Data thoughts

Outside Insight: how social data could contribute to a new vision for decision-making

Several interesting mergers and partnerships in the social listening and analytics spaces occurred in the last couple of months: just for the month of October 2018, research firm Ipsos bought Synthesio, Brandwatch merged with Crimson Hexagon and Linkfluence announced the acquisition of It The consolidation of this industry is accelerating, although there have been some movements for a couple of years; for instance, in 2017, the acquisition of Buzzsumo by Brandwatch made a lot of sense from a SEO/content marketing perspective.

Call me biased, but the acquisition of Sysomos (company I work for at the moment I’m publishing this) by Meltwater announced in April 2018 made even more sense for me.

The Meltwater project: Outside insight

Over the recent years, this company notorious for its media monitoring software, has been acquiring data science startups and social data companies such as Datasift. To what purpose? CEO Jørn Lyseggen has an ambitious long-term plan for a new type of software that would connect as much unexplored yet relevant data as possible, make sense of it and bring a “big transformation when it comes to corporate decision-making”.

He promotes his vision in the book Outside Insight and I’m going to share highlights of the book.

Outside Insight: a swift in the paradigm of decision-making

As of now, companies rely in internal data to make business decisions: quarterly sales, website traffic, CRM stats etc.
But internal data is by essence
– Historical, thus lagging, like “driving a car looking in the rear-view mirror”
– Insular: the focus shouldn’t be on how the company is doing but where the industry is leading.

To summarise, “external online data is the biggest blind spot in corporate decision-making today”.

A personal comment on that: there are out there tons of websites and articles of list of companies that failed because they didn’t adapt to their markets. My favourite example is how Nokia missed the cultural trend in China that switched consumers from mobile phones to smartphones, as explained by the technology ethnographer Tricia Wang, in her TedTalk: if you haven’t seen her presentation on the human insights missing from Big data, go see it immediately and come back after!

Outside insight Meltwater 1
A summary of the decision-making swift of paradigm suggested by the Meltwater Outside Insight approach

Outline of the swift to an external data focus

In this approach, monitoring your industry is essential, and benchmark is “the most honest measure of success”. What matters is not that your performance improved, but how much more it improved compared to your competitors’.

That’s why the book also provides guidance and a framework to incorporate the Outside Insight methodology.
It includes understanding the competitive landscape you need to monitor:

Outside insight Meltwater 2
Outside Insight: framework to incorporate the OI methodology

And the last phase, the most ambitious, would be to convert your focus to new indicators, in touch with the Outside Insight paradigm:

Outside insight Meltwater 3

What role for online data in this Outside Insight approach?

Some of the KPIs regarding your competitors are just not available to you: hence the need to look at new indicators and interpret them efficiently.

News coverage, social media comments and likes, product reviews, job postings from competitors, industrial patents are some of the ‘digital breadcrumbs’ that inform us about how our company and our competitors are doing. By tracking those breadcrumbs, we can make more sense of how the industry is evolving.

This aligns with 3 of my personal beliefs regarding social data:
1. Social media data is a goldmine of insights, mostly used by the marketing industry but untapped by many other sectors, such as business strategy, product development or risk management
2. Far from dismissing the biases and flaws of social data (I’ll dedicate a blogpost on the topic), I recommend combining social data with additional on and offline data types to generate a fuller picture, representing the reality with different perspectives
3. Most of online data points are by nature qualitative, unstructured, messy, and/or way too numerous to be analysed manually: connecting all that data constitutes a serious challenge. So how do you cope with volume, velocity and unstructured data?

“To the rise of an entirely new software category”

As the data is different by nature, Jørn Lyseggen suggests the solution would be a new type of software, complex, relying on NLP, AI and machine learning.
While BI is focused on internal data and operational metrics, OI is “concerned with a real-time understanding of the ebb and flow of the competitive landscape in order to anticipate future threats and opportunities”.
The plans are ambitious, but Meltwater is making the move to connecting all the required dots to provide such a software.
Exciting times ahead…

If you want to know more about the software launched by Meltwater, please visit https://outsideins

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