The Vital Role of Data Quality in Automation

At a glance

• Data integrity drives bottom line results: Clean data is the cornerstone of successful automation. It impacts everything from email marketing to financial reconciliations.
• The impact on your business: Organizations must adopt standardized data dictionaries and naming conventions to facilitate seamless system integration and ensure business automations are fueled with clean, high quality data.
• Next steps: Addressing data quality proactively can prevent costly errors and inefficiencies in automated processes.

Schedule a data quality health check with Aprio’s Technology Advisory Services team today.

The full story

Artificial Intelligence (AI), Robotic Process Automation (RPA), Business Process Automation (BPA), Industrial Automation (IA), Network Automation (NA), Financial Automation (FA), and various other avenues offer significant potential for companies to enhance efficiency and reduce costs. However, the success of these initiatives hinges on the quality and cleanliness of data and its seamless exchange between systems.

Despite this, many organizations have yet to address the challenge of master data management, which involves establishing standardized data dictionaries and naming conventions across the organization.

The importance of clean data

Rather than delving into technical intricacies, it’s often beneficial to simplify discussions using terms familiar to all.

Consider the task of automating email distribution to contacts – a common process facilitated by tools like Constant Contact, HubSpot, Dynamics, and Salesforce, among others. With a simple click, the application can retrieve contacts and send out newsletters. However, the value of clean and accurate data becomes evident when considering the potential for bounce-backs due to incorrect email addresses.

The presence of bounce-backs indicates data quality issues, as technology operates based on the information provided without autonomously verifying details. Surprisingly, many organizations overlook the need to clean their lists, opting instead to delete bounce-back emails.

That may suffice for email marketing, but falls short in more complex automation scenarios. This example underscores the significance of data quality.

Clean data unlocks greater automation efficiency

The promise of any business automation system or software is greater efficiency. But any automation is limited or maximized by the quality of data being fed into it.

The automation of any process, whether it be in marketing, finance or operations, relies heavily on the accuracy and consistency of its underlying data. Inaccurate data could lead to a number of costly errors. Those errors diminish the ROI for that automation system. In extreme cases, inaccurate data could potentially lead to significant financial loss or reputational damage.

For example, consider supply chain management. Automated systems are employed to track inventory levels, manage orders, and forecast demand. However, if the data put into the systems is flawed, it could result in overstocking or lack of critical inventory. Shipments could be delayed. Ultimately, customers could be dissatisfied with the company’s fulfillment.

The ripple effect of errors created by dirty data can be far-reaching and could affect not just your bottom line, but the long-term relationships you’ve worked so hard to build with customers, vendors and partners.

Automating reconciliation: a financial perspective

Now, let’s explore a more intricate process in the financial realm: automating the reconciliation of electronic payments.

As electronic payments dominate transactions, automating reconciliation becomes increasingly appealing to financial leaders. However, this process entails integrating data from various systems and providers, especially in ecommerce, where multiple storefronts and payment processors are involved.

The key to successful reconciliation lies in matching sales data with payment data—a seemingly straightforward task complicated by inconsistent data structures and naming conventions.

Mismatched naming conventions and data dictionaries pose significant challenges in the reconciliation process, unlike email automation errors, which are merely inconvenient. In financial processes, errors can have profound implications for closing books and ensuring data accuracy.

Given the inevitability of customizations in data presentation, investing in master data management becomes imperative for scalability and data integrity.

A word about master data management (MDM)

Master data management (MDM) is not just a technical necessity. It’s a strategic asset that enables companies to harness the full potential of their data.

By creating a single source of truth for the business, MDM helps make sure that all stakeholders, from executives to frontline employees, make decisions based on reliable data. Implementing MDM requires a concerted effort to align data policies, governance, and quality control measures across an organization.

It also requires a cultural shift away from careless management of data toward treating data as the valuable asset that it is.

A final word

Your data is worth managing with the same rigor that you would financial or human resources. It’s more than a technical issue; high quality, clean, reliable data is a business imperative that underpins the success of automation initiatives across diverse domains.

Establishing robust master data management practices is essential for organizations aiming to leverage automation effectively and sustainably. As the world moves toward an increasingly automated future, the businesses that prioritize data quality will be the ones that thrive.

Schedule a data quality health check with Aprio’s Technology Advisory Services team today for more information about evaluating and improving the quality of data being fed to your business automations.

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