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How to Break Down Data Silos in a Scalable Way Using Data Ingestion

Published Aug 23, 2023
Jonathan Trieu

Jonathan Trieu

Product Marketing Associate

data warehouse worker on computer

SaaS applications play a critical role in the success of modern businesses. Companies utilize various applications within each department for day-to-day activities and leverage Big Data and analytics for strategic initiatives. Their tech stack streamlines business processes and facilitates decision-making.

Our Automation for Growth ebook provides detailed insights into how the best companies view SaaS integration as a strategic advantage for their growth. Companies can rapidly build integrations and reduce maintenance costs by selecting an iPaaS platform (Integration Platform as a Service) as the cornerstone of their integration strategy, such as Titan Brands who saved nearly $250,000 by switching to Celigo.

Deploying an iPaaS platform also opens up an opportunity to automate day-to-day business processes and support Big Data and business intelligence by expanding data movement capabilities. Data movement involves getting data from one system to another. This process can be further divided into data ingestion, where data is imported from source applications into a central location such as a data warehouse, and reverse ETL, which involves loading data back into source applications. Similar to integrating applications and automating business processes, data silos pose a challenge to effective analytics.

This is an opportunity for companies: developing an automation strategy lays the foundation for optimizing data movement, as both tasks are inherently similar. Companies can foster a data-driven culture by taking one more step if companies have come this far on their integration journey.

Creating a Data-driven Culture

Establishing a data-driven culture remains a top priority for many organizations. Data helps uncover hidden patterns, insights, and customer preferences, enabling operators to make more informed decisions that better meet their customers’ needs at all levels of the organization. Data allows marketing teams to create campaigns tailored to relevant audiences or empowers support agents with a single source of truth to see a customer’s order history. This enables them to provide more personalized support and reach favorable resolutions.

Moreover, Big Data assists companies in monitoring inventories to avoid stockouts and reduce lead times, resulting in faster order processing for customers.

Unfortunately, the reality of how this vision translates into practice falls short of companies’ aspirations. Deloitte surveyed over 1,000 executives and found that only 37% of respondents believed their company truly embraced a data-driven approach. The remaining 63% either acknowledged that their business planned to adopt analytics but lacked the proper infrastructure to support it, encountered data silos, or were in the process of expanding analytics capabilities beyond silos.

The problem lies with something other than the tools; some applications generate data, effectively store data, and transform data into actionable insights. The primary challenge is the seamless movement of data between these systems. As a result, employees often dedicate excessive time to making the systems work instead of engaging in analysis and making independent decisions.

Challenges with ETL

The challenges associated with data movement are rooted in the methods many companies currently employ. The predominant approach, ETL (extract, transform, load), serves as the primary means of getting data into a data warehouse. However, ETL presents several issues, as highlighted here. Firstly, it is a more intensive process that demands greater technical expertise from IT teams.

Moreover, ETL necessitates data transformation before loading it into a data warehouse, resulting in delayed access to data. This limitation also means that ETL does not support real-time data analytics or machine learning projects.

Consequently, companies often contend with slow-to-deploy pipelines that lack the flexibility for customization and require significant technical resources to maintain. The predominant focus on maintaining current data pipelines leaves little bandwidth for connecting new applications, leading to more data silos and hindering organizations from gaining a comprehensive view of their business and customers.

From a business perspective, data silos undermine the reliability and timeliness of analytics, rendering them incomplete and often unused by operators, which in turn hampers decision-making at all levels of the organization.

For companies proficient in integrating their applications, solving the issue of data silos should be a familiar task, even if it’s not for data ingestion use cases specifically. The technological investments made in an iPaaS solution lay the foundation for an effective data ingestion strategy. Leading iPaaS platforms reduce maintenance costs by providing built-in monitoring and management features, making error handling easy and intuitive. As Brian Weiss, IT Director at Genomic Health, explains: “I can manage the Celigo integration from almost anywhere. If there’s a failure in an extract, I can just log on to Celigo with my cell phone and kick that integration off. And, once the issue is resolved, I can restart it from my phone. This is a big benefit for us.”

Additionally, running all integrations through a central iPaaS platform simplifies pipeline maintenance compared to overseeing individual point-to-point integrations from each application to the data warehouse. iPaaS platforms enable companies to build faster integrations through a user-friendly approach and offer prebuilt templates for added convenience. Furthermore, iPaaS solutions often encompass Reverse ETL capabilities, allowing data to be pushed back into applications, enabling operators to act on up-to-date data.

Creating a Scalable Data Ingestion Strategy

Achieving frictionless data movement isn’t solely about deploying software; it entails building an entire strategy around it. This strategy includes identifying the appropriate stakeholders to engage with and defining clear responsibilities to maximize impact.

Non-technical users (business technologists) play a crucial role in connecting and maintaining integrations at scale. However, we must precisely define the specific type of non-technical users required.

Learn more about the role of the business technologist in our Guide to Data Warehouse Automation and Scalable Data Ingestion.

Scalable Data Ingestion in Action

Operators gain access to the data required to make customer-centric decisions and drive data-driven actions through a scalable data movement process.

But what does this look like in practice?

Enhanced Marketing Efforts

By consolidating customer data from various sources, marketing campaigns can be refined, leading to higher conversion rates. Marketing teams can create more targeted audience segments using data from their data warehouse while also capturing performance data from previous campaigns to gain deeper insights into customer behavior.

Personalization

Real-time data transfer enables the delivery of personalized product recommendations based on user behavior, search history, and purchase records. This level of personalization is particularly impactful for e-commerce companies and businesses that rely on dynamic online browsing experiences.

Optimized Supply Chain

Utilizing real-time data aids in monitoring inventory levels, forecasting demand, and managing suppliers, resulting in a more streamlined and efficient supply chain.

Customer Success

Comprehensive user behavior tracking equips customer success teams with the visibility needed to proactively address user issues, offer targeted assistance resources, and enhance the overall user experience. Access to behavior data enables companies to predict potential churn and take proactive measures to engage with at-risk customers.

Escalation Management

Smooth data movement between helpdesk software and the data warehouse improves response times and issue resolution. This seamless process empowers support agents to access the necessary data for resolving escalations efficiently.

In today’s rapidly evolving digital landscape, where customer expectations constantly change, businesses are tasked with delivering personalized experiences and making customer-centric decisions. Achieving this necessitates implementing a scalable data movement process to break down data silos throughout the organization.

Accelerate Business Growth with End-to-end Automation

The Celigo Platform offers ecommerce businesses the flexibility to adapt to evolving industry needs. Seamlessly connect product data sources, including PIM, CMS, ecommerce platforms, or ERP systems, to your data warehouse.

Celigo’s process-centric approach to automation provides a starting point for companies and teams to execute phased automation roadmaps. Organizations experience fast and frictionless implementations while building a foundation for a scalable automation framework across the enterprise.