During autumn I have been working as a guest professor at Södertörn university, within a project run by my colleague Lars Degerstedt at the School of Natural Science, Technology and Environmental Studies. Together we are now writing an article which deals with new forms of media intelligence, and different challenges for the competitive intelligence business. The idea is to have a finished article in mid January and submitting it to Nordicom Review. At present the article has the title: “More Media, More People—Conceptual Challenges for Social and Multimodal Data Driven Competitive Intelligence”. The introduction gives some hints of what we are trying to do, and the text starts like this:
Today, the amount of data produced in a single minute is mind-numbing. Streams—if not floods—of social and multimodal data consequently pose a pivotal challenge for companies within the competitive intelligence business. One of these, the computer software company Domo, has marketed itself as a service designed to provide direct and simplified, real time access to business data without IT involvement. According to Domo, the contemporary data deluge shows no sign of slowing down. “Data Never Sleeps” has hence been the appropriate title of a series of infographics the company has released. The latest version 3.0 was presented in August 2015. Much of what we do every day happens in the digital realm, Domo states. These activities leave an ever increasing digital t®ail “that can be measured and analysed”. Correspondingly, the infographic “Data Never Sleeps 3.0” revealed that every minute users liked a staggering 4,166,667 posts on Facebook, 347,222 tweets were sent on Twitter, at Netflix 77,160 hours of video were streamed every minute—and 300 hours of video uploaded on YouTube. Furthermore, 284,722 images were shared on Snapchat, and at Apple 51,000 apps were downloaded. Notably, these social data transactions occurred every minute, around the clock (Domo, 2015).
Sleepless data hence seems to be the perfect description of today’s global information landscape. Crowd or community based social media, in short, produces data flows that are both a blessing and a curse for competitive intelligence businesses. Handling new forms of social and multimodal data, however, requires new skills—conceptually as well as technologically. However, no data is error-free. On the contrary. There are a number of myths that flourish within the contemporary hype of Big Data. So called data cleansing for example, always has to be performed before, say the depicted data in Domo’s infographic can be analysed. Moreover, the same data also has to be interpreted. All forms of information and media management within the competitive intelligence business basically follows the same pattern: data needs to be collected, entered, compiled, stored, processed, mined, and interpreted. And, importantly: ”the final term in this sequence—interpretation—haunts its predecessors”, as Lisa Gitelman has stressed in the aptly titled book, Raw Data is an Oxymoron (Gitelman, 2013, 3).
With “each click, share and like, the world’s data pool is expanding faster than we comprehend”, the Domo infographic informs potential customers. At a Domo event prior to the launch of the infographic 3.0, the data artist—yes, that is the way he describes himself—Jer Thorp, stated that “not only are we doing more with data, data is doing more with us”. For consumers and business users alike, “improving our lives” thus requires a better understanding of what contemporary “interactions with data” actually mean, according to both Thorp (and Domo). And naturally this is exactly what is being marketed: only Domo can help a business make sense of the “endless stream of data”. The company even has a business intelligence tool with the enticing name “Magic”, that lets customers “cleanse, combine and transform” their data. Data combinatorics provides greater insights, Domo asserts, and thus enables customers to see the whole picture. “Magic provides several intuitive tools to help you prepare your data”—and especially so, if Magic is combined with the company’s presentational tool kit that “quickly interprets the data for you, and suggests how to visualize it for maximum impact and clarity” (Domo, 2015). In other words, the infographic of Domo is aesthetically pleasing for a reason. Today within the competitive intelligence business, maximum impact simply requires Beatiful Data—which happens to be the title of a fascinating book by Orit Halpern. According to her, all data “must be crafted and mined in order to [become] valuable and beautiful” (Halpern, 2014, 5).
Domo is in many ways a successfull American start-up, currently funded by venture capital, but also with a cristal clear business plan. In a video demo, Domo state that their core idea revolves around “the future of business management”. The demo gives viewers an “exclusive look at Domo”, ending with the invocation: “what you need is a platform that brings your people and all the data they rely on together in one place.” In short, Domo is all about business intelligence as social data. Via this video demo, the beautiful infographic and the sleepless data presented by Domo, the purpose of this article is to address similar challenges facing competitive intelligence in an a gradually modified information landscape. When data structures information—what to collect and analyse? If Domo promises it’s customers that their platform makes it “easy to see the information you care about”, how is data perceive and conceptualised? (Domo, 2015). In this article, we argue that data driven competitive intelligence—which is basically what companies like Domo do—particularly needs to pay attention to new forms of (A.) crowd orientated and (B.) media saturated information. If business intelligence traditionally has referred to a set of techniques and tools that transforms textual data into useful information for business analysis, such techniques need to consider that the media landscape has been altered in both a social and non textual direction.
If more data—is better data (as some would have it), accordingly more people that create more media, should be understood in a similar way. This article will consequently start with some introductory remarks around the broader concept of “media intelligence”, and the ways that competitive intelligence businesses has adapted to a transformed media environment—turned datascape. In the subsequent sections, the notions of “social competitive intelligence” and “media analytics” are used as two further concepts that media intelligence evolve around. Firstly, social competitive intelligence tries to understand how a changing information environment will impact organizations and companies by monitoring events, actors and trends. Information today doesn’t only want to be free—information wants to be social. If general usage of technology was once described with terms like social engineering, the linchpin of today’s culture of connectivity is social software. By presenting some findings from the so called CIBAS-project, we thus describe how organisations and companies increasingly rely on (more or less) (in)formal social networking structures and individual decision making as a means to increase rapid response and agile creativity. Secondly, if business analytics focuses on developing insights primarily on textual data and statistical methods, media analytics basically does the same—yet giving priority to audiovisual media streams, often with a slant of sociality—so called social video is for example perceived as increasingly trendy in the way businesses will use social media in years ahead. In our article we use “fashion analytics” as an example, gleaned from a commercial sector where audiovisual big data is currently in vogue. Finally, some concluding remarks are presented.