These days, companies are generating, gathering and managing massive amounts of data. And while there is considerable business value held within those increasingly large and complex corporate data stores, most organizations struggle to capitalize on that value.
As companies adopt more Software-as-a-Service (SaaS) platforms, IT and data science professionals face several levels of data integration challenges, as both data volume and granularity continue to increase. Ineffective data integration is a significant barrier preventing enterprises from realizing the full value of the massive amounts of data they’re gathering and storing.
Respondents to a recent survey of data professionals, conducted by research firm Foundry, reported spending an average of 41% of their time on data analytics projects dedicated to data integration and preparation. Although companies are investing significant resources in data integration, more than half of the survey respondents are struggling to leverage their data in order to:
- Develop new products and services to gain a competitive edge (57%)
- Adapt products and services to meet market needs (55%)
- Deliver data-driven decisions for businesses (52%)
As Foundry notes in an accompanying report, "Building a modern data stack of the future," modern organizations simply can’t scale their data management processes to match the growth and complexity of their data stores while using legacy tools and outdated methods. They lack the resources, time and budget to allocate sufficiently skilled teams of engineers and data scientists to develop effective data pipelines. And once in place, those pipelines require constant maintenance and attention to continue to function efficiently.
Modernizing data analytics environments with a managed data pipeline can effectively address these issues. Modernization can help reduce operational risk, ensure high performance and simplify ongoing data integration management.
Challenges of modernizing data infrastructure
Modernization is not a simple process, however. To do so successfully, organizations must address three critical areas, each of which poses its own set of challenges.
Expertise among teams
The first is ensuring sufficient expertise among the relevant teams. Unlocking and consolidating data from disparate data silos is difficult. Organizations use myriad SaaS applications and database technologies to store their data, and consolidating all that data is a huge engineering challenge. Finding the required expertise to meet that challenge is very difficult — especially for organizations that don’t focus on software or technology.
Time to market
Next is expediting time to market. Teams need quick wins to justify investing in projects to consolidate data and “drive digital transformation” — especially in a tightening or even recessionary market. Teams working on data projects must show quick wins in terms of increased revenues or profitability, cost savings, or new data-driven offerings.
Security and data governance
And finally, data governance and security are critical issues. Businesses need to know who has access to which data sets, and whether their data is adequately protected from external bad actors and users who should not have access.
They must also ensure that they are adopting a truly modern data integration and management strategy to achieve the optimal level of business value from their increasingly large and complex corporate data stores.
The value of managed data pipelines
Fully managed data integration can resolve these issues. With managed data pipelines, the provider has already done the hard work of determining how to best use the data. Fivetran data pipelines deploy in minutes and automatically adapt to schema and API changes. We offer a thoughtful entity-relationship diagram (ERD) for every SaaS source, prebuilt data models for many popular data sources, and industry-leading change data capture (CDC) replication to move large volumes of data with low impact and real-time delivery. Our Business Critical plan offers the highest level of protection and compliance for enterprise data.
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