Enterprise Data Technology Part 5 — Data Quality With Acumatica

By Robert Kramer, Patrick Moorhead - April 7, 2024

In today’s business environment, it is essential to maintain a high standard of data quality to support effective enterprise data management strategies. The right use of data improves operational efficiency and supports strategic planning and execution—but only if the quality of the data is good enough for you to rely on it. Organizations must prioritize maintaining and improving data quality to take full advantage of the extensive features within today’s enterprise software applications.

Throughout my career in the enterprise resource planning solutions market, I’ve come to understand that ERP data is a particularly crucial asset to most organizations. It serves as the official system of record and the definitive source of truth for a business.

This is the fifth installment of this EDT series; the first provided an overview of internal and external data sources and the second highlighted key components and benefits. The third discussed change management, while the fourth focused on sustainability data. In this installment, I will offer details on the importance of data quality, then summarize key points from my recent discussion with John Case, CEO of the enterprise software company Acumatica, especially his viewpoints on the importance of data quality to the success of his customers.

The Importance Of Data Quality

Identifying and resolving data quality issues is imperative for an organization’s data management strategy, as these issues can keep your organization from getting the most out of the data. These issues include duplicate, inaccurate or missing data, which can lead to confusion and misinformation. Data that’s unused, outdated or inconsistent can further complicate matters. Other significant challenges arise from the difficulty of managing unstructured data such as text and images, variability in data formats across systems, periods when data is unavailable due to maintenance, data overload and a lack of data literacy. Additionally, human errors in data entry or management can contribute to many of the other problems already mentioned. This is why organizations must implement data verification, utilize cleanup tools and conduct regular data quality reviews to mitigate data quality issues.

Data Quality Processes And Tools

Data quality processes need to include integrating and standardizing data across systems for consistent handling and easy exchange, automating data cleansing to correct errors and inconsistencies and establishing clear data governance policies. Other factors to improve data quality include conducting regular assessments to review how data is collected, stored and managed. Data profiling can be used to better understand existing data structures and identify missing values and inconsistencies. Manual checks are necessary to ensure that automated tools are functioning properly. Additionally, it is important to keep documentation up to date to maintain accountability, comply with regulations and provide a reference for the future.

Underlying all of this, implementing a proper data management strategy requires a comprehensive understanding of all data structures within the organization’s technology environment. Achieving a “gold standard” in data management means reaching a point where data is consistently reliable and can be trusted as a basis for critical decision-making. This requires creating a unified, consistent data environment that eliminates discrepancies and ensures that all stakeholders access the same high-quality information.

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Equally important is user training and awareness. It is crucial to educate users about data quality principles and equip them with the skills to identify and report issues. Encouraging the adoption of best practices among users increases engagement and motivates them to uphold high data quality standards in their system interactions. These steps ensure data integrity by defining data ownership (i.e., who is accountable for data quality), data access (which users have access to which data) and data management (the processes used to maintain the organization’s data at high quality).

There are several tools available to improve data quality. For example, Qlik Talendand Data Ladder DataMatch Enterprise help with organizing and correcting data. OpenRefine, formerly Google Refine, cleans up disorganized data. IBM InfoSphere QualityStage and Alteryx are used to standardize data formats, while SAP Data Services and SAS Data Management consolidate and clean data from various sources. Informatica Data Quality and Microsoft Data Quality Servicesoffer comprehensive features for monitoring and improving data quality.

Please also refer to my articles on how Software AG is expanding Super iPaaS to integrate business applications and enterprise data across multiple environments, and on how Informatica is embracing AI to help companies manage their data.

Acumatica And The Importance Of Data Quality

Now let’s look at a company that has focused heavily on data quality for both itself and its customers. Acumatica develops cloud-based ERP software tailored for small and medium-sized businesses; it prioritizes data quality, which has been important to its customers’ success. It started operations in 2008 and is headquartered in Kirkland, Washington. The company focuses on streamlining business operations by providing functionalities to support financial, supply chain, customer and project management. Acumatica’s flexibility allows it to be licensed for on-premises use, cloud deployment or as a software as a service solution across the construction, manufacturing, retail, service and wholesale distribution sectors.

Acumatica stands out because of the importance it assigns to data quality. As Case told me, “The data quality is significant because if all of it is in shape, then suddenly the process of getting the customer to use our modern ERP system versus the legacy systems they are migrating from becomes much simpler.” Data quality in enterprise systems such as Acumatica’s is crucial because these systems act as a business’s central nervous system, storing and managing core data across different departments. This foundational or anchor data is then utilized by numerous other business applications, including customer relationship management, warehouse management systems, business intelligence, human capital management and more.

I’ve already emphasized what a critical asset data is for organizations. The impact of data quality—or the lack thereof—becomes especially pronounced during a system transformation project, for example when new enterprise software is introduced. Such initiatives put the organization’s data under intense scrutiny, where any shortcomings will be noticeable.

That makes changing a company’s ERP system a daunting project, even with modern tools for data migration. When an organization is implementing a critical business system, challenges can occur if a solid plan to integrate the data is not in place. “Acumatica starts ERP implementation within the sales cycle,” Case said. “We ask questions: What’s the structure of your accounts? How up-to-date is your data? How do you capture all these transactions? Do you understand your supplier network?” Acumatica utilizes a highly systematic implementation process, customized for each different industry, to ensure a consistent approach.

This process begins by understanding the users and assessing their readiness, which extends to evaluating the capabilities of internal staff and their ability to effectively execute the system’s required functions. Acumatica then follows a detailed process for data management, moving from data assessment and planning through data migration and cleaning, validation and testing and finally go-live and ongoing data management. Within each phase, Acumatica details specific tasks for governance, standardization, quality control, monitoring, training and maintenance—all of which contribute to its high success rate for ERP implementations.

Acumatica also offers a platform for building systems and processes that prioritize data accuracy. This offering includes detailed features, training, reporting and security, contributing to its success for many customers. One of Acumatica’s customers, Four States Trucking, successfully implemented Acumatica’s core ERP system with functions for accounting, inventory, sales and purchase orders, service management and business intelligence, along with integrated modules for POS, WMS, e-commerce, shipping, electronic data interchange and credit card processing.

“We focus on dedicated responsibilities from our team and the client,” Case said, “as this ensures data quality is addressed with the single-point-of-contacts appointed.” Acumatica was aided by a dedicated project team from Four States Trucking that included an executive project sponsor, project managers and a number of department managers who brought expertise from their specific areas. Their collaborative efforts ensured a shared responsibility and a commitment to the project, centering on reliable data. And while achieving and maintaining data quality is always the main objective, I want to emphasize that training and support are also critical for implementation success—as reflected in Acumatica’s process.

Summary

In a past role, I faced a difficult challenge with an ERP implementation, mainly because the company overlooked the importance of following essential data quality processes. As we neared the go-live phase, substantial problems surfaced, such as discrepancies in inventory, erroneous customer profiles, faulty financial reporting and production order delays—all stemming from data issues. These issues highlighted the importance of understanding and implementing a solid data management practice. Although investing in this requires considerable effort, the long-term advantages are substantial for organizations that dedicate themselves to improving and maintaining their data quality. My own painful experience in that earlier role emphasizes the value of commitment to data management as a fundamental component of business success.

When these kinds of problems are discussed, most of us recognize the importance of effective data management—and, truly, it is fundamental to the success of any organization. Proper data management significantly contributes to efficient and effective business processes by ensuring that data is accessible, accurate and reliable. That’s easily worth the investment it requires.

Robert Kramer
VP & Principal AnalystatMoor Insights & Strategy| + posts

Robert Kramer is vice president and principal analyst covering enterprise data, including data management, databases, data lakes, data observability, data analytics, and data protection. Robert has over 30 years of proven experience with startups, IT companies, global marketing, detailed strategies, business modeling, and planning, working with enterprise companies, GTM assets, management, and execution.

Patrick Moorhead
+ posts

Patrick founded the firm based on his real-world world technology experiences with the understanding of what he wasn’t getting from analysts and consultants. Ten years later, Patrick is ranked #1 among technology industry analysts in terms of “power” (ARInsights)  in “press citations” (Apollo Research). Moorhead is a contributor at Forbes and frequently appears on CNBC. He is a broad-based analyst covering a wide variety of topics including the cloud, enterprise SaaS, collaboration, client computing, and semiconductors. He has 30 years of experience including 15 years of executive experience at high tech companies (NCR, AT&T, Compaq, now HP, and AMD) leading strategy, product management, product marketing, and corporate marketing, including three industry board appointments.