Taming the Data Deluge: Why Data Orchestration is Critical

Efficient data pipelines are key in today’s world. Discover the challenges of manual management, the benefits of orchestration, and how to boost decision-making.

An efficient pipeline management process ensures smooth operations and faster response times. Does your organization have more silos than it knows what to do with? Data orchestration can refine your process, streamline workflows, and reduce operating expenses.

Organizations Have Too Much Data to Handle

The digitalization trend has directly contributed to the world’s growing data collection. Experts project the volume generated, captured, copied, and consumed will reach 394 zettabytes by 2028, up from just 33 zettabytes a decade prior. Nowadays, businesses have more metrics than they know what to do with.  

Pipeline management involves configuring, scheduling, monitoring,g and maintaining the flow of information as it moves from Point A to Point B. At scale, this process gets complex. Manually extracting, staging, transforming, and delivering input from webpages, application programming interfaces, databases, and files can take days. Multiplied by every department — or at least each one with a silo — the collective processes can span weeks, even months.

Since manually managing pipelines is time-consuming, information often remains adrift and unused in data lakes. According to a McKinsey & Co. report, enterprises use less than 20% of the data they generate. Their main concern is the costs associated with moving digital assets across environments. 

Why Data Pipeline Management Must Improve

In 2021, Wakefield Research conducted a quantitative study of executives and C-suite leaders at organizations with over $100 million in annual revenue. The respondents were in the United States, the United Kingdom, Germany, and France. The survey’s findings were enlightening.

You Spend Too Much Managing Pipelines

Most of the business and analytics leaders surveyed admitted to overpaying by about half a million dollars annually due to their reliance on outdated pipeline development and management strategies. On average, they paid their data engineers $520,000 yearly to manually create and maintain pipelines. 

Wakefield Research arrived at this estimate based on the median of 12 engineers per team spending approximately 44% of their time on pipeline management while earning an average salary of $98,000. 

The $520,000 estimate does not factor in inefficiencies, so the actual figure is likely much higher. Have you ever rebuilt a pipeline post-deployment? Do your employees need to fix configuration or transformation errors? Time spent on tasks like these quickly adds up, contributing to unnecessarily high operating expenses. 

Scaling Your Workforce Isn’t the Answer

At first glance, you may think the answer to this problem is to expand your data engineering or information technology (IT) team. However, scaling your workforce would require directing more funds toward labor, which would eat into your returns.

Even if you hire more data engineers, your organization’s reliance on manual methods may remain a pain point. About 90% of employees do not feel confident in their ability to understand, work with, and analyze information effectively. You must dedicate time and money to training before they become productive. 

Besides, while increasing productivity and output would technically solve your problem, your dataset size will continue growing as digitalization advances. Data orchestration is one of the few viable long-term solutions. 

Also Read: Mastering Complex BI Systems with Real-Time Data Streaming Solutions

Data Orchestration’s Role in Pipeline Management

How much of your organization’s information is out of reach? The average business has over 2,000 data silos, each containing knowledge that is inaccessible to other departments. While interdepartmental coordination may not be your priority, you should understand that it could enhance your performance and decision-making. 

Data orchestration automates the flow of metrics as they are collected, ingested, processed, and served, automatically moving digital assets from silos to their final destination. It manages system and application dependencies within pipelines, coordinating various flows to ensure metrics arrive at the right place and time.

Your organized, transformed information is in a central repository, making it available for analysis. Instead of bouncing from department to department to get all the metrics you need, you can go to one place to get the big picture — no more fragmented pipelines leading to hard-to-reach silos. 

The Companywide Benefits of Data Orchestration

Your organization greatly benefits from incorporating data orchestration into your enhanced pipeline management strategy.

Enhanced Organization

Eliminating silos improves visibility into the kind of metrics each department collects, enhancing governance and reducing waste. You quickly identify the most valuable assets when everything is organized and transformed. 

Accelerated Decisions

Data orchestration streamlines information-reliant workflows, enabling you and your colleagues to make decisions faster. Crucially, you do not compromise quality — the larger and more accurate your dataset is, the more relevant and specific your insights will be. 

Automated pipeline management will minimize downtime and disruption for your IT and data engineering teams. Inefficiencies like human error and misconfigured dependencies will become less common.

Improved Compliance

Gaining visibility into metric collection and storage can help refine your compliance strategy. For example, you may realize your finance department is not correctly securing customers’ financial details in transit, preventing swift action. 

Also Read: Is Augmented Analytics the Next Frontier in Business Intelligence?

Incorporating Data Orchestration Into Your Company

The larger your datasets, the more urgent automation will become. While the traditional manual approach to pipeline management may remain effective for now, it is too time-consuming and costly to be viable in the long term for most midsize and large enterprises. Consider incorporating data orchestration into your current strategy.