Enterprise Data Technology Part 5 — Data Quality with Acumatica

In today’s business environment, maintaining high standards of data quality is essential to support an effective enterprise data management strategy. Proper use of data improves operational efficiency and supports strategic planning and execution. However, this is only possible if the quality of the data is sufficiently reliable. Organizations must prioritize maintaining and improving data quality to take full advantage of the wide range of capabilities within today’s enterprise software applications.

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

This is the fifth article in this EDT series. The first provided an overview of internal and external data sources, and the second highlighted key components and benefits. The third part covered change management, and the fourth focused on sustainability data. In this episode, we explore the importance of data quality and highlight key points from a recent discussion with John Case, his CEO of his enterprise software company Acumatica, specifically regarding the importance of data quality in customer success. I will summarize my views. .

The importance of data quality

Identifying and resolving data quality issues is essential to an organization’s data management strategy. These issues can prevent organizations from getting the most out of their data. These issues include duplicate, inaccurate, or missing data, which can cause confusion and misinformation. Unused, outdated, or inconsistent data can further complicate the problem. Other significant challenges include difficulty managing unstructured data such as text and images, variation in data formats across systems, periods of data unavailability for maintenance, data overload, and lack of data literacy. arises from etc. Additionally, human error in data entry or management can cause many of the other problems already mentioned. For this reason, organizations should implement data validation, utilize cleanup tools, and conduct regular data quality reviews to mitigate data quality issues.

Data quality processes and tools

Data quality processes should include integrating and standardizing data across systems for consistent processing and easy exchange, automating data cleansing to correct errors and discrepancies, and establishing clear data governance policies. there is. Other elements of improving data quality include conducting regular assessments to review how data is collected, stored, and managed. Data profiling allows you to better understand your existing data structures and identify missing values ​​and inconsistencies. Manual checks are required to ensure that automated tools are working properly. Additionally, it is important to keep documentation up-to-date to maintain accountability, comply with regulations, and provide future reference.

At the foundation of all this, implementing a proper data management strategy requires a comprehensive understanding of all data structures within an organization’s technology environment. Achieving the “gold standard” in data management means reaching a point where data is consistently reliable and can be trusted as the basis for important decisions. This requires creating a unified and consistent data environment that eliminates inconsistencies and gives all stakeholders access to the same high-quality information.

Equally important is user training and awareness. It’s important to educate your 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 maintain high data quality standards in their system interactions. These steps address data ownership (that is, who is responsible for data quality), data access (which users can access what data), and data management (which organizations use to ensure that their data is of high quality). ensure data integrity by defining

There are several tools available to improve data quality. For example, Qlik Talend and Data Ladder DataMatch Enterprise can help you organize and fix your data. OpenRefine (formerly known as Google Refine) cleans up unorganized data. IBM InfoSphere QualityStage and Alteryx are used to standardize data formats, and SAP Data Services and SAS Data Management integrate and clean data from various sources. Informatica Data Quality and Microsoft Data Quality Services provide comprehensive capabilities for monitoring and improving data quality.

Also see my articles on how Software AG extends Super iPaaS to integrate business applications and enterprise data across multiple environments and how Informatica employs AI to help enterprises manage data please refer to.

Acumatica and the importance of data quality

Now let’s look at companies that are focused on data quality, both for themselves and their customers. Acumatica develops cloud-based he ERP software customized for small and medium-sized businesses. The company prioritizes data quality, which is critical to customer success. The company began operations in 2008 and is headquartered in Kirkland, Washington. The company focuses on streamlining business operations by providing functionality that supports finance, 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 construction, manufacturing, retail, hospitality, and wholesale distribution sectors.

Acumatica stands out because of its focus on data quality. As Case told me, “Data quality is important because if we have all the data in place, we can get our customers to use our modern ERP system rather than the legacy system they are migrating from. Data quality is critical because enterprise systems like Acumatica act as the central nervous system of a business, storing and managing core data across various departments. . This foundational or anchor data is utilized in many other business applications, including customer relationship management, warehouse management systems, business intelligence, human capital management, and more.

We have already highlighted what asset data is important to your organization. The impact of data quality (or lack thereof) is especially noticeable during systems transformation projects, such as when introducing new enterprise software. Such efforts put an organization’s data under intense scrutiny and highlight flaws.

This makes changing a company’s ERP system a difficult project, even with the latest data migration tools. If your organization is implementing critical business systems, you can run into problems if you don’t have a solid plan in place to integrate your data. “Acumatica starts implementing ERP within the sales cycle,” he says. “We ask the questions: What is the structure of your account? How up-to-date is your data? How do you get all these transactions? Do you understand your supplier network?” Acumatica utilizes a highly systematic implementation process customized for different industries to ensure a consistent approach.

This process begins with understanding your users and assessing their readiness, and extends to assessing the capabilities of your internal staff and ability to effectively perform the functions required of the system. Acumatica then follows a detailed process for data management, moving from data assessment and planning, to data migration and cleaning, to validation and testing, and finally to go-live and ongoing data management. Within each phase, Acumatica details specific tasks for governance, standardization, quality control, monitoring, training, and maintenance. All this contributes to the high success rate of his ERP implementations.

Acumatica also provides a platform for building systems and processes that prioritize data accuracy. This product includes detailed features, training, reporting, and security that have contributed to the success of many customers. One of Acumatica’s customers, Four States Trucking, uses Acumatica with accounting, inventory, sales and purchase order, service management and business intelligence capabilities, along with integrated modules for POS, WMS, e-commerce, shipping and electronic data. successfully implemented its core ERP system. Currency exchange and credit card processing.

“We focus on the commitment of our team and our clients, as this ensures data quality is addressed with a single designated point of contact,” Case said. Acumatica was supported by a dedicated project team from Four States Trucking. The team included an executive project sponsor, a project manager, and a number of functional managers who brought subject matter expertise. Their collaborative efforts ensured shared responsibility and commitment to a project centered around reliable data. While achieving and maintaining data quality is always the primary objective, we would like to emphasize that training and support are also critical to a successful implementation, as reflected in Acumatica’s processes.

summary

In a previous role, I faced difficult challenges implementing ERP. The main reason for this was that the company overlooked the importance of following critical data quality processes. As the company approached the go-live phase, significant issues surfaced, including inventory discrepancies, incorrect customer profiles, incomplete financial reporting, and production order delays. All of this is due to data issues. These issues have highlighted the importance of understanding and implementing solid data management practices. Although this investment requires significant effort, the long-term benefits can be significant for organizations that are dedicated to improving and maintaining data quality. My own painful experiences in previous roles highlight the value of addressing data management as a fundamental element of business success.

When such issues are discussed, most people realize the importance of effective data management. And in fact, it is the basis of any organization’s success. Proper data management contributes significantly to efficient and effective business processes by ensuring data access, accuracy, and reliability. It’s well worth the investment required.