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Archive for category: News

You are here: Home1 / News, Events and Articles2 / News

Running the Pre-Sync Data Check for Actor Data

Articles, Frontpage Article, News

If you are getting errors when provisioning users, these errors cannot be auto-corrected.

In SSOCONFIG, use the Data Check for Actor data which identifies errors that exist so you can fix them manually.

From the LSF server, run the ssoconfig utility.

ssoconfig -c- enter the ssoconfig utility password when prompted.

  1. select Manage Federation.
  2. select Perform Pre-Sync Data Check.
  3. Select Check Actor Data
  4. Review the output for errors. For an Actor record with missing or invalid email address, you will have to correct the errors manually.
  5. When you have finished correcting Actor errors, rerun the Pre-Sync Data Check to verify that all errors have been corrected.

Results of the Pre-Sync Data Check are written to the security log file LAWDIR/system/security_provisioning.log

 

06/19/2026
https://www.nogalis.com/wp-content/uploads/2026/06/Running-the-Pre-Sync-Data-Check-for-Actor-Data.jpg 470 470 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-19 08:43:332026-06-11 10:45:22Running the Pre-Sync Data Check for Actor Data

Event-Driven Agentic AI: What Happens When ERP Becomes the Operator

News

Enterprise resource planning (ERP) systems are evolving beyond static transaction engines into intelligent, responsive platforms that can act in real time. In a recent article from ERP Today the author – Robert Kramer of KramerERP – explores how event-driven architecture and agentic AI are reshaping ERP into an active operator within the enterprise. A core idea in the article is the shift from traditional, user-driven ERP systems to event-driven systems that respond automatically to business triggers. Instead of waiting for manual input or scheduled batch processes, ERP systems can now react instantly to events such as inventory changes, supply chain disruptions, or production issues. Layered on top of this is the rise of agentic AI, where autonomous digital agents can execute workflows, coordinate actions across systems, and make decisions within defined governance rules. These agents reduce manual effort by handling tasks like procurement, scheduling, reconciliation, and exception management—while escalating edge cases to humans when needed. Kramer emphasizes that this evolution transforms ERP from a passive system of record into a proactive “operator” that orchestrates business processes in real time. The article also highlights the growing importance of cross-system agent collaboration, where ERP agents interact with CRM, SCM, and other enterprise systems to coordinate end-to-end workflows. However, the shift also introduces challenges around governance, data quality, integration complexity, and workforce adaptation. Organizations must define clear boundaries for autonomy and ensure strong oversight of agent-driven actions. Ultimately, Kramer positions event-driven agentic ERP as a major step toward more responsive, automated, and intelligent enterprise operations—where systems don’t just record work, but actively drive it.

 

For Full Article, Click Here

06/18/2026
https://www.nogalis.com/wp-content/uploads/2016/04/ERP-IT-Enterprise-Resource-Planning.jpg 413 620 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-18 09:13:022026-06-11 11:40:30Event-Driven Agentic AI: What Happens When ERP Becomes the Operator

Autonomous Enterprise versus Composable ERP

News

As ERP (enterprise resource planning) systems continue to evolve, businesses are increasingly balancing the promise of autonomous operations with the flexibility of composable ERP environments. In an article from E3 Magazine, Editor-in-Chief Peter M. Färbinger examines how organizations are rethinking ERP strategies to better support automation, agility, and long-term digital transformation. The article explains that the “autonomous enterprise” concept focuses on AI-driven systems capable of analyzing data, automating workflows, and making operational decisions with minimal human involvement. The goal is to improve efficiency, reduce repetitive manual tasks, and help businesses respond more quickly to changing demands. At the same time, composable ERP emphasizes flexibility through modular applications and services that can be integrated based on a company’s specific needs. Rather than relying on one rigid platform, organizations can build adaptable ecosystems that evolve alongside the business. This approach allows companies to adopt new technologies faster and avoid the limitations of traditional monolithic ERP systems. A major takeaway from the article is that these strategies are not mutually exclusive. In fact, composable ERP may provide the ideal foundation for autonomous capabilities by making it easier to integrate AI tools, automate processes, and scale innovation across departments. Ultimately, Färbinger suggests that businesses combining flexibility with intelligent automation will be best positioned for the future of ERP and enterprise transformation.

 

For Full Article, Click Here

06/17/2026
https://www.nogalis.com/wp-content/uploads/2016/08/erp-enterprise-resource-planning-it-checklist.jpg 433 650 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-17 09:53:412026-06-11 11:04:52Autonomous Enterprise versus Composable ERP

Lawson Error – “Unable to log on RUNUSERKEY Account”

Articles, Frontpage Article, News

If you run into this error, it likely means that many of your batch jobs in Lawson are going into recovery. The reason this may have occurred is due to a domain name change or possibly an accidental password reset.

 

To resolve, first login into LSA, then go to Manage Privileged Identities.

Once there, click the Environment named service ie. LSFPROD, LSFTEST or however yours is named. Select the BATCH key and find out what user is assigned under it.

 

This BATCH key may be named differently so check your lajs.cfg file under RUNUSERKEY as shown below in the system folder.

If you know the latest password login to LID with it first to confirm.

 

After confirming, type the password in the password field shown below and remember to click CHANGE after you confirm the password:

 

If you’re able to track down the right user and update the password, your jobs should recover without too much trouble. If errors like this keep popping up or if you would rather not spend time digging through configs and logs, our managed services team can step in. With a dedicated group of Lawson experts behind you, we handle issues like batch job failures, user access, and system troubleshooting so your team doesn’t have to. Our goal is to keep your Lawson environment stable and reliable while you focus on running the business.

06/16/2026
https://www.nogalis.com/wp-content/uploads/2026/06/Lawson-Error-Unable-to-log-on-RUNUSERKEY-Account.jpg 470 470 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-16 08:39:132026-06-11 10:42:57Lawson Error – “Unable to log on RUNUSERKEY Account”

AI Has A Data Problem – Causal Data May Solve It

News

Artificial Intelligence (AI) may be powerful, but this article argues its biggest limitation isn’t the models — it’s the type of data we feed them. In a recent piece for Forbes, writer and AI expert Gary Drenik explores the idea that modern AI systems rely too heavily on observational, transaction-based data, and that a shift toward causal data could significantly improve results. The core problem is that most AI today learns from what has already happened — purchases, clicks, searches, and other behavioral signals. This makes models good at spotting patterns, but weaker when conditions change or when they need to explain why something is happening. The article contrasts this with “causal data,” which aims to capture the drivers of behavior before actions occur — things like intent, expectations, sentiment, and constraints. The argument is that this kind of data gives earlier and more meaningful signals about future outcomes than traditional datasets. A key point is timing. Transaction data reflects behavior after it happens, while causal signals can appear months earlier. By the time spending slows or revenue drops show up in the data, the underlying causes may have been developing for a long time.

Drenik also highlights a broader issue in enterprise AI: organizations are accumulating massive amounts of data, but much of it is noisy, delayed, or shaped by algorithms themselves. This creates scale without clarity — lots of information, but not always meaningful insight. Causal data, in contrast, aims to reduce guesswork by focusing on measurable drivers of decision-making. That can make models more robust, more interpretable, and more stable when conditions shift. The takeaway is that the next leap in AI performance may not come from bigger models or more data, but from better data — specifically data that explains why outcomes happen, not just what happened.

 

For Full Article, Click Here

06/15/2026
https://www.nogalis.com/wp-content/uploads/2016/08/data-scientist-it-infor-technology.jpg 199 500 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-15 09:49:402026-06-11 10:52:30AI Has A Data Problem – Causal Data May Solve It

AI Is Changing ERP, Not Replacing It

News

Artificial Intelligence (AI) is making enterprise resource planning (ERP) systems feel like they’re on the verge of reinvention — but, as this article in Forbes argues, it’s more evolution than replacement. In a recent Forbes article, Robert Kramer, founder and managing partner of KramerERP, examines how a new wave of AI-native ERP startups is challenging established players like SAP, Oracle, Microsoft, and Infor. These newer tools promise faster implementations, automated reconciliation, and simpler workflows — and they’re attracting significant investor attention. But the core argument is that ERP is not just software; it’s the operational backbone of the enterprise. It governs finance, procurement, inventory, payroll, compliance, and auditability. Because of that, it can’t be replaced by lightweight workflow tools or “AI layers” alone. The article draws a sharp distinction between automation and system-of-record responsibility. AI can accelerate tasks, but it cannot replace the governance structures that ensure accuracy, traceability, and regulatory compliance. In fact, applying loosely governed “vibe-coded” AI to ERP systems could introduce serious financial and compliance risks, especially in regulated industries. A major theme is data governance. AI performance in ERP environments depends less on model sophistication and more on whether underlying data is consistent, well-structured, and properly controlled. Without that foundation, AI simply scales bad decisions faster. Kramer also highlights how the ecosystem is evolving. Platforms like Snowflake, Microsoft Fabric, and SAP’s Business Data Cloud are becoming connective layers between ERP systems and AI applications, enabling more reliable data flow across the enterprise. Vendors like Infor and QAD are cited as examples of a more disciplined approach, where AI is embedded within industry-specific processes and governed workflows rather than layered loosely on top. Kramer’s takeaway is clear: AI is reshaping ERP, but it isn’t replacing it. The winners will be the systems that combine AI with strong governance, trusted data, and industry-specific operational rigor.

 

For Full Article, Click Here

06/12/2026
https://www.nogalis.com/wp-content/uploads/2024/10/business-meeting-company.jpg 333 500 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-12 09:36:162026-06-05 11:38:48AI Is Changing ERP, Not Replacing It

A Guide to Managing Infor Year-End Updates for AP and Payroll

Articles, Frontpage Article, News

This article will give you all the information you need to know about the Infor Year End Regulatory Patching that will be released by the end of the year.

 

Why Year-End Updates Matter

As you probably know the YE regulatory patches

  • Will keep you in compliance with your w2s,1099 and/or ACA reporting.
  • They will Ensure the Lawson data is accurate for tax filings and vendor payments
  • Many forms or report errors are symptoms of missing patches
  • Being Current on Updates will maintain your Lawson functionality and supportability

 

TimeLine

Infor will release a KB article at the end of November. That article contains the dates, and the documentation that is needed for patching. Make sure to subscribe/follow the article so that anytime they make a change to it, you’ll get notified.

The YE communication process starts in Nov and then the downloads will be available around the beginning of December. That is when the install files, the PDF and the change documentation will be available.

Products and Documentation

To get the documentation and downloads, log into InforXtreme and go to product downloads.

You should see a link to product search on the right-hand side.

Download Search

In the Product Search box, enter the year,2025, and Year-End. That search will return many links for all platforms. Look for the one that fits your organization

You only need to download what products you actually use so if you don’t run benefits out of Lawson, then you will not need to download the benefit Patch.

 

Reviewing The Changes

For the changes, once you get your tar file, untar them and then inside there’s will be two HTML documents one of them is a Delta read me and the other one is a regular read me file.

The Delta lists the major changes. It’s a one-page high-level view of what changes are in this patch.  The regular read me file is more detailed. Review both of those and  look for any job parameter changes on any forms or in a batch jobs, if there are any, it will require you to create a new batch job because the new parameters on the batch job will cause the fields to shift or move, so the old jobs parameters could be off once the patch is installed. The documentation will tell you if the jobs must be recreated because of perimeter changes.

other changes that may occur with YE patches

  • they may add fields to a form. This would impact your add-in queries and uploads.
  • they might change the interface file by adding a column to an interface conversion file (ex AP520)
  • Be sure to search for any database changes in the readme file by Searching for the key word dbreorg. If a DBreorg is required, it means that the structure of the database will change.
  • If there are new forms with the patch, you will need to apply security rules to the new forms for the users who will need to have access to them.

Form Changes

You want to review:

  • If AP145 has changes- make sure all your boxes are set up correctly
  • The AP245 may have the most changes in the form. They may add new tabs and/or remove tabs. They may add/remove fields, they may move fields from one tab to another. If that is the case, it is best to add a new job with a new name for the AP245.

 

Installation

  • The installation process is the same as any other CTPs.
  • You download the patches into a folder/directory on the lawson server application server
  • Use the lawappinstall command to preview, update on each patch and run activate after all patches have been run with update. The activate will pick up any patches that ran in update and haven’t been activated.
    • Perl GENDIR/bin/lawappinstall preview PDL
    • Perl GENDIR/bin/lawappinstall update PDL
    • Perl GENDIR/bin/lawappinstall activate PDL

 

TESTING

Test in a non-production environment first before you apply the patches to production. Test several different types of processing just to make sure that nothing has been impacted by the changes.

  • Run daily processing; inquiry on forms
  • run a couple of daily batch job
  • process payroll and make sure that the taxes are calculated correctly
  • validate the data; verify the benefits are deducted correctly
  • run your month end process
  • run 1099 and validate the data
  • run W-2s
  • run ACA (Affordable Care Act)

Recommended Timeline (Nov–Jan)

  • November – Install patches in non-prod and begin testing
  • December – Validate reports and reconcile data
  • January – Finalize and submit W-2s/1099s

Key Takeaways

  • Test early and document everything.
  • Communicate timelines with HR, Payroll, and Finance teams.
  • Leverage Nogalis resources for troubleshooting and guidance.
06/11/2026
https://www.nogalis.com/wp-content/uploads/2025/11/A-Guide-to-Managing-Infor-Year-End-Updates-for-AP-and-Payroll-webinar.jpg 470 470 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-11 08:40:292026-05-28 11:49:40A Guide to Managing Infor Year-End Updates for AP and Payroll

Why a modern data foundation takes more than a new platform

News

Modernizing enterprise data systems is often less about technology upgrades and more about improving governance, consistency, and trust across the organization. In an article posted on CIO.com, enterprise technology expert and CIO Thai Vong argues that the hardest part of modernization is untangling years of inconsistent reporting logic, fragmented systems, and poor governance that accumulate over time. A major issue is that many organizations struggle with trust in their data. Different teams often use different definitions for the same KPIs, while business logic becomes scattered across ETL jobs, spreadsheets, scripts, and databases. As companies grow, these inconsistencies create reporting debt and make systems harder to scale or maintain. The article stresses that modernization should focus on restoring architectural discipline, not just upgrading tools. That includes separating ingestion, transformation, and reporting layers, reducing duplicated logic, and creating a single source of truth for critical metrics. Vong also emphasizes the importance of master data management, especially around customers, suppliers, and products. Without consistent definitions and deduplication, even modern platforms can still produce unreliable reporting. Platform selection should prioritize operational fit rather than just technical capabilities. The “best” platform is the one that aligns with the organization’s skills, governance model, and long-term operating structure without adding unnecessary complexity. The article also highlights the value of phased execution, medallion architecture models, and strong operational practices like CI/CD, monitoring, and environment separation. Finally, Vong warns against leading modernization efforts with AI before the underlying data foundation is trustworthy and well-governed.

 

For Full Article, Click Here

06/10/2026
https://www.nogalis.com/wp-content/uploads/2025/08/IOT-data-tech-IT-computing.jpg 250 444 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-10 11:11:492026-06-04 11:14:47Why a modern data foundation takes more than a new platform

AI won’t fix your data problems. Data engineering will

News

Artificial Intelligence (AI) may feel like a model problem on the surface, but as this article in CIO.com argues, most enterprise failures actually come down to something more fundamental: data engineering. The author, Carter Page, EVP of research and development at Astronomer, explains that organizations are investing heavily in models, compute, and tooling — assuming better intelligence will automatically lead to better outcomes. But the real issue is that AI systems often lack the business context needed to operate reliably inside an enterprise. The problem starts with fragmentation. Customer, billing, product, and usage data are typically spread across multiple systems, each with different definitions and timing. Humans can navigate these inconsistencies through experience and judgment. AI agents, however, act on whatever data they receive — which means incomplete or inconsistent context leads to quietly incorrect decisions at scale. Page argues that this shifts data engineering from a supporting role to a core operational one. It’s no longer just about building pipelines for analytics dashboards, but about creating trusted, real-time context that AI systems can safely act on. That includes entity resolution, data freshness controls, and strong lineage tracking so organizations can understand where data comes from and how reliable it is. It also highlights a second challenge: orchestration. As companies deploy more autonomous agents, they need infrastructure to manage scheduling, permissions, cost controls, human approvals, and auditability. In other words, AI agents require the same operational discipline as any critical enterprise system. Moreover, AI doesn’t fail because models aren’t smart enough — it fails when the underlying data and operational systems aren’t designed for decision-making. Strong data engineering and orchestration are what turn AI from a promising tool into a reliable business system.

 

For Full Article, Click Here

06/09/2026
https://www.nogalis.com/wp-content/uploads/2025/11/AI-erp-it.jpg 334 500 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-09 10:49:182026-06-02 12:54:06AI won’t fix your data problems. Data engineering will

Amazon Athena SQL Syntax Quirks

Articles, Frontpage Article, News

When working with Amazon Athena, SQL syntax quirks can sometimes trip up even seasoned database professionals. In this post, we’ll look at several syntax patterns that come up often when building queries in Athena.


Common Table Expressions (CTEs) with WITH

Athena supports CTEs, which allow you to define temporary result sets that can be referenced later in the query.

✅ Correct usage:

WITH cte1 AS (

SELECT column1, column2 FROM some_table

),

cte2 AS (

SELECT * FROM cte1 WHERE column1 > 100

)

SELECT * FROM cte2;

❌ Incorrect usage:

WITH cte1 AS (SELECT * FROM table1)

WITH cte2 AS (SELECT * FROM table2)

SELECT * FROM cte1 JOIN cte2 ON …;

Athena requires all CTEs to be defined under a single WITH keyword, separated by commas.


Window Functions: LAG() and ROW_NUMBER()

When you need to look at values in previous rows or assign sequence numbers, Athena provides window functions:

  • ROW_NUMBER()
    Assigns a unique number to each row within a partition.
  • ROW_NUMBER() OVER (
  •     PARTITION BY employee_id
  •     ORDER BY start_date ASC
  • ) AS row_num
  • LAG()
    Retrieves the value from the previous row in a partition.
  • LAG(salary) OVER (
  •     PARTITION BY employee_id
  •     ORDER BY start_date ASC
  • ) AS prev_salary

These functions are especially powerful when tracking changes over time or comparing current and prior values.


BETWEEN is Inclusive

In Athena, the BETWEEN keyword includes both the lower and upper bounds of the range.

WHERE event_date BETWEEN DATE ‘2021-12-01’ AND DATE ‘2021-12-31’

The query above returns rows from December 1 through December 31.
If you want to exclude the upper bound, you’ll need to switch to an explicit condition:

WHERE event_date >= DATE ‘2021-12-01’

AND event_date < DATE ‘2022-01-01’


No APPLY, Use LATERAL Instead

If you come from SQL Server, you may be familiar with CROSS APPLY or OUTER APPLY. Athena does not support APPLY. Instead, you can achieve similar functionality with a LATERAL join.

SELECT e.*, x.*

FROM employees e

CROSS JOIN LATERAL (

SELECT *

FROM salaries s

WHERE s.emp_id = e.emp_id

ORDER BY s.effective_date DESC

LIMIT 1

) x

Here, LATERAL allows the subquery to reference columns from the outer query, just like APPLY would in T-SQL.


Key Takeaways

  • Use one WITH clause with multiple comma-separated CTEs.
  • Use window functions like LAG() and ROW_NUMBER() to track changes or assign row order.
  • Remember that BETWEEN is inclusive in Athena.
  • Replace APPLY with LATERAL joins when you need correlated subqueries.

By mastering these syntax patterns, you’ll avoid some of the most common pitfalls when writing Athena queries, and make your SQL more efficient, readable, and powerful.

06/08/2026
https://www.nogalis.com/wp-content/uploads/2026/06/Amazon-Athena-SQL-Syntax-Quirks.jpg 470 470 Angeli Menta https://www.nogalis.com/wp-content/uploads/2013/04/logo-with-slogan-good.png Angeli Menta2026-06-08 08:31:502026-06-02 12:37:30Amazon Athena SQL Syntax Quirks
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