2016 Trends in Big Data: Insights and Action Turn Big Data Small

Big data’s salience throughout the contemporary data sphere is all but solidified. Gartner indicates its technologies are embedded within numerous facets of data management, from conventional analytics to sophisticated data science issues.

Consequently, expectations for big data will shift this year. It is no longer sufficient to justify big data deployments by emphasizing the amount and sundry of types of data these technologies ingest, but rather the specific business value they create by offering targeted applications and use cases providing, ideally, quantifiable results.

The shift in big data expectations, then, will go from big to small. That transformation in the perception and deployments of big data will be spearheaded by numerous aspects of data management, from the evolving roles of Chief Data Officers to developments in the Internet of Things. Still, the most notable trends impacting big data will inevitably pertain to the different aspects of:

• Ubiquitous Machine Learning: Machine learning will prove one of the most valuable technologies for reducing time to insight and action for big data. Its propensity for generating future algorithms based on the demonstrated use and practicality of current ones can improve analytics and the value it yields. It can also expedite numerous preparation processes related to data integration, cleansing, transformation and others, while smoothing data governance implementation.
• Cloud-Based IT Outsourcing: The cloud benefits of scale, cost, and storage will alter big data initiatives by transforming IT departments. The new paradigm for this organizational function will involve a hybridized architecture in which all but the most vital and longstanding systems are outsourced to complement existing infrastructure.
• Data Science for Hire: Whereas some of the more complicated aspects of data science (tailoring solutions to specific business processes) will remain tenuous, numerous aspects of this discipline have become automated and accelerated. The emergence of a market for algorithms, Machine Learning-as-a-Service, and self-service data discovery and management tools will spur this trend.

From Machine Learning to Artificial Intelligence
The correlation between these three trends is probably typified by the increasing prevalence of machine learning, which is an integral part of many of the analytics functions that IT departments are outsourcing and aspects of data science that have become automated. The expectations for machine learning will truly blossom this year, with Gartner offering numerous predictions for the end of the decade in which elements of artificial intelligence are normative parts of daily business activities. The projected expansion of the IoT and the automated activity required of the predictive analytics required for its continued growth will increase the reliance on machine learning, while its applications in various data preparation and governance tools are equally as vital.

Nonetheless, the chief way in which machine learning will help to shift the focus of big data from sprawling to narrow relates to the fact that it either eschews or hastens human involvement in all of the aforementioned processes, and in many others as well. Forrester predicted that: “Machine learning will replace manual data wrangling and data governance dirty work…The freeing up of time will accelerate the execution of data and analytics strategies, allowing organizations to get to the good stuff, taking actions and driving better business outcomes based on the data.” Machine learning will enable organizations to spend less time managing their data and more time creating action from the insights they provide.

Accelerating data management processes also enables users to spend more time understanding their data. John Rueter, Vice President of Marketing at Cambridge Semantics, denoted the importance of establishing the context and meaning of data. “Everyone is in such a race to collect as much data as they can and store it so they can get to it when they want to, when oftentimes they really aren’t thinking ahead of time about what they want to do with it, and how it is going to be used. The fact of the matter is what’s the point of collecting all this data if you don’t understand it?”

Cloud-Based IT
The trend of outsourcing IT to the cloud is evinced in a number of different ways, from a distributed model of data management to one in which IT resources are more frequently accessed through the cloud. The variation of basic data management services that the enterprise is able to outsource via the cloud (including analytics, integration, computations, CRM, etc.) are revamping typical architectural concerns, which are increasingly involving the cloud. These facts are substantiated by IDC’s predictions that, “By 2018, at least 50 % of IT spending will be cloud based. By 2018, 65 % of all enterprise IT assets will be housed offsite and 33% of IT staff will be employed by third-party, managed service providers.”

The impact of this trend goes beyond merely extending the cloud’s benefits of decreased infrastructure, lower costs, and greater agility. It means that a number of pivotal facets of data management will require less daily manipulating on the part of the enterprise, and that end users can implements the results of those data driven processes quicker and for more specific use cases. Additionally, this trend heralds a fragmentation of the CDO role. The inherent decentralization involved in outsourcing IT functions through the cloud will be reflected in an evolution of this position. The foregoing Forrester post notes that “We will likely see fewer CDOs going forward but more chief analytics officers, or chief data scientists. The role will evolve, not disappear.”

Self-Service Data Science
Data science is another realm in which the other two 2016 trends in big data coalesce. The predominance of machine learning helps to improve the analytical insight gleaned from data science, just as a number of key attributes of this discipline are being outsourced and accessed through the cloud. Those include numerous facets of the analytics process including data discovery, source aggregation, multiple types of analytics and, in some instances, even analysis of the results themselves. As Forrester indicated, “Data science and real-time analytics will collapse the insights time-to-market. The trending of data science and real-time data capture and analytics will continue to close the gaps between data, insight and action. In 2016, Forrester predicts: “A third of firms will pursue data science through outsourcing and technology. Firms will turn to insights services, algorithms markets, and self-service advanced analytics tools, and cognitive computing capabilities, to help fill data science gaps.”

Self-service data science options for analytics encompass myriad forms, from providers that provision graph analytics, Machine Learning-as-a-Service, and various forms of cognitive computing. The burgeoning algorithms market is a vital aspect of this automation of data science, and enables companies to leverage previously existent algorithms with their own data. Some algorithms are stratified according to use cases for data according to business unit or vertical industry. Similarly, Machine Learning-as-a-Service options provide excellent starting points for organizations to simply add their data and reap predictive analytics capabilities.

Targeting Use Cases to Shrink Big Data
The principal point of commonality between all of these trends is the furthering of the self-service movement and the ability it gives end users to hone in on the uses of data, as opposed to merely focusing on the data itself and its management. The ramifications are that organizations and individual users will be able to tailor and target their big data deployments for individualized use cases, creating more value at the departmental and intradepartmental levels…and for the enterprise as a whole. The facilitation of small applications and uses of big data will justify this technology’s dominance of the data landscape.

Source: 2016 Trends in Big Data: Insights and Action Turn Big Data Small

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