Guide to data warehouse automation and scalable data ingestion

Establishing a data-driven culture is a top priority for most organizations. Business users, from operations teams to marketing, sales, finance, and customer service teams, need to access their data in real-time to make better, faster business decisions. 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.

However, a recent survey of over 1000 executives found that only 37% of respondents believed their company was data-driven. 63% of the respondents either felt that their business planned to have analytics but didn’t have the proper infrastructure to support it, they felt that their data was siloed, or were currently trying to expand analytics capabilities beyond silos.

Data Ingestion Key Terms

Data Ingestion is the process of importing large, assorted data files from multiple sources into a single source — often a data warehouse, to be accessed and analyzed.

Data warehousing refers to collecting, storing, and managing large volumes of data from various sources in a centralized, structured, and easily accessible manner. By integrating data from multiple sources, your organization gets more comprehensive analysis and unique insights that would otherwise not be possible by looking at one data source alone.

Reverse Extract, Transform, Load (ETL) is the process of copying data from a warehouse into business applications like CRM, analytics, and marketing automation software.

Data Movement, Data Pipelines, and ETL

Data warehouse integration can be time-consuming and labor-intensive. Data pipeline integration tasks are technical projects. Getting data from databases, ecommerce marketplaces, ERP, CRM, and other data sources out of the data warehouse for analytics is challenging. ETL requires data to be transformed before being loaded into a data warehouse, delaying access to data. ETL does not support real-time data analytics or machine learning projects. This results in slow-to-deploy pipelines that often lack customization flexibility and require many scarce technical resources to maintain. Data ingestion and integration are significant problems for companies with limited (or businesses without) specified IT resources.

Other data ingestion challenges include:

• There are insufficient technical resources to keep up with ad-hoc data integration requests from finance, sales, operations, and marketing teams. IT admins own the maintenance and support of every data warehouse integration. Every time there is a change in a data source, data pipelines can break; IT is in a constant cycle of fixing broken data pipelines.
• Long integration project timelines become bottlenecks, holding business teams back on critical decisions. Critical business data is often outdated before it gets to the user.
• The data warehouse itself becomes a data swamp with siloed, outdated, inaccessible, and unusable data.

With most of the bandwidth dedicated to maintaining current data pipelines, connecting new applications takes a backseat, leading to more data silos and preventing organizations from getting a complete view of their business and customers.

Data silos cause analytics to be unreliable, untimely, or go unused by operators due to incomplete data. This hurts decision-making at all levels of the organization.

Creating a Data Ingestion Strategy

Empowering the Business Technologist

Achieving frictionless data movement isn’t just about deploying software but building an entire strategy around it. That includes identifying the right stakeholders to engage and creating clear responsibilities to maximize impact.

Business technologists (also known as citizen integrators) are line-of-business users who are experts in their department’s processes and data. Business technologists should be enabled with low-code integration tools to manage data warehouse pipelines. According to Gartner’s research, 41% of employees can be described as business technologists – it’s a strategic role that equips line-of-business users to build digital capacities and accelerate digital transformation.

Because of their familiarity with their tech stack, understanding of analytics, and working directly within their department, business technologists can help address this data integration problem. They know what data silos exist and what integrations are necessary to break down data silos.

To create a scalable data ingestion strategy, identify and engage business technologists within the organization. Once you have identified the business technologists within your organization, clearly outline the objectives and expectations, and emphasize how their unique skill set can contribute to breaking down data silos, improving data-driven decision-making, and fostering innovation.

Benefits of Data Warehouse Automation

For IT

The self-service integration allows technical personnel more time to focus on complex queries and requests, such as curating data sets and developing operational improvement strategies. IT teams can eliminate costs related to building, maintaining, and supporting ad-hoc data warehouse integrations.

For Finance

Finance teams have real-time visibility to accurately represent the economic volatility or activity with invoicing, revenues, and subscription metrics to predict future cash positions and avoid cash shortages. They can eliminate manual data dumps and management of multiple spreadsheets that result in static, outdated reports. Finance teams can create real-time reports from trusted data that improve the speed, accuracy, and value of financial reporting, forecasting, and analysis.

For Operations

Access to real-time data improves operational efficiency. Operations teams can measure key performance metrics around order-to-fulfillment rates, stock-outs, order cancellations, and cart abandonment to identify areas for operational improvement.