Big Data
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Schweighofer (2013) further replies to the question of how to integrate external and
internal data in an efficient way, as follows: What makes the handling of IP-related
data efficient though, is the combination of own IP-data with additional business
data (finances, project data, development data, market reports, or feedback from
customers), as well as external data (trends, information about competitors, data
from authorities, and so on). In this context it is important to have the ability to derive
relevant information for the own IP-strategy from a pool of Big Data. This can have
an impact on the whole business strategy for technology-based companies: Which
areas should be developed further? In which areas should protective right be kept
up, or dropped?
5.1
Data Warehouse in the Era of Cloud
For Leo Faltus (2013), working at Software One, a company that deals with licensing
models for Microsoft programs, Big Data is a term, which is derived from the world
of databases and is closely related to Data warehousing. These data warehouses
process a huge amount of data, which can be used to discover new insights. Faltus
further notes, that in the era of cloud computing, it is now possible to gain even more
insights from even bigger amounts of data. This is made possible through affordable
computing power provided by cloud services. In fact knowledge acquisition is a
process, which happens when required, on-demand.
Faltus regards Big Data as a next milestone in the context of knowledge acquisition
through huge amount of data. The great advantage is that it is no longer necessary
to operate huge sever farms on-premise, but to buy capacity, when required (Faltus,
2013).
5.2
Finding the Needle in a Haystack
The challenge for companies is not only to manage Big Data, but also to derive
useful information for business purposes. A case study conducted at McKinsey in
June 2011 put a figure on Big Data:
