The BIG Project examined how big data technologies can enable business innovation
and transformation within different sectors by gathering big data requirements from vertical industrial sectors, including health
, and transport
. There are a number of challenges
that need to be addressed before big data-driven innovation
is generally adopted. Big data can only succeed in driving innovation if a business puts a well-defined data strategy
in place before it starts collecting and processing information. Obviously, investment in technology requires a strategy to use it according to commercial expectations; otherwise, it is better to keep current systems and procedures. Organizations within many sectors are now beginning to take the time to understand where this strategy should take them.
The full results of this analysis are available in Zillner et al. (2014). Part III of this book provides a concise summary of the key findings from a selected number of sectors. The remainder of this chapter provides an executive summary of the findings from each sector together with discussion and analysis.
Investigation of the healthcare sector in Chap. 10 revealed several developments, such as escalating healthcare costs
, increased need for healthcare coverage, and shifts in provider reimbursement trends, which have triggered the demand for big data technology. In the sector the availability and access of health data is continuously improving, the required big data technology (such as advanced data integration
technologies) are in place, and first-mover best-practice applications have demonstrated the potential of big data technology. However, the big data revolution in the healthcare domain is in a very early stage with the most potential for value
creation and business development unclaimed as well as unexplored. Current roadblocks to big data-driven innovation are the established system incentives of the healthcare system that hinders collaboration and, thus, data sharing
and exchange. The trend towards value-based healthcare delivery will foster the collaboration of stakeholders
to enhance the value of the patient’s treatment, and thus will significantly foster the need for big data applications.
The investigation of the public sector
in Chap. 11 showed that the sector is facing some important challenges—the lack of productivity
compared to other sectors, budgetary constraints, and other structural problems due to the aging population that will lead to an increasing demand for medical and social services, together with the foreseen lack of a young workforce in the future.
The public sector is increasingly aware of the potential value to be gained from big data-driven innovation via improvements in effectiveness
and with new analytical tools. Governments generate and collect vast quantities of data through their everyday activities, such as managing pensions and allowance payments, tax collection, etc. The main requirements, mostly non-technical, from the public sector are:
: An obstacle to exploit data assets due to the fragmentation of data ownership and the resulting data silos.
support and political willingness: The process of creating new legislation is often too slow to keep up with fast-moving technologies and business opportunities.
Privacy and security issues
: The aggregation of data across administrative boundaries in a non-request-based manner is a real challenge.
Big data skills
: Besides technical people, there is a lack of knowledge regarding the potential of big data in business-oriented people.
Finance and Insurance
As covered in Chap. 12 the finance and insurance
sector is the clearest example of a data-driven
industry. Big data represents a unique opportunity for most banking and financial services organizations to leverage their customer data
to transform their business, realize new revenue
opportunities, manage risk, and address customer loyalty
. However, similarly to other emerging technologies, big data inevitably creates new challenges and data disruption
for an industry already faced with governance, security, and regulatory requirements, as well as demands from the increasingly privacy-aware
At this moment not all finance companies are prepared to embrace big data, legacy information infrastructure, and organizational factors
being the most significant barriers
for its wide adoption in the sector. The deployment of big data solutions must be aligned with business objectives for a successful adoption of the technology to return the maximum business value
Energy and Transport
Chapter 13 examines the sectors of energy and transport
which from an infrastructure perspective, as well as from resource efficiency
and quality of life perspectives, are very important for Europe. The high quality of the physical infrastructure and global competitiveness
of the stakeholders
needs to be maintained with respect to the digital transformation
and big data-driven innovation
The analysis of the available data sources in energy as well as their use cases in the different categories for big data value
: operational efficiency
, customer experience
, and new business models
make it clear that a mere utilization of existing big data technologies as employed by the online data businesses will not be sufficient. Domain- and device-specific adaptations are necessary for use in the cyber-physical systems
of oil, gas, electrical, and transport. Innovation regarding privacy and confidentiality preserving data management and analysis is a primary concern of all energy and transport stakeholders that are dealing with customer data, be it business-to-consumer or business-to-business. Without satisfying the need for privacy and confidentiality
, there will always be uncertainty around regulation and customer
acceptance of new data-driven offering.
The increasing intelligence embedded in the infrastructures will enable the “in-field
” analysis of the data to deliver “smart data
”. This seems to be necessary, since the analytics
involved will require much more elaborate algorithms than for other sectors such as retail. Additionally, the stakes are very high since the optimization opportunities are within critical infrastructures
Media and Entertainment
The media and entertainment
industries have frequently been at the forefront of adopting new technologies. Chapter 14 details the key business problems that are driving media companies to look at big data-driven innovation as a way to reduce the costs
of operating in an increasingly competitive landscape, and at the same time, the need to increase revenue from delivering content. It is no longer sufficient to publish a newspaper or broadcast a television programme—contemporary operators must drive value
from their assets at every stage of the data lifecycle
Media players are also more connected with their customers and competitors than ever before—thanks to the impact of disintermediation, content can be generated, shared, curated, and republished by literally anyone. This means that the ability of big data technologies to ingest and process many different data sources, and if required even in real-time, is a valuable asset companies are prepared to invest in.
As with the telecom industry, the legal and regulatory
aspects of operating within Europe cannot be disregarded. As one example, it is critical that just because it is technically possible to accumulate vast amounts of detail about customers from their service usage, call centre interactions, social media updates, and so on, it does not mean that it is ethical to do so without being transparent about how the data will be used. Europe has much stronger data protection rules than the United States, meaning that individual privacy and global competitiveness will need to be balanced.
The telecom sector
seems to be convinced of the potential of big data technologies. The combination of benefits within marketing
and offer management, customer relationship
, service deployment, and operations can be summarized as the achievement of the operational excellence for telecom players.
There are a number of emerging big data telecom-specific commercial platforms available in the market that provide dashboards, reports to assist decision-making processes, and can be integrated with business support systems (BSS)
. Automatic actuation on the network as a result of the analysis is yet to come. Besides these platforms, Data as a Service (DaaS)
is a trend some operators are following, which consists of providing companies and public sector organizations with analytical insights
that enable third parties to become more effective.
Another very important factor within the sector is related to policy. The Connected Continent framework, aimed at benefiting customers and fostering the creation of the required infrastructure for Europe to become a connected community, at first sight, will most probably result in more strict regulations
for telco players. A clear and stable framework is very important to foster investment in technology, including big data solutions.
The retail sector
will be dependent on the collection of in-store data
, product data
, and customer data
. To be successful in the future, retailers must have the ability to extract the right information out of huge data collections acquired in instrumented retail environments in real time
. Existing business intelligence
for retail analytics
must be reorganized to understand customer behaviour
and to be able to build more context-sensitive, consumer- and task-oriented recommendation tools for retailer-consumer dialog marketing.
The core requirements in the manufacturing sector
are the customization of products and production—“lot size one”—the integration of production in the larger product value chain
, and the development of smart products
The manufacturing industry is undergoing radical changes with the introduction of IT technology on a large scale. The developments under “Industry 4.0
” include a growing number of sensors
and connectivity in all aspects of the production process. Thus, data acquisition
is concerned with making the already available data manageable, i.e., standardization
and data integration
are the biggest requirements. Data analysis
is already applied in intra-mural applications and will be required for more integrated applications that cover complete logistics chains
across factories in the production chain and even into the post-sale usage of (smart) products. Production planning needs to be supported by data-based simulation
of these complete environments.
Complex and smart machinery
, e.g., airplane engines, can benefit from big data-based predictive maintenance
where sensor and context information is used with machine learning algorithms to avoid unnecessary maintenance and to schedule protective repairs when failures are predicted. Given the additional infrastructure costs, manufacturers are using new business models
where machinery is leased and not sold; and in turn sensor data and services are owned and executed by the manufacturer and not the user of machinery. This leads to challenges in regulations
and contracts concerning data ownership.
The European manufacturing sector can be both a market leader using big data in the context of Industry 4.0, and a leading market, where manufacturing big data is integrated in the larger product value chain and smart products can be put to use.