In-memory and flash-based technologies could mark an inflection point for the enterprise applications market
In contrast to the other two topics that we discussed, we believe that in-memory and flash- based technologies could be an inflection point for the enterprise applications market. The roots for the enterprise applications market date back to the 1950s with the evolution of the first MRP (Material Requirements Planning) systems as a precursor to the first ERP (Enterprise Resource Planning) systems. Broader market adoption of ERP systems started in the 1970s and 1980s with mainframes as the infrastructure and the client-server infrastructure lead to the mass market evolution in the 1990s and 2000s (see our report SAP – winner of
the shakeout process, 2 June 2003, page 17f. for further details).
What was the idea and limitation of the existing (extended) ERP and business intelligence systems
Traditional ERP software focuses on automating back-office processes, generating transactional data based on events (e.g. an order). The major idea of the ERP software is rationalization, leading to a cost advantage. However, traditional ERP systems were not able to deliver a competitive advantage based on real-time insight into the operating business. Given a widespread number of different ERP systems installed at an average customer, a widespread number of different "data silos" emerged from all the different OLTP (Online Transaction Processing) systems carrying out day-to-day business functions such as ERP, CRM or SCM. Although business intelligence solutions emerged in the early 1990s and enabled enterprises to systematically analyze data often generated by an ERP system, queries as a rule took some time (e.g. overnight batch processing) and were often based on outdated data (stemming often from daily or monthly batch loads). ETL (Extract, Transform, Load) mechanisms brought the data into data warehouses where e.g. OLAP systems (Online Analytical Processing) performed queries on the data, resulting in reports with the required information. The reason for this constraint is that traditional business intelligence platforms
TRADITIONAL BUSINESS INTELLIGENCE ARCHITECTURE
OLAP Multidimensional calculation, Information modeling, Presentation Standard reporting, Ad hoc analysis,
Data mining
Operating systems OLTP systems, ERP systems, External sources
ETL Extract, Transform, Load
Data Warehouse AdministrationData storage,
OLAP Multidimensional calculation, Information modeling, Presentation Standard reporting, Ad hoc analysis,
Data mining
Operating systems OLTP systems, ERP systems, External sources
ETL Extract, Transform, Load
Data Warehouse AdministrationData storage,
Source: BARC, UniCredit Research
What has changed and why could in-memory technologies be an inflection point for enterprise apps today?
In-memory databases are not a new idea. They date back to the 1980s. In contrast to traditional (relational) database management systems that retrieve query data from slow hard disks, in-memory-based systems use the server's main memory as primary storage (see e.g. Hasso Plattner and Alexander Zeier In-memory data management – an inflection point for
enterprise applications, for a more in depth analysis of the topic. We took this book as a basis
for our descriptions). However, several technical limitations kept the idea at bay: 1. relatively high DRAM costs, 2. 32-bit systems with an addressable memory limit of 4 gigabytes and
3. single-core CPUs (Central Processing Units). However, technological improvement has led
to the abolition of these limitations.
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re. 1: In sync with other storage prices, DRAM prices per megabit dropped from almostUSD 82,000 in 1Q74 to about USD 0.032 at the end of 2007 and have kept falling since. The price for 1MB disk space dropped from more than USD 250 in 1970 to below USD 0.01 in 2001.
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re. 2: 32-bit architectures were the dominating format in the PC segment until the first halfof the last decade. However, 64-bit architectures that had already been around since the 1970s, started to supersede 32-bit architectures at the end of the first half of the last decade. In contrast to an addressable memory limit of 4 gigabytes of the 32-bit architectures, 64-bit systems have a theoretical limit of 16 exabytes (16bn gigabytes) of RAM. This, in combination with larger memory capacity on servers, substantially increased the data volume that can be stored in dynamic random access memory.
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re. 3: Single-core processors were the dominating format of processors in both the homeand business computer segment until 2005. However, multi-core processors replaced single-core processors as dominating format in 2H06 as multi-core architectures overcame the limitation of the stagnation of clock speed per core.
Summary: in-memory will result in a new breed of applications supporting tactical decision making
The above-mentioned technological progress on the hardware side, in combination with the advantages of columnar databases that allow a high degree of data compression paired with their favorable indexing and parallelized query attitudes, explains, in our view, why there could be an inflection point of enterprise apps today. In summary it is worth highlighting that it has become possible through this technological progress to store data sets of whole companies entirely in main memory, thus enabling real-time insight into the operating business of a company. This trend is likely to result in a new breed of applications supporting tactical decision making.
A word about flash-
based technologies In contrast to in-memory based solutions, flash-based technologies (e.g. NAND) do not imply
re-written applications, but rather enhance the speed of existing database solutions without any change to the existing code. While competing with in-memory based solutions at first glance, we believe the concept is a different one, because in-memory will result in a new breed of applications that were technologically not possible before. Still, the revenue impact of flash-based solutions for the vendors (e.g. Oracle with its Exaseries) is likely to have a greater short-term impact on revenues than in-memory, which we see as more of a transformational technology than flash.
Penetration with BI solutions within organizations is still relatively low
Estimates of market research firms (see, e.g. Gartner Group's publication from 8 February 2008,
Emerging technologies will drive self-service business intelligence) suggest that no more than
20% of users in most organizations use reporting, ad hoc querying and online analytical processing (OLAP) tools on a regular basis. Back then, Gartner Group expected that emerging technologies (like e.g. in-memory analytics) would help reach the 80% of users that weren't using analytical applications. Recently, Gartner revised its assumption from 2008 in a new report from 3 June 2011 (the consumerization of BI drives greater adoption). The company now estimates that only 28% of the potential users are using standard BI tools. This still implies a potential of 70% as of today.
What is the potential
market opportunity? However, the market volume (defined as new licenses, updates, subscriptions and hosting,
technical support, and maintenance) was USD 10.5bn in 2010. Assuming that 28% of the potential users were in fact using BI solutions in 2010, the theoretical market volume (assuming 100% market adoption) would have been USD 37.6bn in 2010, indicating that there is still a substantial market opportunity left, if the new applications including in-memory analytics and interactive visualization lead to a stronger market penetration. Based on Gartner's market share analysis, SAP had a market share of 22.9% in 2010, followed by Oracle with 15.6%, SAS with 13.2% and IBM with 11.6%. Our scenario analysis shows the resulting revenue potential for the cited players, based on the assumption of a stable market share and an increasing adoption of partly new BI solutions within enterprises.
IN-MEMORY WILL AMPLIFY THE BUSINESS INTELLIGENCE MARKET VOLUME (USD BN) User penetration
(%) (based on 2010 figures)Market volume with stable market share Revenue potential
28 10.5 SAP: 2.4; ORCL: 1.6; SAS:1.4; IBM: 1.2 40 15.0 SAP: 3.4; ORCL: 2.4; SAS:2.0; IBM: 1.7 60 22.5 SAP: 5.2; ORCL: 3.5; SAS:3.0; IBM: 2.6 80 30.1 SAP: 6.9; ORCL: 4.7; SAS:4.0; IBM: 3.5 100 37.6 SAP: 8.6; ORCL: 5.9; SAS:5.0; IBM: 4.4 Source: Gartner Group, UniCredit Research
In-memory technology is often used in analytics, but also in other products
In-memory analytics are the base of the BI platforms of several market leading vendors like SAP (market share in 2010: 23%), IBM (market share: 12%), MicroStrategy (market share: 3%), QlikTech (market share: 2%) and TIBCO (market share: 1%). Many larger vendors helped build their in-memory product portfolio by acquisitions: Oracle acquired TimesTen in 2005, TIBCO acquired Spotfire in 2007, IBM acquired Cognos and Solid Information Technology in 2008, SAP acquired Sybase in 2010 and Software AG acquired Terracotta in 2011. While the majority of the vendors focus their in-memory approaches mainly on analytics, SAP applied a broader approach including transactional and analytical processing. In addition, database vendors (incl. Oracle, IBM and SAP) also offer in-memory databases.
AN (INCOMPLETE) OVERVIEW OF PRODUCTS BASED ON IN-MEMORY TECHNOLOGY
Company Product(s) based on in-memory technology Description
IBM Cognos Express BI and planning for midsize companies; the IBM Cognos Express Advisor includes an in-memory analytics server
solidDB In-memory relational database technology
Microsoft SQL Server PowerPivot Self-service business intelligence tool available for Excel 2010 and SharePoint
MicroStrategy MicroStrategy OLAP Services In-memory technology delivered via an enhanced Intelligent Cube, a dynamic in-memory cache
Oracle TimesTen In-memory Database Memory-optimized relational database In-memory analytics accelerator Appliance to be released at Oracle OpenWorld in fall 2011 QlikTech QlikView Business Discovery Platform Light-footprint, in-memory-based visual tools
SAP Business ByDesign The analytical functions of BBD are based on in-memory technology BW Accelerator Appliance to speed up OLAP queries
HANA Appliance that runs the SAP in-memory DBMS (ICE) Sybase ASE in-memory databases In-memory database technology as an option SAS High-Performance Analytics Analytics based on in-memory technology
(general availability targeted for 4Q11)
Software AG Terracotta Enterprise Suite In-memory data grid (distributed caching platform) TIBCO Spotfire In-memory analytics platform
ActiveSpaces Suite Distributed peer-to-peer in-memory data grid