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Big data solutions: trends, innovation

We have seen the birth to a generation of enterprises that are data-rich and analytically driven, eagerly following trends in big data and analytics. Let’s take a closer look as I provide some use cases demonstrating how IBM is helping clients find innovative big data solutions.


1. Datafication-led innovation
Data is the new basis of competitive advantage. Enterprises that use data and sophisticated analytics turn insight into innovation, creating efficient new business processes, informing strategic decision making and outpacing their peers on a variety of fronts.


2. Sophisticated analytics for rich media
Much of produced data is useless without applying appropriate analytics to it. Where does opportunity lie? According to the International Data Corporation (IDC), rich media (video, audio, images) analytics will at least triple in 2015 to emerge as a key driver for big data and analytics technology investment. And such data requires sophisticated analytics tools. Indeed, consider e-commerce–based image search: accurate, relevant image search analysis that doesn't require human tagging or intervention is a significant opportunity in the market. We can expect similar smart analytics capabilities to offer similar opportunities.


3. Predictive analytics driving efficiency
Applications featuring predictive capabilities are picking up speed. Predictive analytics enhances value by boosting effectiveness, providing measurability of the application itself, recognizing the value of the data scientist and maintaining a dynamically adaptive infrastructure. For these reasons, predictive analytics capabilities are becoming an integral component of analytics tools.


4. Big data in the cloud
Over the next five years, IDC predicts, spending on cloud-based big data analytics solutions will grow three times more quickly than spending on on-premises solutions—and hybrid deployments will become a must-have. Moreover, says IDC, with data sources located both in and out of the cloud, business-level metadata repositories will be used to relate data. Organizations should evaluate offerings from public cloud providers to seek help overcoming challenges associated with big data management, including the following:

  • Security and privacy policies and regulations affecting deployment options
  • Data movement and integration requirements for supporting hybrid cloud environments
  • Building a business glossary and managing map data to prevent overwhelming data
  • Building a cloud metadata repository (containing business terms, IT assets, data definitions and logical data models) that points to physical data elements.


5. Cognitive computing
Cognitive computing is a game-changing technology that uses natural language processing and machine learning to help humans and machines interact naturally and to augment human expertise. Personalization applications using cognitive computing will help consumers shop for clothes, choose a bottle of wine or even create a new recipe. And IBM Watson is leading the charge.


6. Big money for big data
Increasingly, organizations are monetizing their data, whether by selling it or by providing value-added content. According to IDC, 70 percent of large organizations already purchase external data, and 100 percent are expected to do so by 2019. Accordingly, organizations must understand what their potential customers value and must become proficient at packaging data and value-added content products, experimenting to find the “right” mix of data and combining content analytics with structured data, delivered through dashboards, to help create value for external parties interacting with the analysis.


7. Real-time analytics and the Internet of Things
The Internet of Things (IoT) is expected to grow at a five-year CAGR of 30 percent and, in its role as a business driver, to lead many organizations to their first use of streaming analytics. Indeed, the explosion of data coming from the Internet of Things will accelerate real-time and streaming analytics, requiring data scientists and subject matter experts to sift through data in search of repeatable patterns that can be developed into event processing models. Event processing can then process incoming events, correlating them with relevant models and detecting in real time conditions requiring response. Moreover, event processing is an integral part of systems and applications that operationalize big data, for doing so involves continuous processing and thus requires response times as near to real time as possible.

8. Increased investments in skills
Many organizations want to combine business knowledge and analytics but have difficulty finding individuals who are skilled enough to do so. Leading companies in particular feel this talent gap keenly, for as they move to broaden skills across the enterprise, the need for combined skills becomes ever more apparent. Indeed, combined skills are of critical importance in speed-driven organizations, for such skills speed the translation of insights into actions through deep knowledge of the business drivers—and the data related to them—that are likely to affect performance.

Originally posted on Data Science Central

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