Data management

Data management contains BW analyses which are described below. Analyses System size and Housekeeping potential are described in the Basis add-on chapter.

_{Data management analyses tiles}

Busy and quiet times of queries and DTPs

The analysis provides an overview of busy and quiet times of queries and DTP loads which were executed on all InfoProviders on a monitored system. The heatmap shows a total number of queries, DTPs or their sum. You can chose these options in the combo box View. Data is aggregated by weekdays and hours and it can be filtered by specific InfoProvider or by analyzed period. This analysis can help you to find quiet hours e.g. for operations which need more system resources. Also the busiest hours can be examined and their content can be split into other less busy hours.

_{Busy and quiet times of queries and DTP analysis chart}

InfoProviders size

The analysis provides an overview of all InfoProviders in the system divided into 3 categories. Each category represents size of InfoProviders that correspond to a particular group. E.g. "10%" group represent first 10% largest InfoProviders and their corresponding size. By clicking on a bar, application will be redirected to "Top InfoProviders" analysis, where "Top %" filter will be applied accordingly.

_{InfoProviders size analysis chart}

You can switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

InfoProviders by year

This analysis provides an overview of InfoProviders in the system. Two charts are displayed here:

Distribution by InfoProvider type - displays whole system per InfoProvider type
InfoProviders - objects in the chart are sorted by size by default and can be further filtered

Both charts always display last 4 years + all others merged into other group (e.g. 2013 and lower) based on amount of InfoProvider data that belong to the group. InfoProviders chart can be filtered using:

Top % - displays InfoProviders that belongs to a particular filter, e.g. "10%" will display first 10% of InfoProviders,
# of objects - specifies max. number of objects to be displayed

_{InfoProviders by year analysis chart}

You can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

It is possible to switch color mode of bottom chart to "Query usage". This colorizing mode groups data in InfoProvider by their respecting reporting usage. All data that were reported on less than 10 times are colorized with blue color and all data reported on more than 1000 times are colorized with red color. Warm data colorized with orange color belong to the category between these two threshold values.

_{InfoProviders by query usage analysis chart}

You can change these threshold values by clicking on the button.

NLS potential

Based on the analysis performed in the system, the visualization provides you with an overview on the potentials for archiving of particular object types (such as InfoCubes, DataStore objects, Write-Optimized DSOs) in form of column charts.

_{NLS potential analysis chart}

You have the possibility to change archiving settings.The analysis recalculates the archiving potential accordingly by the button .

_{NLS potential analysis settings}

Table view provides a detailed archiving potential on object level. You can download data into a CSV file for further processing.

_{NLS potential analysis table view}

Data distribution

These analyses provide information about how data is distributed in InfoProviders based on a specified characteristic and based on how many times these data areas were accessed by reporting (queries). The number of Data distribution analysis tiles depends on the settings on the central system. By default the BW module is delivered with only one predefined Data distribution analysis. The Data distribution analysis tile displays the number of analyzed InfoProviders which contain the specified characteristic. The below figure displays temperature based data categorization which is a type of data distribution and is by default included in the package.

Data distribution tile

By clicking on tile you can view detailed results of the specified Data distribution analysis.

First load of the analysis results can take multiple seconds due to postprocessing of the results from the system.

The result table contains multiple columns. Below are columns that are present in all Data distribution analyses results:

Column	Description
Object	contains technical name of an InfoProvider
Type	displays the type of InfoProvider (INFOCUBE, DSO, ADSO, WODS)
Description	long text of InfoProvider
Size [MB]	total object size in MB
Number of queries	total number of queries that read data in InfoProvider
Trend - Number of queries	visualization displaying the usage of data areas
Trend - Size [MB]	visualization displaying the size of data areas
Split characteristic	describes applied time characteristics
Archived size [MB]	size of unpacked archived data in MB
DTP source count	number of times InfoProvider was used as a source in DTP load requests
Last DTP exec.	timestamp of last DTP load request execution
Lookup count	number of lookups that are present in a system for an InfoProvider

_{Data distribution analysis standard view}

The columns number of queries and size of data areas depend on data distribution settings of a particular analysis. These columns are created based on data groups you specify in your analysis. See /wiki/spaces/ITOA/pages/14700282 for details about how to define data distribution and it's respective data groups for analysis. Each of these columns contains a sum (for sizes) and sum (for the use) of corresponding data area values. The footer of the table displays the number of records was already loaded to the table. This number will gradually increase, as you scroll down in the table.

Note that data groups will be empty, if the distribution analysis could not identify split characteristic for a particular InfoProvider and distribution wasn't performed. For more information why split characteristic was not identified please check /wiki/spaces/ITOA/pages/14700282.

You can filter the content of a table column by clicking on the column header. There is also a possibility to download the table content (without chart columns) into a csv. file by clicking on toolbar button. You can also hide/show specific column groups by clicking on column group in View options toolbar. You can sort results based on field of your preference, by clicking again on column header:

_{Sorting and filtering directly according to the column header}

You can click on any InfoProvider line to display queries that were executed on the InfoProvider. Each line in this detail view contains a query that was executed with a filter selection that was used during its execution (only filters for the same characteristic as was used for data distribution are displayed).

_InfoProvide_{r query execution details}

User behavior

User behavior contains BW analyses which are described below.

_{User behaviour analyses tiles}

Data size vs. query usage

BW objects data partitions scatter plot

The visualization shows the relationship between data access and size of InfoProviders divided by time characteristic (in this case, year) in form of a scatter plot. It can show user how many times were data (of corresponding year) of an InfoProvider accessed in reporting. Visualization summarizes displayed information in tiles on right hand side.

_{Data size vs. query usage scatter plot}

It is possible to select multiple objects and display only them in the chart.

_{Analysis area sub-selection}

If you want to go back to previous selection (scale) just click on the button or if you want to display all collected objects click on the button. The user can also filter data. Table view is also available in one to one form, that means objects that you can see on chart will be listed in table form and ready to download into a CSV file for further processing.

Front-end usage

The visualization shows usage of different front-ends. It shows user which front-ends are used and how many queries were executed via them per some analyzed time period.

_{Front-end usage analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

It is possible to click on the data in the plot area and user will be redirected to the other analysis called 'Query usage by country' with used filter based on the chosen data.
To distinguish between front-ends we are using handle type (field HANDLETP) of table RSDDSTAT_OLAP. Here is the list of possible front-ends that we are collecting:

Handle type	Front-end description
CMD	BeX Web Application
EXTN	External application (needs to be defined) e.g. Analysis office, BOBJ, Crystal reports
XLS	Excel
MDX	OLAP BAPI (MDX, XML for Analysis, OLE DB for Olap)
BICS	ABAP BICS API
W3_I, W3_T	Web templates
BTCH	Batch Query
PLAN	Planning
PLPE	Planning via Planning engine

We provide also possibility to define your custom logic for separation of front-ends that is based on handle type and you can define it by implementation of BADI "/DVD/HS_GET_QUERY_FRONTEND_USG".

Query usage by country

The visualization shows usage of different queries from all over the world. It shows user from which countries queries were executed and how many times, per some analysed period.

_{Query usage by country analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

It is possible to click on the country in the map or in the bar chart and user will be redirected to the other analysis called 'Query usage by user group' with used filter based on the chosen data.
Identification of country is done by company address assigned to user that executed query.

We are providing possibility to implement custom logic of assignation user to country by implementation of BADI "/DVD/HS_CENTRAL_GET_QUERY_GRP".

Query usage by user group

The visualization shows the usage of different queries according to user groups per some analysed period.

_{Query usage by user group analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

It is possible to click on the user group in the bar chart and user will be redirected to the other analysis called 'Query usage by query group' with used filter based on the chosen data.
As default implementation for identification of user group, "User group for authorization check" of user is used. We are providing possibility to implement custom logic of assignation user to user group by implementation of BADI "/DVD/HS_CENTRAL_GET_QUERY_GRP".

Query usage by query group

The visualization shows the usage of different queries according to query groups per some analysed period.

_{Query usage by query group analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

It is possible to click on the user group in the bar chart and user will be redirected to the other analysis called 'Query top usage' with used filter based on the chosen data.
Query group is read from InfoArea which contains InfoProvider where queries were executed. We are providing possibility to implement custom logic for identification of query group by implementation of BADI "/DVD/HS_CENTRAL_GET_QUERY_GRP".

Query top usage

The visualization shows the most used queries. Queries can be filtered using:

Top % - displays queries that belongs to a particular filter, e.g. "10%" will display first 10% of queries,
# of objects - specifies max. number of objects to be displayed

_{Query top usage analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

It is possible to click on the query in the bar chart and user will be redirected to the other analysis called 'Query runtime single' with used filter based on the chosen data.

Disused / Unused queries

The visualization shows disused and unused queries per some analysed period. It shows the user queries which are not used anymore "disused" and those which were not used at all "unused".
Queries can be filtered using:

Top % - displays queries that belongs to a particular filter, e.g. "10%" will display first 10% of queries,
# of objects - specifies max. number of objects to be displayed

_{Disused queries analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

The user can switch between disused and unused analyses using appropriate radio buttons. Unused queries are displayed as a list.

_{Unused queries analysis list}

Data can be downloaded into a CSV file for further processing.

InfoProvider usage

The visualization shows reporting usage (query executions) of InfoProviders in analysed time period.

_{InfoProvider usage analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

System health

System health contains analyses which are described below.

_{System performance analyses tiles}

The worst runtime queries

The visualization shows the worst and also best query executions runtimes per some analysed period per day. For each day maximum of 10 worst query executions are captured.

_{Query worst runtime analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.
It is possible to click on the data in the plot area or in the table and the user will be redirected to the exact selection of query for corresponding worst execution, where data can be filtered and downloaded into the CSV.

_{Selection filters of worst query execution}

Query runtime

The visualizations show query average runtimes per some analyzed period. There are two charts visible. Left side chart displays query groups based on average runtime i.e. how many queries in system belong to each performance group during analyzed period. Right side chart displays scatter chart plot for each individual query with average runtime and number of executions. The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

_{Query runtime analysis charts}

User can click (drill down) in the left column chart in order to get more information about query execution.

_{Query execution details table}

Here it is also possible to filter or download data. User can also click (drill down) on the row in order to get more information about query execution as time when query was executed, min., max. and average query runtime and also selection string.

To this analysis it is also possible to go directly from the scatter plot in the Query runtime analysis (by drilling down on the data in the scatter plot).

_{Query execution details per filtering InfoObjects combinations}

Here it is also possible to filter or download data or switch to table view and download data to CSV file for further processing.

Query runtime by categories

The visualizations show query average runtimes according to query processing categories per some analyzed period. Displayed categories are BI Suite, Data Manager, Olap - Cache, Olap – Olap, Olap – Authorizations and Olap – Input Help (for more info).
Top part of analysis display average time spent in processing by each category for whole system. While bottom chart displays average processing times in categories for individual queries.

_{Query runtime by categories analysis chart}

The user can filter data and also switch to the table view for more detailed information where data can be downloaded into a CSV file for further processing.

KPI analyses

There is a lot of possible KPI analyzes which can be visualised per some analyzed period (check appendix H: Complete list of KPIs).

_{CPU usage analysis chart}

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