SAP Solutions deals with data exchange among multiple systems in order to cover all core business processes. Keeping all master and transaction data consistent in hybrid environment with On Premise, SaaS cloud, SAP Cloud Platform components is a very challenging task due to the multiple communication channels like inbound-outbound queues, iDocs and Web Services.
Monitoring and Maintaining data consistency is a characteristic of high performing information system.
In this blog, I would like to use the specific monitoring use-case of data consistency management (DCM) in SAP Solution Manager to illustrate how to take advantage of Focused Insights metrics and KPIs capabilities.
Maintaining data consistency relies on the possibility to visualize and manipulate the same data consistency measurements in multiple forms: Here, we need to differentiate metrics and KPIs:
Metrics and KPIs
· Metrics are quantifiable measurements that help IT Solution Centers to manage efficiently the services provided by their IT. Metrics give an instant and exact picture of the status of the IT.
· KPIs are also metrics used to evaluate if an organization is meeting its objectives. A KPI is always associated to a target, which measure if its objectives are achieved. A KPI obtains its value from aggregating a metric.
Metrics and KPIs visualization
To be effective, it is important to have a reporting/dashboard system where you can visualize any of your metrics and your KPIs in the following forms:
- Time series
- Service Level Objective
A set of measurements obtained over a period of time, at regular intervals. Time-series contain only numeric data types and are indexed by one date field. In general, the period is always a Rolling period (A window of time that moves continuously: yesterday, last 3 months, …).
Ex: Monthly Sales Order not posted to FI, Number of Incidents per day. Number of transports per day for Last week.
Tables can include various unsorted data types (strings, numbers, dates, etc.) and can be filtered by different fields. The result corresponds to a snapshot of the metrics data at a specified date.
Ex: Open Incidents, Data base inconsistency.
Service Level Objective (SLO)
SLOs are constructed by aggregating a metric with the following attributes:
· Aggregation method (maximum, minimum, average, last, accumulation)
· Target to achieve.
· Target trend (maximum, minimum).
Ex: Less than 20 number of master data differences per month.
Event are constructed by extracting the last value of a metric time series and rating this value against a target with two thresholds for warning and error.
It corresponds to the last snapshot of the data of the metrics.
Ex: High Transaction Response Time. High number of Failed transport
Cross Database Comparison (CDC)
CDC is a SAP Solution Manager application used to compare data sources with a complex structure or hierarchy across different systems like sales orders with several items or a master data records distributed across several tables. You can check if the data in the source and the target systems is consistent and if the source system data have been correctly replicated.
CDC is accessible from the SAP Solution Manager Launchpad. Once the setup of CDC is performed, comparison runs are scheduled at regular intervals as shown in the screen below:
Finally, the result of each run is accessed by business operators to fix the identified issues.
In Focused Insights, each CDC comparison object could be analyzed with 8 metrics:
Data Inconsistencies occur when two instances representing the same data have different values or are missing absolutely in one of both systems.