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imagen: Data Science Central

Lo mejor es que empecemos con una revisión de las tendencias que varios expertos identifican en el sector Data Science para este año 2016. Esta información la hemos recopilado del blog BAM! Business Analytics Management, Data Science Central e import.io Allá vamos:

Nuesto hiper-resumen: este año crecerá la analítica en tiempo real y los algoritmos machine learning, el SaaS se afianza en el sector como medio para llegar a usuarios de forma masiva y la explosión de datos procedentes de sensores de la IoT será ya una realidad

Predicciones para 2016 hechas por personas de referencia:

“2016 will be the year of deep learning. Data will move from experimental to deployed technology in image recognition, language understanding, and exceed human performance in many areas.”

– Gregory Piatetsky, President of KDNuggets

“2016 will be exciting for Big Data – Big Data will go even more mainstream. 2016 will also be the year when companies without solid big data strategies will start to fall behind. In terms of technology, I see particular growth in real-time data analytics and increasing use of machine-learning algorithms.”

– Bernard Marr, Big Data Guru and Bestselling Author

“In 2016, the world of big data will focus more on smart data, regardless of size. Smart data are wide data (high variety), not necessarily deep data (high volume). Data are “smart” when they consist of feature-rich content and context (time, location, associations, links, interdependencies, etc.) that enable intelligent and even autonomous data-driven processes, discoveries, decisions, and applications.”

Kirk Borne, Principal Data Scientist at Booze Allen Hamilton and founder of RocketDataScience.org

“2016 will see an expansion of big data analytics with tools that make it possible for business users to perform comprehensive self-service exploration with big data when they need it, without major hand holding from IT.”

– Ulrick Pedersen, COO of Targit

“Because big data needs a lot of processing power, many organisations will make use of cloud-based, big-data-as-a-service offerings, so they can get the full value of their information, without the associated capital expenditure.”

– Stuart Mills, Regional Sales Director at CenturyLink

“In 2016 it will be all about what actions you will derive from the data you have access to. Bring in the algorithms. Algorithms define action and they are very specific pieces of software that are very good at a very specific action, much better than humans can do. Think for example of quickly determining the right advertisement based on your profile when you visit a website or finding an outlier in vast amounts of transaction data to determine fraud.”

– Mark van Rijmenam, Bestselling Author and Founder of Datafloq

“The use of masses of data as an indicator of success will turn to the quality of the data being collected. This will mean that the variety for each company is likely to decrease, but the specific data that will be collected will become far more efficient, useful and plentiful. As companies realize that most of what they collect isn’t being used and just taking up storage space, this will become more apparent and the use of this data will come under increased scrutiny.”

– Chris Towers, Head of Big Data Innovation at Innovation Enterprise

“As with every industry, disruptive forces—security, sustainability, speed and costs—are driving change in the way data centers are architected, constructed and operated. This should continue throughout 2016 as the ability to deliver applications and content to users while collecting and analyzing data becomes more critical to business success.”

– Steve Hassell, President of Data Center Solutions at Emerson Network Power

“Machine learning will reduce the insight killer — time. Machine learning will replace manual data wrangling and data governance dirty work. The freeing up of time will accelerate data strategies.”

– Brian Hopkins, VP and Principal Strategest at Forrester Research

“Enabling users to see a broad range of factors contributing to their business is becoming more important than ever. With the ability to combine both internal and external data sources, users now have access to more context around their data, which ultimately leads to more insights and better decisions. Adding socio demographic or location data to analysis easily and quickly can help organisations de-risk some of their management choices.”

– James Richardson, Business Analytics Strategist at Qlik

“In 2016 I’m looking to fund those businesses that make possible to create APIs, turn web into data, all those difficult problems that constitutes the plumbing of the Internet, will be the like the Levi’s of the net”

– Thomas Korte, Founder of AngelPad

“Next year businesses will look at deriving value from ALL data. It’s not just the Internet of Things but rather Internet of Anything that can provide insights. Getting value from data extends beyond devices, sensors and machines and includes ALL data — including that produced by server logs, geo location and data from the Internet.”

– Scott Gnau, CTO at Hortonworks

“Artificial intelligence for mobile phones (your phone being able to figure out what you are doing and predict what you are going to do next).”

– Andrea Cox, Open Data Institute

“The use of personal-identifiable data is becoming a growing concern for consumers, a focus for regulators, and a battleground for consumer trust. Companies that proactively respect and protect consumer data are going to be the winners. Privacy will become the killer app for 2016.”

– Tim Barker, CEO of DataSift

“Several jump to mind but the one that sticks out is the surge in new apps that use strong encryption to secure mobile messaging, voice, video and file exchange, for businesses as well as individuals. Not many people have noticed this yet but they will. Doubtless, governments will be unhappy but there is no stopping this one. Businesses in particular no longer trust open communication so we’re heading for a world in which it will all be encrypted.”

– John Dunn, Editor for Computerworld UK and Techworld

Tendencias emergentes en analítica de datos:

  1. Plumbers wanted: data management overhead demands professional data mungers
  2. Hardening models:  increasingly complex models require tighter approaches to diagnostics and validation
  3. The tunnel link: big data engineering and methodological approaches meet in the middle
  4. Change management to the fore: evidence-based decision-making requires management to contemplate new organizational forms
  5. Invisible architectures: enterprise architecture embraces systems management to forge a path through the mist of multi-systems complexity
  6. We’re not in Kansas anymore:  increasingly diffuse models requires a deeper methodological understanding of broader research paradigms
  7. Living with the paradox: coming to terms with irresolvable methodological quandaries
  8. Cyborg enterprise:  industrial-scale analytics ushers in the age of highly integrated, large-scale techno-organizational decision programs
  9. Not for everyone, but necessary none-the-less:  analytics as a service and outsourcing analytics as a function
  10. On-ramping AI: organizational operationalization as a step towards machine automation
  11. Emerging profession: professional computational decision engineers and AI stewardship
  12. Far-future: the birth of the Chief Meaning Officer – equal parts decision scientist, IT manager, storyteller, and organizational anthropologist