johnson then detailed various customer success stories where CData was used to modernize data infrastructures. One example, Manhattan Associates, used CData Sync to consolidate data into Snowflake with cost efficiency in mind.
According to Jack Smith, principal solutions engineer at Syncari, “master data or governance strategies fail up to 75% of the time because there’s been no tangible outcome or business strategy attached.” This is the obstacle that Syncari seeks to resolve for enterprises, helping them “prepare their datasets [and] prepare their models for AI infrastructure, for better analytics, and cross-domain automations,” explained Smith.
Data integration in hybrid and multicloud environments is particularly challenging, as the amalgamation of different tools built out to address specific problems lack the interoperability necessary to support a robust data infrastructure. Meeting your data where it is—sprawled across both legacy systems and the latest technologies—Syncari aims to solve data connectivity problems for autonomous issues related to that data structure, according to Smith.
Facilitating a seamless data fabric architecture is key to Syncari’s value, offering the following benefits:
- Data control plan and hub
- Patented, multi-directional data sync
- 360-degree views of any entity
- Democratized data
- Support for analytics and automation
- Low- and no-code framework
For the full, in-depth webinar featuring customer examples, a Q&A, and more, you can view an archived version of the webinar here.