New Business paradigm : Within a decade, enterprises have had to adapt to entirely new marketing channels and paradigms that are largely dominated by web and social media. The availability of customer data is changing how many enterprises view business processes. While in past, enterprises have focused on transactional relationships with customers, they have widely ignored valuable customer interactions such as social media data, call center reports, emails, texts and audio files. The vast amount of social data that is now available is forcing enterprises to look closer at how unstructured data of all types can help build a more robust view of customers.
Issues with Large Data : One of the biggest challenges among this is the handling of "Large Data". Enterprise data has grown very huge in the past 70 years and research predicts that enterprises need to maintain data 50 times more by 2020. The growing data in enterprises has increased the complexity and cost requirements for expanding the conventional data models for application, database and storage to accomodate the demands of Large Data.
Multimodal, Multilingual and Multicultural data: Enterprise relevant data is multifaceted ranging from structured data (such as real-time transactions or measurements), unstructured data (such as social media, news, reports, blogs, emails, tweets, wikis and word processing documents) to multi-channel data (such as pictures and graphic images, videos, streaming instrument data, webpages, pdfs, Powerpoint presentations). Workspaces are also getting embedded with various IoT devices such as smartphone trackers, wearables that are open to the web through APIs. Similarly, news about markets, enterprise internals are also readily available on the web. Far reaching and location specific news in distant branches and supply chain of an enterprise are available if not in English, in local languages with the advancement of W3C and W4A. As enterprises become increasingly inter-dependent with various geographies and face the inevitable need to address the concerns of their diverse workforce to reach optimum productivity, compliance and risk assessment, these unstructured sources of data become important to requestor relevant contextual intelligence. Unfortunately, most enterprise data still lives in silos, whereas the actual intelligence comes from fusing across data sets. Acquisition of such large data and then efficiently applying AI techniques to extract relevant knowledge across multiple data sources and channels can give enterprises a huge business intelligence.
Lack of Behavioral Signal Analysis: Another important point of concern is the lack of strategies to study behavioral signals both within and outside the enterprise. With a lot of customers and employees now communicating on the web and a large number of enterprise processes becoming open, we have access to employee understanding and insight into enterprise health and process from them.
Focus on Agility: Further, it is becoming increasingly important to enterprises to become Agile. Enterprise data are distributed and it has moved from the Desktop to Web to Mobile. In today's competitive business scenario, it has become inevitable to provide customers with the mobile access to enterprise data, to retain and win customers. Mobility has risen to prominence due to the dynamic way customers do business from anywhere at any time of day. Mobile expansion demands fresh thinking for creating mobile business environments that may be relied on cloud based technologies and services.
Looking into the future, enterprises will have to
enhance their traditional business models by merging it with information
and analytic techniques on the growing supply of unstructured
information sources to generate contextually relevant social insight,
about their customers. In many cases, this will require enterprises to
examine the ability of their existing business intelligence environments
to handle the streams of unstructured data that flow from traditional
channels and new social media channels. As one can imagine, it is an
extremely multi-disciplinary endeavor, where one needs inputs not only
from Business professionals, AI and Machine-Learning researchers, but
also linguists, social scientists, HCI, design and vision researchers.
Therefore, the primary goal of this workshop is to bring together researchers working in the aforementioned fields and those whose work concerns the intersection of these areas, together and provide a venue for the multidisciplinary discussion of how ubiquitous AI technologies can help addressing the aforementioned challenges. More specifically we focus on addressing the following issues:
How AI technologies including, machine learning, natural language processing, natural language generation, chatbots can help extracting relevant social and enterprise intelligence?
How data collected across multiple channels can be fused together to get better social and enterprise intelligence
With such an explosion of information, how contextually relevant knowledge can be provided to the right person at real time.
The workshop is aimed at focusing the attention of Web Conference research community on the research challenges and opportunities in the area of social sensing and enterprise intelligence. This workshop is intended to bring together researchers in all related disciplines and provide a forum to understand the unique research challenges of this domain. Given the lack of large standardized corpora for this area of research, we are also interested in developing public data sets for this area.
The advent of social media in the last decade has affected the way people interact and employees share information. Previously, the unstructured information that is in the minds of employees was shared and worked upon through formal channels such as paper notices and more recently emails. Social media has brought about the formal sharing of work related information across geographies changing workspaces irreversibly. Almost nothing happens in a closed room anymore. Most discussions and the ongoings in an enterprise, both policy and execution is open to employees irrespective of hierarchy. Some of the challenges in enterprise have been heavily researched from organizational science and not yet from the large amount of human generated data that is now available on the web, and hence it is a nascent area of research to accelerate in depth application of computational social science to this. The Web Conference (WWW 2018) is the appropriate venue to highlight this as an upcoming research area and generating interest in the research community towards problems in the same. Therefore, the workshop addresses the niche topic of social media analytics and social intelligence from an enterprise perspective, which is quite adjacent to the key topics of the main conference. Connecting social and enterprise intelligence to transform the customer experience is an emerging area of research, highly relevant for Web conference attendees, given the increasing volume of social media data from citizens on different issues related to an enterprise and increasing customer participation in an customer centric business environment.
Social Media analytics for enterprise intelligence
Information fusion for enterprise intelligence
Multilingual text mining
Multimodal data analytics
Code mixed information extraction
Employee behavior modeling
Machine Learning for data compliance and risk assessment
Web intelligence for customer behavior modeling
Analytical techniques for customer retention and churn prediction
Crowdsourcing for enterprise task solving
Image processing applications for enterprise intelligence
Social network analysis
Natural language interfaces for enterprise applications