Table of Contents
Data-led innovations leading to convergence across markets
The exponential growth of digital data has been well accommodated by the Big Data technology. However, gaining useful insights from the huge amount of data is immensely essential. Big Data Analytics delivers the capability to scale down the data and extract information for decision making. Big Data Analytics serves as the means to leverage Big Data for effective analytics. This research service focuses on various innovations in the Big Data Analytics space along with the opportunities for convergence with various other futuristic technologies.
The research service includes the following:
- Technology value chain, and key stakeholder activities
- Big Data Analytics–Key Trends
- Highlights of Prominent Innovation Initiatives
- Convergence Scenarios: Big Data Analytics with Futuristic Technologies
- Big Data Analytics and its Role in Sustainable ICT
- Opportunity Space for Developers
- List of Key Contacts and Patents
- Step 1: To provide a thorough analysis of each topic, Technical Insights’ analysts perform a review of patents to become familiar with the major developers and commercial participants and their processes.
- Step 2: Building on the patent search, the analysts review abstracts to identify key scientific and technical papers that provide insights into key industry participants and the technical processes, on which they work.
- Step 3: The analysts then create a detailed questionnaire with content created to address the research objectives of the study, which functions as a guide during the interview process. While the analysts use structured questionnaires to guarantee coverage of all the desired issues, they also conduct interviews in a conversational style. This approach results in a more thorough exchange of views with the respondents, and offers greater insight into the relevant issues than more structured interviews may provide.
- Step 4: The analysts conduct primary research with key industry participants and technology developers to obtain the required content. Interviews are completed with sources located throughout the world, in universities, national laboratories, governmental and regulatory bodies, trade associations, and end-user companies, among other key organizations. Our analysts contact the major commercial participants to find out about the advantages and disadvantages of processes and the drivers and challenges behind technologies and applications. Our analysts talk to the principal developers, researchers, engineers, business developers, analysts, strategic planners, and marketing experts, among other professionals.
- Step 5: The project management and research team reviews and analyses the research data that are gathered and adds its recommendations to the draft of the final study. Having conducted both published studies and custom proprietary research covering many types of new and emerging technology activities as well as worldwide industry analysis, the management and research team adds its perspective and experience to provide an accurate, timely analysis. The analysts then prepare written final research services for each project and sometimes present key findings in analyst briefings to clients.
Leveraging the capabilities of fast data aggregation and massive data management, big data will play a vital role in shaping up of a sustainable future. Advancement towards sustainable future will also be facilitated by convergence with other ICT technologies such as Internet of Things and cloud computing.
Big data projects will be spurred by convergence of various technologies within and outside information and communication technology domain in order to deliver advanced applications for various markets.
Technology clusters such as health and wellness, information and communication technology, sustainable energy, microelectronics, and medical device technology are already experiencing major initiatives from stakeholders in the space.
Other clusters such as advanced manufacturing and automation and sensors and control technologies are also expected to gain interest in a very short time span of xx to xx years.
Data management, factory and machine monitoring, deforestation monitoring, energy grid management, water management, and green telematics are some of the sustainable opportunities that big data analytics can create.
Next-gen sequencing, biomarkers, personalized medicine, remote patient monitoring, targeted drug delivery, cloud computing, virtualization, augmented reality, semantic search, in-memory computing, next-gen volatile memory, data visualization, grid energy management, advanced energy storage are some of the technologies that are gaining prominence and penetration with the convergence of big data analytics.
Apart from the convergence across the various technology clusters, future big data analytics applications are expected to converge with advanced technologies, such as artificial intelligence, natural language processing and biometric systems, to empower applications with machine learning ability for advanced intelligence.
This movement towards advanced intelligence system will act as the stepping stone for several future technologies such Cognitive Computing and Cloud Robotics.
What can Big Data Analytics Offer?
Big data analytics refers to a set of data management tools, appliances and techniques for effective analysis of these big data sets toward deriving intelligence on business operations and customer interactions.
- Big data analytics overcomes the limitations in scalability and slow processing speed which exist in conventional data warehousing processes.
- Big data analytics is capable of processing both structured and unstructured data from various sources.
- Information procurement time is becoming more and more critical for business intelligence. Big data analytics empowers enterprises with real-time extraction of data from various sources like RFID, Web, automated sensory technologies, social networking sites, and mobile devices.
Tap the Power of Event, Data, Information, Visual Discoveries toward Strategic Planning
The key deliverables from Big Data Analytics are discoveries which are immensely useful for business enterprises to facilitate them with effective and efficient decision making and strategic planning. This section gives an overview of the four different types of discoveries obtained from Big Data Analytics.
- Event Discovery
Big data analytics has enabled enterprises with successful event discovery leading to a satisfaction level as high as about xx %.
- Processing of events helps enterprises to identify changing trends over time
- Identification of relationships between events aids in strategy formulation.
- Data Discovery
Efficiency in sourcing, combining and relating data helps analysts and data scientists to not only infer, but also support their claims in trend extrapolation studies and in scenario analysis.
- Information Discovery
The extensive amount of information stored in unstructured and semi-structured content and documents is vastly helpful in business understanding, process improvement, reporting and analytics.
- Visual Discovery
Big data analytics facilitates enterprises in performing content analytics for information discovery as an automated function.
Visualization reduces the time spent in data analytics. However, the conventional analytics technologies are not capable of proving interactive and efficient visual analytics. Big Data Analytics has opened up new horizons in visual discovery enabling collaboration and sharing capabilities electronically besides being interactive.
Overcoming Key Challenges
Modifications to technical architecture, changes in operational techniques
- Integration of the big data analytics with the existing data storage platforms: Higher integration through compatible connector development can enhance adoption of big data analytics
Shortage of skilled data science professionals: Introduction of technology convergence related courses that cover applications of big data analytics across verticals.
- High operational and implementation expenses:
High operational and implementation expenses for big data and analytics have been a concern. Further development of new, cost-efficient models such as big data analytics in cloud platforms will enhance and attract much more adoption.
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