January 5, 2015Doug Hadden
Doug Hadden, VP Products
It won`t exactly be the year that we start to love the big data bomb, as in Dr. Strangelove. We`ll still dislike some of the consequences of big data use – in particular, privacy.
But, by the end of the year, the naysayers who think big data is all about `big`will be marginalized. (It isn`t about big (volume) so much, it`s about variety and velocity.) Just another technology term that is somewhat counter-intuitive.
2015 will be the tipping point where analytics, big data, social media and visualization come together. And, sorry major enterprise software vendors, the heavy big data lifting will come via open source. This merging of technologies will enable pattern recognition – will transcend heuristics and biases to become useful, to visualize data. But, it won’t be mainstream in 2015.
- Big data experimentation becomes mainstream. 2015 will not be the year of “big data put into production” except by early adopters. The pieces required – analytics (theme 2), new business models (theme 3), greater understanding (theme 4), security (theme 5), cloud enablement (theme 6) and data governance (theme 7) will be in place by the end of the year.
- Analytics and visualization tool adoption increases, as does mobile business intelligence. Although there is some disagreement about the maturity of these tools for 2015, it appears that dashboards, visualization and integration with back office suites will be mainstream. This is critical for business end-users. Maturing of text analysis, natural language, machine learning and semantic web will also help adoption.
- More business models to monetize big data emerge, but this won’t address privacy concerns. Business models of selling internal data and mashing up internal ane external data will emerge. As will government open data business models. This won’t be mainstream in 2015. But, the emerging models will become use cases for exploration. Meanwhile, governments, companies and consumers will continue to struggle with the privacy implications of big data.
- The characteristics of big data to become better understood in 2015. The complaint that “big data” is a poor descriptor will continue as people begin the understand the key characteristics of the category are velocity and variability, not volume. There will be more understanding of data from activity streams, social connections, external data, IoT, linked data and open data.
- Big data will be increasingly leveraged to improve cyber security. Activity information and machine learning will be particularly important
- Cloud computing will become a fundamental enabler of big data. Cloud services enable experimentation at low cost – the key trend for 2015. Organizations will increasing realize the benefits of using internal data on the public cloud for fast analysis, particularly SMEs with a lot of data.
- Data governance becomes the critical enabler of big data for production.Expect more talk of data governance, ETL, MDM, data warehouses and data lakes. It will become clear that the old models cannot scale to meet the demands of big data. Nevertheless, it will be a transitional year as legacy tools continue to be used.
- The trend for proprietary in-memory databases linked to ERP vendors will slow as customers become concerned about vendor lock-in through “engineered systems”. Open in-memory approaches and the use of the public cloud will become attractive alternatives. Many organization will not be able to justify the cost of in-memory in the short term.
- Backlash against big data will continue by vested interests. This will slow adoption as specialists fight against cross-disciplinary “data science”. There will be resistence to notions of “marchine learning” and exploring data without hypothesis. There will be talk about “smart data” and other ideas that would seem, incorrectly, to be outside the scope of big data.
- Open source remains the engine for big data innovation. It’s not clear what open source projects will lead developments in 2015. But, it is clear that even proprietary big data solutions leverage open source.
Detailed predictions from many pundits are storified below.
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