We are on the cusp of an information driven corporate finance reformation. That is an overhaul and restructuring of how financial information is placed “in context” with operational and machine generated information to allow the business user to: monetize operations in terms of units, rates, drivers and sensitivities; analyze and understand the financial and operational impact of decisions in real time; and convert insight into action. This also elevates Finance’s role as a key partner/resource for monetizing operational decisions and strategies.
This is the first of a blog series that will explore the corporate finance reformation, including what has changed to make this possible, what it will require from finance departments and information technology and some of the key benefits.
In the past, different innovations have come close to resulting in a corporate finance reformation but have fallen short. Online Analytics Processing [OLAP] tools with their cached results and intuitive modeling techniques were one approach. While they supported sophisticated models for analyzing “What If”, their data was too high level and not connected to the detail transactions where questions are answered, anomalies identified and action plans defined. Techniques used to link OLAP databases to the detail relational transactions proved fragile.
A second approach was to use relational accelerators to speed up relational query time and create a line of sight between summary totals and detail level transactions. The challenge with these solutions were twofold: first the speed still wasn’t fast enough; and complex data architecture became a challenge for the business user to navigate. Traversing data with inner and outer joins connecting 10 or 20 different tables to create a business view required a combination of IT and business acumen.
Both of these approaches along with the typical relational database/data warehouse approach suffer from data inflexibility. It required data/information to be architected in anticipation of the way business users wanted to look at the information. If business users queried the data the way it was anticipated, the queries were fast. If the business user stepped outside the way the data was architected, the query was slow.
Why is it different now?
This reformation is fueled by in-memory technology and the data simplification that in-memory technology provides. In-memory technology:
- Provides the ability to organize data in columns and effectively makes each field (column) act as an index. It thereby removes the need for data architecture choreographed the way we want to query the data.
- Enhances the speed of analysis. Eliminating speed as barrier to analysis enables simplification of data architecture.
- Allows us to write transactions to the same detail table we do the analysis on and eliminates aggregated tables.
- Eliminates the need to anticipate the questions being asked by the business user and provides a more “flexible and agile” corporate climate.
The results of in-memory technology is a system architected without aggregate tables and without separate transaction tables that have to be maintained, reconciled and linked when creating reports or performing analysis. This concept is the core of S/4HANA Finance.
The Universal Journal table, provides a single source of truth in accounting and controlling where:
- All financial transactions are written including transactions from accounts payable, accounts receivable, general journal, material ledger, asset accounting and COPA sub ledgers.
- All reports are based, from high level financial statements to detail level accounts payable summaries and daily check log to apply against accounts receivable transactions.
- Transactions can be tracked from original purchase order of raw materials to final sale of finished goods.
Why is it important for companies?
The real-time environment and the ever accelerating corporate “clock speed” demand an organizational capacity to respond in ever-shorter time frames with information and insight. S/4HANA Finance provides a business platform that incorporates a powerful predictive engine and facilitates decision making in the accelerated time frame. It helps organization’s create and capture new economic value by merging financial transactions with their operational attributes to create integrated planning. It helps to focus the company on the financial implications of operational decisions and provides the missing link for supporting data driven insights rather than basing decisions on assumptions or intuition.
My next blog will dig deeper into “Why is it different now?” and focus on key features of “in-memory” technology that make this reformation possible.
 See the Harvard Business Reivew Article: “The Future and How To Survive It” October 2015, by Richard Dobbs, Tim Koller and Sree Ramaswamy