How Simple Finance Removes Redundancy

Jens Krueger

Posted by Jens Krueger on September 30, 2014

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At SAP, we set out to change how finance is done. Traditional systems relied on inflexible models, precomputed data, and slow computational systems. With SAP HANA and SAP Simple Finance we changed that, to give you a more agile approach to financial management and planning. By eliminating things such as needleless duplication and precomputation of data which clogs other systems, we have significantly lowered your cost of managing Financials. And because our experience at building robust accounting systems, we can do this without disruption to your business. By freeing your system of the constraints of precomputed data models, we also open up your system to a more agile way of computing, allowing financial analysts to quickly experiment with what-if models while speeding quarterly closes.

This blog post is the first of a series that dives deeper into various aspects of SAP Simple Finance and their technical underpinnings.

Recently, Hasso Plattner wrote about the impact of aggregates and explored the negative impact of materialized aggregates and why relying on them for performance severely restricts flexibility. Generally speaking, the generic term materialized view and the special case materialized aggregate refer to the physical storage of derived and redundant data in a database. In this deep dive, we look closer into the technical foundations and highlight the positive impacts that can be realized when removing materialized views. We do this by looking at SAP Simple Finance – SAP’s next-generation Financials solution that for the first time does accounting without materialized views powered by SAP HANA. We are going to explore why materialized views and materialized aggregates are no longer necessary and how removing redundant data storage in a non-disruptive manner improves transactional throughput and lowers storage costs without compromising analysis performance.


In this blog post, we focus on the non-disruptive changes to the data model that removed redundancy from SAP Simple Finance and why switching to Simple Finance is possible in an entirely non-disruptive manner. In the next two blogs we will investigate the concepts of materialized views and materialized aggregates, respectively, and demonstrate that it is indeed feasible with in-memory database systems to get rid of these redundant constructs. Future parts of the deep dive series will also highlight additional improvements and paradigms of Simple Finance that are possible thanks to SAP HANA and focus on, for example, the business value associated with Simple Finance, non-disruptive innovation, and how Simple Finance enables decision makers to overcome aggregate information loss.


In this first part of the series, we begin with exploring the concept of redundancy in general. Afterwards, we look deeper into the changes of the data model brought with Simple Finance and highlight how this non-disruptive innovations has been possible, allowing an almost seamless switch-over to SAP Simple Finance. We demonstrate the positive impact of the new data model on database footprint and transactional throughput.


While we focus on the Financial Accounting component, other components such as Management Accounting (Controlling) have similarly been changed, so our comments apply to Simple Finance as a whole. The following explanations focus on the classical data model in order to foster easier understanding for all readers, even those not familiar with New General Ledger Accounting (New G/L). Please note that SAP Simple Finance is using New G/L.

SAP HANA Makes Redundant Data Obsolete

Redundant data refers to data that is physically stored in a database but could be derived by calculating it from other data in the database (Codd: “A Relational Model of Data for Large Shared Data Banks”; Communications of the ACM 13(6); 1970). Redundancy is a frequent source of inconsistency and anomalies. As redundant data needs to be kept in sync on updates, redundancy in a data model leads to slower updates. To reduce redundancy, database normalization techniques such as normal forms have been introduced (Codd: “Further Normalization of the Data Base Relational Model”; IBM Research Report RJ909; 1971). Historically, redundant data has nevertheless often been stored explicitly only to improve read performance, as the calculations to derive it in the absence of materialization required too much additional effort.


For years, enterprise applications have employed redundant data storage in order to provide sufficient performance to users in transactional and analytical applications alike. The limited performance of traditional, disk-based database systems required redundantly kept data that could be accessed quickly, but needed to be updated and kept in sync with transactional changes. SAP ERP Financials has historically not been an exception. In view of millions or billions of accounting documents (headers, stored in table BKPF) and their line items (table BSEG), materialized views and aggregates as mechanisms to maintain redundant data provided fast access to items with specific properties. For example, SAP ERP Financials contained a materialized view for all open Accounts Receivable line items (table BSID) and materialized aggregates of the debit, credit, and balance amounts for each customer per fiscal period (table KNC1).


Building on SAP HANA’s in-memory technology, it has now been possible to non-disruptively transform the Financials system into a purely line-item-based Simple Finance that gets rid of all redundant financials data. Thus, SAP Simple Finance overcomes the associated costs such as reduced transactional throughput and increased database footprint. At the same time, SAP Simple Finance is a non-disruptive innovation of the classical ERP Financials because it replaces the materialized views with non-materialized compatibility views. All applications that have read from the materialized views, be it SAP standard reports or customer modifications, continue to access the views as before without requiring any changes.


Although these are already big breakthroughs, the most important advantage of SAP Simple Finance is the dramatically improved flexibility that, for example, encourages exploring the data according to various analysis needs and contributes to the success of Simple Finance as an information system. Researchers have since long stressed the value of information flexibility – see, for example, “Searching and Scanning: How Executives Obtain Information from Executive Information Systems” (Vandenbosch and Huff, 1997; MIS Quarterly 21(1)), “Exploring the perceived business value of the flexibility enabled by information technology infrastructure” (Fink and Neumann, 2009; Information & Management 46), or “An empirical investigation of the factors affecting data warehousing success” (Wixom and Watson, 2001; MIS Quarterly 25(1)). A follow-up blog post will elaborate on the business value that can be realized thanks to the gain in flexibility and the simplification.

A New, Non-disruptive Data Model with Zero Redundancy

The fundamental and essential data tuples of every financial accounting system are the accounting documents and line items, in this case stored in the tables BKPF and BSEG, respectively. The traditional data model of SAP ERP Financials – which is running on SAP HANA as part of SAP Business Suite powered by SAP HANA (in the following: Suite on HANA) – additionally contained materialized views that allowed fast access on disk-based database systems to open and cleared line items, separated by accounts receivable, accounts payable, and general ledger accounts (tables BSID, BSAD, BSIK, …). As calculations and observations in future installments of this deep dive series will show, SAP HANA’s in-memory database makes materialization obsolete thanks to the improved performance. Hence, the decision for SAP Simple Finance was clear: the previously existing materialized views have been removed and replaced by compatibility views. These compatibility views transparently provide access to the same information calculated on the fly to ensure that the changes are non-disruptive.


Materialized aggregates in the traditional Financials data model included totals per account receivable (KNC1), account payable (LFC1), and G/L account (GLT0 / FAGLFLEXT). In addition to removing the materialized views, SAP Simple Finance also evolves the traditional Financials data model by removing the materialized aggregates from the system, again replacing them by compatibility views.


In the end, the transactional data model of core Financial Accounting – besides master data – consists only of the essential accounting document header (BKPF) and accounting document line item tables (BSEG). All the other tables mentioned on the left side of the following Figure 1 are then obsolete. Instead, transparent access to exactly the same information is provided on-the-fly by equally named compatibility views, as outlined on the right side of Figure 1.

Key elements of Financials data model before and after simplification

Figure 1: Key elements of Financials data model before and after simplification


Non-disruptive Innovation

SAP ERP Financials is being used all over the world by many thousand customers. It offers rich modification options to customer to adapt the system to their needs, including custom modifications. Changes to the core data model need to take this into account. They have to be non-disruptive with regard to the large amount of code (by SAP and its customers) that builds on top of the data model. To ensure adoption of the benefits of SAP Simple Finance, all innovations have been implemented in a non-disruptive manner. Switching to SAP Simple Finance does not require updating any custom coding for reading from the database.


So-called compatibility views have been the means to achieve this non-disruptiveness: a compatibility view is a non-materialized view – essentially, a named query – that seamlessly replaces a materialized view (or materialized aggregate). As it has the same name and is transparently accessed via the data dictionary, existing applications that so far relied on the materialized view for accessing items or aggregates do not experience any difference. They continue to work as before without any changes to the code. For example, a query accessing BSID has previously read the value from the materialized view. Now, the same access to BSID is resolved into a query using the view definition and calculated on-the-fly. The result is the same in both cases. As the calculations have demonstrated, this is feasible without compromising performance.


As a consequence of the non-disruptive innovation, all of the benefits associated with Simple Finance – such as increased throughput, flexibility, and reduced database size – are available at no adaptation costs to customers – a migration of the database (mostly removing the redundant tables) is sufficient.


A future deep dive will look into non-disruptive innovation and the technology of compatibility views in more detail.

As a benefit of the non-disruptive nature of the above-mentioned changes, code changes in the Financials component (done by SAP) were only necessary to remove the now unnecessary writing operations to former materialized views and aggregates when posting accounting documents. In addition, code has been adapted to further leverage the possibilities of SAP HANA. This includes code pushdown of data-intensive logic to the database and backend decoupling for new Web-based user interfaces (SAP Fiori).

Reduction of Database Footprint

The removal of both materialized views and materialized aggregates significantly reduces the database footprint of Financials that has been occupied by redundant data. In an exemplary system from an SAP customer, the total size occupied by the set of core tables for financial accounting is reduced by a factor of 6.5 with SAP Simple Finance. For details, please refer to the following table that includes a non-exhaustive list of tables that have been replaced by compatibility views.


Table sizes of customer system



ERP Financials in Suite on HANA

Simple Finance on HANA


Accounting documents

1.2 GB 1.2 GB


Accounting document line items

4.5 GB

** 5.0 GB


Open items Accounts Receivable

0.1 GB


Open items Accounts Payable

0.0 GB


Open items General Ledger

* 23.2 GB


Cleared items Accounts Receivable

3.3 GB


Cleared items Accounts Payable

2.1 GB


Cleared items General Ledger

* 5.5 GB


Totals Accounts Receivable

0.1 GB


Totals Accounts Payable

0.0 GB


Totals General Ledger

0.0 GB

Total (of above tables)

40.0 GB 6.2 GB

* Includes partially archived items (not considered here)

** Includes additional fields for New G/L

The numbers shown above are in both cases already for a system running on SAP HANA as the database. Factoring in the additional savings in database size enabled by HANA’s storage architecture and compression, the potential savings are even more impressive. Looking at SAP’s main internal ERP system, the following Figure 2 demonstrates the end-to-end possibilities in database size reduction. The combination of SAP HANA’s inherent compression with the removal of redundancy allows running a company such as SAP – with revenue of close to 17 billion Euro in 2013, over 250,000 customers, and over 65,000 employees – completely in-memory using commodity enterprise hardware.


Figure 2: Database sizes of SAP’s main internal ERP

Increase of Transactional Throughput

The removal of materialized views and materialized aggregates immediately has a positive impact on the transactional throughput of the system. Posting an accounting document no longer requires inserting redundant duplicates into materialized views nor updates of redundant total figures. Beforehand, especially the concurrent aggregate updates could lead to contention as materialized aggregates had to be locked for updating. In case of, for example, heavily used G/L accounts, the database system had to handle the otherwise parallel postings sequentially. In Simple Finance, posting now only requires inserting the actual document and its line items. Experimental measurements based on customer data (see Figure 3) have shown that the end-to-end transaction time required for posting 500 documents with 6 line items each has been reduced by a factor of more than 2, doubling the throughput. The number of tuples affected by the database operations for posting the documents has been reduced by a factor of almost 2.5 thanks to the no longer required synchronization of redundant data.


Figure 3: Increased transactional throughput

Benefits of SAP Simple Finance in Summary

The data model of SAP Simple Finance is now exclusively consisting of line items. It is “simply” recording all business transactions as they happen. Everything else is being calculated on-the-fly by algorithms on the data. This implies that the previously needed modifying operations to maintain the materialized views are no longer necessary, thereby simplifying the program code and increasing the transactional throughput.


The fact that it is feasible to remove the redundancy from the Financials data model demonstrates that in-memory database systems enable new levels of flexibility. Instead of pre-defining materialized views and materialized aggregates, it is possible to aggregate flexibly directly on the line items. Instead of being restricted to specific analysis questions supported by the underlying model of materialization, all questions are feasible that can be answered based on the actual items themselves via any aggregation on top of them. An improved and user-friendly user experience adds to that and allows unprecedented user productivity and novel insights.


This first blog post of the Simple Finance deep dive series looked at the non-disruptive changes to the data model. In particular, we outlined the practical benefits associated with removing redundancy of materialized views and materialized aggregates; increased throughput and reduced database footprint. In the upcoming blog posts of the series, we will demonstrate how SAP HANA as an in-memory database makes these changes feasible using performance calculations and technical considerations.

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