1. bigData_wcloudNEWThe year of predictive analytics

Mark Darbyshire, Chief Technologist at SAP UKI: “With the dawn of the ‘zettabyte era’, and the world churning out more than a trillion gigabytes of data, 2016 will see businesses looking to predictive analytics to uncover trends and patterns and gain unprecedented insight into customers, businesses and markets.

“This will allow them to go beyond reaction, to anticipating and shaping better business outcomes. Insurance company Aviva, for example, is using predictive analytics to target the right customers, from their 31 million-strong customer base, with the right offers at the right time.”

  1. Cognitive machines

Chetan Dube, CEO, IPsoft: “I believe a tectonic shift in the relationship between man and machine is imminent. As the intelligence of cognitive systems matures it will carry humans to higher planes of creative thinking.

“These cognitive machines are going to redefine the business landscape. In fact, I wouldn’t be surprised if within the next 10 years, you will walk down the corridor past a co-worker and not know if they are human or machine.

  1. A focus on value

Gareth Martin, Analytics Portfolio Lead EMEA, HPE: “Organisations are assessing the success of their Big Data programs and realise that without a specific focus on the value from analytic solutions, they don’t tend to break even as quickly as planned, or at all.

“Rather than build a data lake, companies are moving toward building analytic labs to focus on value-based solutioning.”

  1. Overcoming the limits of legacy systems

Bob Wiederhold, Couchbase CEO: “In 2016, we’ll see more enterprises re-platform their data management systems using NoSQL to overcome the limits of their 30-year old legacy relational systems.

“Mobile applications must be fast, responsive and reliable to meet end users demanding expectations. To meet these demands, mobile developers are no longer building applications for the best case performance scenario, they are building apps for the worst case scenario – guaranteeing an ideal mobile experience regardless if connectivity is ideal or spotty.

  1. Salaries to skyrocket

Mike Maciag, COO, Altiscale: “Salaries for both data scientists and Hadoop admins will skyrocket in 2016 as growth in Hadoop demand exceeds the growthof the talent pool.

“In order to bypass the need to hire more data scientists and Hadoop admins from a highly competitive field, organizations will choose fully managed cloud services with built-in operational support.

“This frees up existing data science teams to focus their talents on analysis instead of spending valuable time wrangling complex Hadoop clusters.”

  1. Skill shortages to persist

Dan Graham, GM of Enterprise Systems at Teradata: “Shortages of analytics experts will persist, expanding from data scientists to power users, architects, and data management experts.

“Loss of subject matter experts and their knowledge coupled with scarce replacements will force corporations to apply knowledge management techniques to analytics staff.

“Business Intelligence tools will compete by including collaborative features for the capture, reuse, vetting, and tagging of tribal knowledge to fill gaps.”

  1. The age of the algorithm

Prof.Dr. Michael Feindt, Founder, Blue Yonder: “Everyone has big data now, but raw data on its own provides no value. The value comes from what you do with that data and what this means for 2016 is that we will enter the age of the algorithm.

“Only by applying algorithms will people find transformative value from their data. Algorithms help organisations put their data to work, providing predictive analytics and automated decisions. Data visualization tools are useful in analyzing large sets of data using graphs and dashboards thereby creating reports helpful for taking business decisions.

“Algorithms create action; without action you achieve very little. With the Internet of Things taking off; smart phones; driverless cars; connected devices, comes more and more data. This data requires algorithms to make sense of it; to create operational efficiencies; to predict outcomes and make decisions based on this; to differentiate your brand; to stay ahead of the competition.

“Also progress in medicine could be much faster if data were exploited better.”

  1. Recognising customers

Andy Lawson, MD and SVP at Salesforce UK: “2016 will be the year of 1-to-1 customer engagement. With the number of connected devices soon to reach 75 billion, businesses need to recognise that behind each of these devices is a customer, and the opportunity to connect with them has never been greater.

“Businesses will start to use the huge amounts of data generated by connected devices to discover insights that can be used to engage with customers in an entirely new way. They will be able to track, respond and even anticipate their customer’s needs, creating a personalised 1-to-1 customer experience. I believe this new approach to business intelligence will enable organisations to transform their relationships with customers.”

  1. Big data becomes omnipresent through apps

Michael Benedict, Chief Product Officer, Progress: “This first wave of Big Data focused on the infrastructure stack-storage, scale and integration. It’s the next wave of technology that I’m most excited about, because it will make Big Data mainstream and consumable by everyone.

“Companies will stop thinking about Big Data as a big data warehouse to be managed and scaled. Instead, they’ll think about the marketing analytics application that automatically provides the next best piece of content to users and drives higher conversion levels. True Big Data value will emerge from this next wave of applications and services. These are the ISVs to watch.”

  1. What will hurt big data?

Gareth Martin, Analytics Portfolio Lead EMEA, HPE: “IT-centric projects resulting in dead-end technology platforms and rogue data pools will reduce market appetite for ‘big data’ projects. Immature software and inadequately skilled resources that do not meet inflated expectations have similar results.

“Underestimating the step from discovery type lab projects to operationalize both platforms and SLA bound operations, integrating with existing technology and processes, will lead to delays and adding rescue cost and time.” source