We’ve learned that at the typical large enterprise, data is treated with insufficient discipline. Enterprise data is ineffectively used during most key decision-making processes because there is a lack of visibility and understanding of available data. Efforts to corral that data is usually met with half-hearted or poorly structured efforts that are incapable of corralling the chaos.
More than 70% of employees have access to data they should not be able to see. And 80% of analysts’ time is devoted to manual data analysis because initial data quality is so poor, it can’t be automated. These problems will only grow as businesses increasingly rely on data-driven business models to help them improve their financial results.
Managing enterprise data is a massive topic, and the way to best understand it is through the lens of observability. Data observability provides a structured way of monitoring and managing data at scale across hybrid data lakes and data warehouses. In short, by implementing comprehensive data observability, enterprises can ensure that their data works to help achieve their most critical business objectives.
We need to make a broad distinction between the source of data truth and derivatives of that data. For example, marketing, sales and customer service teams use data from CRM and marketing automation systems to track status about customers and prospects and where they are in the buying journey. Derivations of that information can be created by integrating it with data from other sources.
This enables a marketing team, for instance, to create specific messages and custom campaigns to these same prospects and customers, all while using data from an original source and pairing it with data from other repositories.
At GE, the Finance Data Lake (FDL) team integrated 140 different sources to create a baseline to improve financial operations, including cash flow, accounts receivable/payable, and contracts. FDL provides the financial single source of truth for most GE businesses, and each business can use it for their own operational context.
What does all of this mean with regard to implementing data quality as a process? To answer that, let’s first define three key elements that are important to understanding who is benefiting from data quality and what they’re looking for:
In addition, it’s important to understand the different types of data outages and where outages can occur at various stages of the data lifecycle:
Data scientists need a foundation of early warning systems that test quality and conformance across every stage of the data lifecycle. They have to align these systems with testing schedules, and the results of that testing must identify where applications and data repositories are having issues. Applications which can process faulty data should know when data is no longer consumable, and the producing application group should act on it at once.
Not all data, however, can pass through rules, but quality checks of large production sample sets is mandatory. Data scientists use augmented data quality platforms become important in sifting through vast quantities of data accumulated from numerous sources. A taxonomy of the data tables along with the interpretation of the relationships between data sources is crucial to be offensive in propagating the usage of data collection across the enterprise. All this while retaining the sanctity and perimeter of control for source of truth in the organization.
Data consumers can integrate with the data quality results and outcomes to programmatically run checks and put in circuit breakers. The outcome may be a report to an interested user group or a rerun of the data pipeline, or a rewrite of application logic if something has changed.
Data observability is an emerging field that allows enterprises to gain a semantic understanding of the underlying data and provides taxonomy of the data into producers, consumers and critical data elements. Once the primary sources of truth are identified, the production of that data can have a strong data validation check and advance information of failure is sent to the team that is responsible for that data.
Creating a data quality process requires effective and reliable data observability because it enables data teams to work with large datasets with confidence without being restrictive. Enterprise data teams will need to protect their sources of truths but allow the proliferation of data with strict standards for network effects to benefit the organization. Acceldata can help by automating data quality and reliability at scale, to ensure that data is accurate, complete, and timely throughout the entire data pipeline.
Join us for a demo of the Acceldata platform to learn more.