Analytics Setup in LS Central

Explore the key features and updates in Analytics using the links below:

Analytics Setup

The Analytics Setup page added in LS Central v 28.0 is the central configuration hub for Analytics for LS Central. It also provides controls for managing data lifecycle, including configuring automated data retention to keep your data warehouse lean and performant.

In the Analytics Setup page you can:

  • Enable / Disable Analytics in general
  • Enable / Disable modules, like Hotels, Bookings, etc.
  • Enable / Disable companies
  • Trigger a Factory Reset for Analytics
  • Specify the Azure SQL Service Tier for calculation and non-calculation periods
  • Configure automated data retention to purge transactional data older than a specified number of days

General Settings

Analytics Enabled

Turns Analytics on or off for the entire system. When disabled, all feature flags are automatically turned off and all companies are disabled.

You can then enable or disable specific analytics features based on your business needs:

Feature Description Notes
Sales Enabled Calculates sales-related fact and dimension tables Always enabled when Analytics is on
Sales Enabled Calculates sales-related fact and dimension tables Always enabled when Analytics is on
Actionable Insights Enabled Calculates tables for actionable business insights Always enabled when Analytics is on
Finance Enabled Calculates finance and accounting tables Optional
Inventory Enabled Calculates inventory-related tables Linked with Supply Chain
Supply Chain Enabled Calculates supply chain tables Linked with Inventory
Hospitality Enabled Calculates hospitality module tables Optional
Hotels Enabled Calculates hotel management tables Optional
Bookings Enabled Calculates booking-related tables Optional

Note: Inventory and Supply Chain are linked – enabling one automatically enables the other.

No. of Companies

Displays the number of companies configured in the Analytics system. Click the field to drill down to the Analytics Companies list.

Days to Keep Data in Analytics

To optimize database storage and maintain query performance, Analytics includes a built-in automated data retention mechanism. This field controls the data retention lifecycle for transactional data stored in the data warehouse.

  • Default / Zero value: If this field is set to 0, data retention is disabled and historical data is kept indefinitely.
  • Non-zero value: When you enter a positive integer (X), the system automatically purges transactional data older than X days from the data warehouse once the data retention pipeline has been enabled.

The new Data Retention pipeline is used to delete data from certain staging and fact tables based on the "Days to Keep Data in Analytics" parameter in the Analytics control page in LS Central. If the value of the parameter is 0, then no data is deleted, otherwise all data older than the amount of days specified is deleted from the staging and fact tables listed here. The pipeline uses the 4 new procedures added, listed below, to delete data.

Please note that this pipeline has to be enabled manually, this is done so that no one accidentally sets a value in the control page which results in data being deleted from the Analytics data warehouse.

Tables Covered by Data Retention

The retention procedures operate across the following staging and data warehouse tables, grouped by data domain:

Procedure Staging Tables (stg$) Data Warehouse Tables ([DW])
AnalyticsPurgeOldSalesData

stg$Trans_ Sales Entry

stg$Transaction Header

stg$Sales Invoice Line

stg$Sales Invoice Header

stg$Sales Cr_Memo Line

stg$Sales Cr_Memo Header

 [DW].[FactSalesPosted]
AnalyticsPurgeOldPurchaseData

stg$Purchase Line

stg$Purchase Header

stg$Purchase Line Archive

stg$Purchase Header Archive

stg$Purch_ Rcpt_ Line

stg$Purch_ Rcpt_ Header

stg$Purch_ Inv_ Line

stg$Purch_ Inv_ Header

stg$Purch_ Cr_ Memo Line

stg$Purch_ Cr_ Memo Hdr_

[DW].[fPurchaseOrders]

[DW].[fPurchaseReceipts]

[DW].[fPurchaseInvoice]

[DW].[fPurchaseCreditMemo]
AnalyticsPurgeOldHospitalityData

stg$Din_ Area Plan Statistics

stg$Dining Table Receipt Entry

stg$Dining Table History Entry

stg$Din_ Tbl_ Rcpt_ Entry Arch

stg$Din_ Tbl_ Hist_ Entry Arch

stg$Din_ Tbl_ Hist_ Entry Res_

stg$Din Tbl Hist Entry Res Arc

stg$Din_ Reserv_ History Entry

stg$KDS Modified Status

stg$KOT Line Routing

stg$KOT Line

stg$KOT Header

[DW].[fDiningAreaPlanStatistics]

[DW].[fDiningReservation]

[DW].[fKOTLines]
AnalyticsPurgeOldTransferData

stg$Transfer Line

stg$Transfer Header

stg$Transfer Shipment Line

stg$Transfer Shipment Header

stg$Transfer Receipt Line

stg$Transfer Receipt Header

[DW].[fTransferOrders]

[DW].[fTransferOrderShipments]

[DW].[fTransferOrderReceipts]

Tasks Configuration

No. of Analytics Tasks

Shows the current number of Analytics Tasks in the system. These tasks track operations like company activation, deactivation, and factory resets.

Days to Keep Analytics Tasks

Specifies how many days to retain Analytics Task history before automatic cleanup. Default is 365 days, a value of zero keeps all tasks.

Analytics Tasks can be deleted manually from the Analytics Tasks page or by using the Codeunit 10012035 in the Scheduler.

Azure Service Tier Settings

Optimize performance by automatically switching between Azure SQL service tiers during analytics processing.

Azure Service Tier (Calc.)

The Azure SQL service tier to use during Analytics pipeline processing and data warehouse calculations. A higher tier provides better performance for intensive calculations, but it will also increase the cost of the database for the duration it is scaled up.

Available DTU Standard Tiers in Azure: S0, S1, S2, S3, S4, S6, S7, S9, S12

You can view more about the price of the different tiers under the Cost Components and Calculator page.

Azure Service Tier (Normal)

The Azure SQL service tier to use during normal operation when Analytics is not actively processing. Use a lower tier to optimize costs.

How it works:

  1. Before running Analytics pipelines, the system automatically switches to the Calculation tier
  2. After processing completes, it switches back to the Normal tier
  3. Leave blank to disable automatic tier switching

Analytics Companies

The Analytics Companies page manages which companies participate in Analytics processing.

Analytics can be enabled or disabled individually for each company in your Business Central environment

Managing Companies

Viewing Companies

  • Navigate to Analytics Companies from the Analytics Setup page
  • The list shows all companies in your Business Central instance
  • Each company has an Analytics Enabled checkbox

Enabling a Company

  1. Locate the company in the list
  2. Check the Analytics Enabled field
  3. The system automatically creates an activation task with task type "CMP_ACTIVATE"

(Previous activation/deactivation tasks for this company become obsolete)

Disabling a Company

  1. Locate the company in the list
  2. Uncheck the Analytics Enabled field
  3. The system automatically creates a deactivation task with task type "CMP_DEACTIVATE"

(Previous activation/deactivation tasks for this company become obsolete)

Update Companies Action

Use the Update Companies action to refresh the company list if you have added new Companies to LS Central that you want to include in Analytics.

Analytics Tasks

The Analytics Tasks page provides visibility into Analytics operations and serves as an audit trail.

Each task contains:

Field Description
Entry No. Unique sequential identifier
Task Name Type of task (CMP_ACTIVATE, CMP_DEACTIVATE, FACTORY_RESET)
Parameter Additional information (e.g., company name)
Created at Date and time when the task was created
Created by User ID who created the task
Completed (Internal) Internal flag indicating task completion
Obsolete Marks tasks that are no longer relevant

Managing Tasks

Delete Tasks (Days to keep)

Deletes all Analytics Tasks older than the retention period specified in Analytics Setup.

Delete Obsolete Tasks

Deletes all tasks marked as Obsolete.

Actions and Operations

Factory Reset Action

Initiates a complete factory reset of the Analytics system. This creates a FACTORY_RESET task that instructs the Analytics pipeline to:

  • Clear all calculated data warehouse tables

  • Rebuild the data warehouse from scratch

  • Recalculate all dimensions and fact tables

Warning: This is a destructive operation that will remove all calculated analytics data. Use only when necessary.

Also, please note that since the files exported using bc2adls are deleted after the staging process has completed. You will need to reset and export all tables at the same time you perform the Factory reset task. This is explained in more detail in the bc2adls operations page.

Getting data from Setup page to Analytics

Now that you have enabled features, set parameters you need to push the data from LS Central to Analytics.

The Analytics Setup tables are not company specific and are called:

  • LSC Analytics Company$5ecfc871-5d82-43f1-9c54-59685e82318d
  • LSC Analytics Setup$5ecfc871-5d82-43f1-9c54-59685e82318d
  • LSC Analytics Task$5ecfc871-5d82-43f1-9c54-59685e82318d

The data from these tables is pushed/pulled into Analytics by query, replication or export depending on the LS Central platform and data export method:

LS Central On-prem

The two pipelines that utilize the setup page data, query the LS Central source tables directly.

Replication using Data Director

The two pipelines, that utilize the setup page data, query the prestaging tables created during Analytics setup.

To populate these tables normal subjobs must be created for the three tables and added to the INS_NORMAL_FULL job

BC2ADLS

If you are running BC2ADLS export you need to add the three tables to the bc2adls export configuration in your production environment and export the data to the storage account.

New pipelines

Once the data has been added to the prestaging tables it will be picked up by new pipelines that should be run instead of the Initial load and Scheduled run pipelines.

The pipelines are called:

  • Initial load with Control Page processing

  • Scheduled run with Control Page processing

These pipelines consider the information that has been added to the control page before the intial load and then since the last scheduled run.

If you are using the control page to setup Analytics then you should use the new pipelines for the Initial load and the Scheduled run trigger.

Analytics URLs

The Analytics URLs section stores the direct links to the Power BI reports included in the Analytics for LS Central product package. Entering the URLs here makes them accessible directly from within LS Central, allowing users to open the relevant report without leaving the system.

Each field corresponds to a specific report:

Field Description
Analytics Dashboard URL Link to the main Analytics Dashboard Power BI report, providing a consolidated overview of key business metrics. Open report
Finance Report URL Link to the Finance Power BI report covering accounting and financial data. Open report
Sales Report URL Link to the Sales Power BI report covering sales transactions and performance. Open report
Inventory Report URL Link to the Inventory Power BI report covering stock levels and inventory movements. Open report
Supply Chain Report URL Link to the Supply Chain Power BI report covering purchasing, receipts, and supply chain operations. Open report
Hospitality Report URL Link to the Hospitality Power BI report covering dining, reservations, and kitchen operations. Open report
Bookings Report URL Link to the Bookings Power BI report covering booking-related data and activity. Open report
Hotels Report URL Link to the Hotels Power BI report covering hotel management data. Open report
Actionable Insights Report URL Link to the Actionable Insights Power BI report surfacing recommended actions based on business data. Open report

Note: The URLs are available from your Analytics for LS Central product package. Only populate the fields for the modules you have enabled.

Best Practices

Performance Optimization

1. Use Service Tier Switching

  • Configure higher tier for quicker transformations and calculations (e.g., S9 or S12)

  • Configure lower tier for normal operation (e.g., S0 or S1) but be aware of the max size of the database on different tiers.

    • A database that exceeds 250 GB can not be scaled below S3 and if scaling down fails then the database remains at the high pricing tier.

  • Reduces costs while maintaining performance

2. Enable Only Needed Features

  • Don't enable features you don't actively use

  • Reduces processing time and resource consumption by the ETL processes in the Azure data factory

3. Selective Company Enablement

  • Start with production companies

  • Exclude test and development companies unless needed

4. Configure Data Retention

  • Set Days to Keep Data in Analytics to a value that reflects your reporting and compliance requirements

  • Keeping only the data you need reduces database size, lowers Azure SQL storage costs, and improves query performance

  • Leave the value at 0 if you require full historical data or have not yet determined your retention policy