Companies are spending a considerable amount of money on ERP modernization, AI, automation, and connected digital solutions. However, far too many organizations fail to consider the most important factor that can make or break these technologies: the quality of the data they rely on.
Having access to an advanced ERP application or extra AI-powered tools could be a wonderful addition to a company; however, with incomplete, duplicated, outdated, or departmentally fragmented data, the technology may merely generate faster outcomes with unreliable data. To plan the next big technology upgrade, businesses should consider whether their data foundation is sound enough to support it.
The Secret Challenge with Tech Refreshs
ERP systems and AI applications rely on uniformity of information. Accurate and accessible customer records, inventory details, financial data, supplier data, employee records, and operational metrics are necessary.
But over the years, spreadsheets, standalone applications, legacy databases, and manual methods can cause data issues. Customers can be registered by various names, product data can be available in different formats, and significant data can be stored in systems that are incompatible.
These challenges may go overlooked until a company tries to make a significant digital transformation.
The problem with bad data becomes an ERP problem.
An ERP implementation is connecting various business functions together. Shared information could be necessary for finance, procurement, sales, inventory, manufacturing, and other departments.
If this information is not consistent prior to migration, then the new ERP system can receive problems as well. Staff can waste time making corrections to the records rather than utilise the system effectively.
Therefore, data cleansing should be performed before the migration and not after the new platform is launched. Organizations should be aware of duplicate records, eliminate redundant data, standardize data formats, and define the responsibility for important data.
In the AI era, data quality becomes even more critical.
AI adds a new level of reliance on business data. AI can sift through massive amounts of data to find patterns, make predictions, and make decisions. However, the value of such outputs is largely dependent on the data being analysed.
An AI-powered system fed with partial sales data, for instance, could generate inaccurate customer behavior analysis. Likewise, incorrect inventory data may also impact forecasting and planning.
This is not to say that companies should have absolutely perfect data before they dabble in AI. It involves learning about trustworthy data sets, data sets that could be improved, and places where the data sets may be restricted.
Establish A Strong Data Foundation First
To kick off an ERP or AI upgrade, it's essential to have a structured data preparation process in place.
Audit Existing Data
Identify important data storage locations first. Map databases, spreadsheets, applications, and departmental systems to understand the current data landscape.
Eliminate duplicate and error information.
Duplication of customer records, outdated product information, missing fields, and inconsistent names can cause issues when integrating with a system. The cleaning of such records can result in simplified complexity.
Establish Data Ownership
It is important to have clear ownership of every important dataset. There should be someone on the team who is accountable for getting the data right, approving it, and solving data quality problems.
Standardize Information
Data can be more easily shared from one system to another in a common format and definition. Having consistent naming conventions, addresses, product codes, financial information, and other fields that are important in terms of integration can help.
Technology Should Come After Data Strategy
When it comes to AI and next-generation ERP platforms, the buzz can sometimes push businesses into quickening the pace of their upgrade. However, technology isn't enough to fix years of inadequate data management.
A better way is to consider preparing the data as a component of the transformation plan. With a solid understanding of its information, better-quality information, and good governance, ERP and AI investments have a more solid foundation.
The next digital upgrade that your company makes could be the most sophisticated ever acquired, but it could also be the least sophisticated ever installed, because it could rely on the most basic of all, whether the data below it is reliable or not.
FAQs
1. why is it important for businesses to do data cleansing prior to an ERP implementation?
Before implementation, cleaning data reduces duplicate, inaccurate, outdated, and inconsistent data from entering the new ERP environment.
2. What are the potential consequences of harmful data on AI performance?
Since AI relies on information it processes, poor-quality data can affect the reliability of the insights, predictions, classifications, and recommendations it offers.
3. Evaluate and explain the concept of data governance.
Data governance is a framework to establish the collection, management, protection, standardization, and upkeep of data throughout an organization.
4. What are the things that companies should do before upgrading their ERP?
Before starting an ERP upgrade, companies should consider data quality, duplicate records, system integrations, ownership, data formats, legacy information, and data migration needs.
5. How does data preparation help minimize ERP implementation challenges?
With proper preparation, organisations can more quickly detect data-related problems, the migration can be easier, and processes for maintaining information following implementation can be easier.
