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Industry examples

Life sciences: business objectives with broader benefits

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This paper addresses these issues by looking at five key industry sectors where companies’ exploitation of big data is at different stages of development.
 

Life sciences: companies are still at an early stage in their efforts to generate actionable insights from data.

Maturity level
Good data storage and collection are just beginning to generate insights.

Holistic approach
Now improving, with greater use of enterprisewide strategies.

Business benefits
Early stage, but potential for gains from new revenue streams.

Tougher rules requiring pharmaceutical firms to track and respond to any report of an adverse drug reaction will demand more joined-up information systems. Companies that are able to successfully implement data sharing will gain a commercial advantage and limit their legal risks.

One exciting project is EUResist, in which data scientists use analytics tools to segment patient populations at an increasingly granular level. Such an approach has helped scientists to make huge advances in treating HIV.


Consumer products: focusing on risk and return

Maturity level
Good in sales and marketing areas; functions such as supply chain, finance and HR are now catching up.

Holistic approach
Benefiting from shift toward multifunction business services.

Business benefits
Being realized in both sales and marketing, and operations.

Consumer goods companies are often at the forefront of exploiting big data opportunities in both consumer and customer-facing areas. But there are clear challenges too, like the diversity of the marketplaces in which companies operate.

Several consumer product organizations have created centers of excellence to drive the take-up of analytics opportunities across their business.
Another key challenge is to convert the insights generated at the center into actions in each area of the business around the world.
 


Financial services: emerging from the credit crisis

Maturity level
Good in operations and risk; early adoption of big data into customer and growth agenda.

Holistic approach
Good, but the struggle to unite disparate systems continues.

Business benefits
New monetization opportunities are already being explored, legal and regulatory risks are now being mitigated.

Regulatory stress has forced many businesses to invest in areas such as risk management, compliance and operations. That has accelerated a trend toward enterprise data management.

The financial services sector is largely mature in terms of big data and analytics. The focus is now increasingly on utilizing these capabilities to drive new sources of revenue.

Reputational risk is an important issue. Financial services companies are extremely cautious in areas such as data privacy and accessibility, where they fear the risk of further deterioration in client relationships.
 


Power and utilities: data explosion promises infrastructure gains

Maturity level
Slow to adopt new technologies, but potential now being seen.

Holistic approach
Hampered by legacy IT issues and the structure of many companies.

Business benefits
There are exciting asset and infrastructure management opportunities.
 

Trends in the power and utilities sector are leading to the emergence of vastly bigger data volumes along the whole value chain. The level of maturity of big data technology adoption varies among the stages of the value chain.

The steering of generation plant utilization as well as the energy trading activities already depend on elaborate production and market data models. But the full potential of big data in infrastructure operations and retail is still to be uncovered.

Smart meters and smart devices are an important new source of data that enables utilities companies to build much closer relationships with customers and to move into new markets.


Automotive: driving more enterprise-wide benefits

Maturity level
Low in customer-facing areas — functions such as supply chain and engineering are starting to catch up.

Holistic approach
Difficult, given complicated supply chains, but some progress is being made.

Business benefits
Experiments are taking place with new revenue streams.

Some of the most interesting developments are to be found in engineering and other technical areas. For example, the use of analytics to identify problematic components is dramatically improving production quality and is generating substantial cost savings.
One key issue is the complexity of the supply chain in the industry. Like other industries, automotive is struggling to see the big picture on big data and analytics.

Some companies are also beginning to think about monetizing their data on previous owners and mileage to customers. The “connected vehicle” phenomenon will only add to such opportunities.