In a highly competitive environment, making smarter decisions faster dramatically impacts both the top and bottom lines. According to Forrester, advanced insights-driven businesses (IDBs) — firms that use data, analytics, and software in closed, continuously optimized loops to differentiate and compete — are eight times more likely than beginners to say they grew by 20% or more. That kind of growth is a good argument for making data available to everyone in your enterprise in a timely fashion.
Getting there is easier said than done. The many steps you must take to connect, transform, and process data can create speed bumps that slow you down. We studied this kind of data integration friction in a recent survey of over 650 data decision-makers and practitioners.
The survey found that 68% of data leaders say data integration friction prevents them from delivering data at the speed the business requests, with more than 40% admitting that it’s a chronic problem in their organization. And is it any wonder?
68% of data leaders say data friction is preventing them from delivering data at the speed the business requests it
The different areas of your business each have their requirements and needs, and as those needs arise over time, you can end up with a hodgepodge of systems:
Point solutions brought in to address a specific portion of the process — that aren’t well-suited to handle a wide range of requirements.
Tools from existing cloud providers — that create vendor lock-in.
Products focused on integrating data from SaaS applications — that can’t address the decades of valuable data in legacy systems.
Filling in these gaps then requires significant hand-coding and one-off implementations. But the shortage of data engineers makes it hard to keep up. And as data continues to grow in volume, complexity, and urgency — and data platforms continue to change and proliferate — the resulting skills and data silos will become increasingly difficult to integrate. Organizations end up hiring specialized teams to focus on specific platforms or areas, but that’s expensive, and it reinforces data silos.
And as the volume of data continues to increase, so does the risk of breaking these hand-coded integrations. In fact, the same research referenced above found that more than a third (36%) of data leaders say their pipelines break every week, and 14% say they break at least once a day. All-in-all, data teams are spending 31% of their time troubleshooting problems and recoding pipelines. Companies are using expensive data talent to do what should be simple pipeline-building tasks. This complexity and inefficiency mean your business users aren’t getting the data they need to stay competitive, which can lead to customer churn and reduced revenue.
The good news is, there is a better way.
Meet the Data Needs of Your LOBs — Faster and With Fewer Resources
StreamSets platform eliminates data integration friction so you can keep up with need-it-now business data demands in three ways.
Learn Once To Create Many Different Data Integration Pipelines. A single interface provides functionality to support the entire data integration lifecycle, from developing pipelines to deploying and running them in production. You’ll accelerate—and lower the cost of—data pipeline development and management.
Templatize Data Pipelines for Scale. With our Python SDK, you can easily create and manage hundreds of pipelines and jobs with just a few lines of code. You’ll free up engineering resources to address other important activities and requests.
Simplify All Transformations—Even Advanced or Rare Cases. With over 50 pre-defined processors that can be dragged and dropped into ingestion pipelines and also be easily extended to include custom code, you’ll reduce project backlogs and enable a more productive workforce.
Download the ebook: How Do You Eliminate Data Integration Friction?
And keep your eyes open for our exclusive research report on how chronic data integration friction is putting the brakes on innovation — coming soon!
The post How To Quickly Support Diverse LOBs With Scarce Data Engineering Resources appeared first on StreamSets.
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