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Chapter 8
industries, such as financial services, manufacturing, consumer products, retail, and
utilities or to functional areas, such as supply chain management, finance, human
resources, IT, and service, sales, and marketing. SAP BI includes a set of tools for
exploration, analysis, and presentation that can be applied to a wide range of business
questions. Enterprise performance management is the concept of developing strategic
goals for the organization and then gathering data to evaluate how the organization is
performing in relation to those goals. The governance, risk, and compliance category
represents a group of activities focused on ensuring an organization is functioning
ethically and legally. Governance refers to the processes that ensure that top management
is receiving accurate and timely data necessary to run the organization and that control
mechanisms are in place to make sure that management directions and instructions are
220 being carried out. Risk, or risk management, consists of processes to identify risks to the
organization (technological, financial, information security, supply chain disruptions, and
so on) and to develop plans to minimize the potential damage to these risks. Compliance
means conforming to stated requirements, which could be customer specifications for
goods or services, Sarbanes-Oxley reporting requirements (discussed in Chapter 5), or
state and federal regulations such as those relating to product safety. Data warehousing is
the technology used to store the large volumes of data used in the analysis. Enterprise
information management is a relatively new term that describes the business and
technology functions that manage information as a corporate asset.
The final section of the diagram in Figure 8-1 is titled “Access,” and it describes how
users will access BI. Previously, this was primarily through personal computers, but with
the growth in mobile technology, an increasing variety of devices can now be used to
access BI.

IN-MEMORY COMPUTING

Up until recently, BI systems have typically accessed data that was stored on data
warehouse servers, which use hard disk technology to store data. Data stored in
data warehouse servers usually consist of aggregated data—from an ERP system or other
sources. To speed access to this data, the data in a data warehouse are structured as
multidimensional data cubes, which allow for relationships in the data to be analyzed
quickly. For example, a company might want to look at sales over time, by store, and by
product. This means analyzing sales along three dimensions, and the sales data would
need to be structured as a three dimensional cube. If the company was also interested in
looking at these sales by geographic region, it would need a four dimensional data cube.
There are two main challenges with using a multidimensional cube structure. First,
a significant level of technical expertise is needed to construct a cube, which means that
an IT specialist rather than an end user usually must complete this task. In practice,
the end user and an IT specialist usually have to collaborate to construct the correct
multidimensional data cube, and frequently they do not speak the same language—that is,
the end user thinks in terms of business problems while the IT specialist thinks in terms
of database technology. Second, the multidimensional cube is a structure that necessarily
restricts how the data can be analyzed.
In Chapter 2, we introduced the topic of SAP’s HANA in-memory computing
technology. HANA allows customers to analyze large amounts of data instantly. The key to
this technology is that data is stored in computer memory rather than on hard disk servers.

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