Consumers will increasingly expect retailers to offer highly customized buying recommendations at the right time through the right device. Being able to follow these through with seamless and secure e-commerce transactions. The potential of Data blending in every area from automotive telemetry to medical science to national security is enormous.

1. Trends in Big Data

3. 2010
Just getting started with Hadoop
2015
The Hadoop ecosystem dominated big data market.
We focused on ‘putting big data to work’ through use cases shown to generate ROI from increased
revenue and productivity and lower risk.
2016
Big data continues beyond this ‘domain’ and more mainstream companies adopting Big Data and
Internet of Things (IoT) with traditionally conservative and skeptic organizations starting to take the
plunge.
Big data will be put to work and blending of data will be bigger and more important Increasingly sophisticated
demands means the pressure to innovate here will remain high in 2016.
2025
IoT will create anywhere from $3.9 trillion to $11.1 trillion in economic value.
more about these six emerging big data trends.

4. 2016, the year Big Data will be put to Work
Consumers will increasingly expect retailers to offer highly customized buying recommendations at the
right time through the right device.
Being able to follow these through with seamless and secure e-commerce transactions.
The potential of Data blending in every area from automotive telemetry to medical science to national
security is enormous.

5. Internet of Things Is Getting Real!
Predictive Maintenance, Smart Cities and Smart Homes. Large industrial companies that make,
move, sell and support physical things are plugging sensors attached to their ‘things’ into the internet.
A myriad of new challenges and opportunities for instance in the areas of data governance,
standards, health and safety, security and supply chain.
Companies must start planning now or risk being left behind.

6. Embed and Deliver Analytics at the Point of
Impact
Analytics is the top strategic enabler to grow the company’s revenue and productivity and cut costs.
The next generation of analytics is where business users consume analytics through familiar, day-to-
day business applications will be mission-critical.
The classic BI model of a data analyst using a tool outside the application flow to analyze historical data
will become somewhat helpful, but obsolete.
Analytics

7. High Volume Data Integrators enables tools
to be ready
Spark, Docker, Kafka, Lucene, Solr and other emerging open source tools designed to enable large-
scale, high-volume analytics on petabytes of data are moving from the explorative to the applicable
phase.
These tools are critical to driving faster innovation and are becoming more mature as we speak.
Being in the lead with open source innovation and being able to incorporating these and other exciting
technologies into platforms while ensuring these technologies are hardened for use by customers.
Integration platforms are the future.

8. Cloud Is Emerging as
a Preferred Deployment Model for Big Data
Many have either deployed their big data applications in the cloud, or are planning to do so.
This includes businesses operating in highly secure environment, handling and looking for anomalies in
billions of transactions every day.
Companies want the option to deploy big data applications either behind the firewall, 100 percent in the cloud
or a hybrid, private/ public cloud environment.
Use of f.inst. Amazon Web Service (AWS) capability supporting customers as they look to the optimize
deployment models across workloads and user cases.

9. Cognitive Technology enables better
User Experience
Main focus in the big data analytics ecosystem will be on User experience.
This goes beyond cloud, being able to build better user experiences for streaming and predictive analytics.
Integrating new, secure technologies such as facial recognition to reassure users that their data is safe and
trustworthy. Real-time speed is also a key part of the user experience. Existing distributed Big Data
processing platforms are still too hard, and vendors need to invest in making the experience easier.
The easier we all make it, the more use cases we unlock for the companies and their business cases.

10. • Data Integration and Business Analytics
• Accelerate the Time to Big Data Value
• Completely embeddable and deliver governed data to power any analytics in any environment
• Harness the value from all company data, including big data and IoT, enable companies to find new
revenue streams
• Operate more efficiently, deliver outstanding service and minimize risk
• Create a better User Experience

11. Stig-Arne Kristoffersen is a Corporate exec with substantial corporate experience.
Stig-Arne provide preemptive support in German or English, with basic skill set in Russian.
Kristoffersen focus on Knowledge Based Information processes and systems within various
industries, contract drafting, asset negotiations within real estate and energy sectors.
Stig Arne has a broad experience in all aspects of Geo-science. He has direct experience
with energy business, technical consulting and venture capital.
Stig has extensive experience in play development and prospect generation in various basins globally.
Stig Arne has performed a large variation of risk assessment as part of prospect maturation with Hi-end tools from
various vendors including Tibco, SAS, Hadoop, Schlumberger, Landmark and SMT.
Stig Arne has participated in multiple projects with efficient Exploration and Production of oil and gas resources,
and experience in making quick turnaround from resource to reserves. Utilizing acceptable international renown
techniques to achieve the goal of the projects.
Stig Arne Kristoffersen has experience in farm-in and -out negotiations, asset management, strategy decisions
within oil and gas as well as information technology matters. Hands on experience in asset evaluation as well as
exploration strategy and portfolio management.
AUTHOR