There is still time to join the fantastic Big Conference 2012. There will be lots of great market research papers presented and lots of talk about the intersection between Big Data and Market Research.
See the trailer video and the programme here www.bigconference.org
In a couple of previous posts I covered influence in relation to the debate on Big Data. (here and here)
(See here for more on influence)
The top 15 most influential stakeholders in the debate on Big Data are listed below:
Each stakeholder is shown with their April and February rank.
NY Times has now taken the number 1 spot and done so after really stepping up coverage of the area, both in their main channels and in their bits-blogs.
See for example this great piece
McKinsey continues to move up the ladder. Their comprehensive white paper on Big Data is really paying off for them.
Most of the hardware and software suppliers lost grounds in the last couple of months. The exception is Microsoft who is new in the top 15 (they were just outside in Feb). Their announcements to offer Hadoop on their cloud platform as well as the launch of SQL Server 2012 are some of the reasons they are moving up.
Curt Monash dropped out of the top 15 and is now number 25. But he is still easily the most influential individual with no other individuals in top 50.
If you have an interest in Big Data you should consider subscribing to our quarterly Big Data report. It gives a comprehensive overview of the trends in the debate on Big Data and analyse what is driving the attention. It also gives a more complete list of the top 100 influential stakeholders. Drop us a note if you are interested in learning more.
Stakeholders listed are New York Times, Strata Conference, GigaOM, Wired, McKinsey, Oracle, ZDnet, Cloudera, Forbes, O’Reilly Media, IBM, EMC, ReadWriteWeb.com, Techcrunch.com, Microsoft
Together with Maxine Cook, Microsoft, I am presenting a paper at the BIG Conference 2012, 25-27 April 2012.
The paper is titled “Adding online listening and analytics to the research mix” and provides a distillation of the experiences and learnings from a number of organisations and a specific case study about Microsoft’s experience in this area.
There are lots of other great papers being presented there. See the full program here
Following on from my previous post about who are influential in the debate on Big Data I decided to have a look at how much attention some of the main players are drawing and how it has developed over time. Our market intelligence platform, InfluenceMonitor, has the answer (and keeps it up-to-date).
The chart below shows the Share-of-Influence (SoI) of 9 brands often associated with the Big Data debate.
SoI is the share of the mention, but with each mention weighed in with the measured influence (on the topic of Big Data) of the voice that makes the mention. The direction of SoI is often a leading indicator for the direction of market share.
The graph shows a number of interesting trends.
First, notice how Oracle has really done well in Q4 and Q1 (so far).
Second, HP seems to be losing out. The Autonomy deal announced in Q3 11 did seem to buck the negative trend in that quarter, but hasn’t helped in Q4 and Q1 12.
Third, McKinsey, a thought leader in the space, have grown their influence almost 9 fold from Q1 11 to Q1 12.
Looking at the graph the big question is clearly, “what did Oracle do that so dramatically moved the needle?”
Well, big data (sorry) helps answer the question.
Using the root-cause analysis function in InfluenceMonitor it turns out that the increase in Oracle’s SoI from Q3 to Q4 was driven by several factors. Oracle’s launch of their NoSQL database had a substantial impact and so did the fact that Day 2 of Oracle OpenWorld focused on Big Data and Analytics.
The attention to Oracle in Q1 12 was largely driven by its launch of Big Data Appliance and its partnership with Cloudera.
The screenshot below shows the Root-Cause Analysis in InfluenceMonitor. The box on the left shows what was driving growth from the first selected metric or period to the next. The right box shows what was pulling the metric the other way. So very quickly you get a good understanding of what was moving the needle.
Companies mentioned: Cloudera, HP, Mckinsey, SAS, Teradata, Microsoft, EMC, IBM, Oracle
Big Data is attracting increased attention these days and I had a quick look at who are the most influential stakeholders of the debate.
The list below shows the top 15 most influential stakeholders in the debate on Big Data.
There may not be many surprises. McKinsey is clearly getting credit for their recent excellent overview of the whole area.
Curt Monash (at #13) is the most influential individual.
Conferences play a big role. Strata Conference and GigaOM’s Structure conference draw a lot of attention and influence.
How to read the list (left to right): The arrow shows the most recent direction of movement and a star means they are new to the list. Example: NY Times is moving down the list and GigaOM is moving up.
Influence here is the relative punching weight of the individual stakeholder. Example: Since Oracle’s influence is about twice that of Forbes, communication on this issue from Oracle will have the twice the market impact than a similar message from Forbes.
Next column is Popularity, which is essentially a measure of how well-known a stakeholder is on the issue. GigaOM is the most well-known stakeholder on the issue, followed by NY Times and Techcrunch. So why are they not the most influential? Because those who cite those sources on this issue are on average less influential than those who cite for example Cloudera and Oracle.
The column Rel Influence shows how a stakeholder is punching in the debate, compared to what conventional wisdom (aka their popularity) leads us to believe. Notice how Curt Monash punches with 3 times the expected weight and Techcrunch punch below their expected weight. The explanation is simple. Those who cite Monash on this issue are, on average, much less influential than those who cite Techcrunch. Rel(ative) Influence is a great measure for identifying those who should be at the top of your engagement list.
Stakeholders listed: Cloudera, Oracle, New York Times, Strata Conference, GigaOM, ZDnet, EMC, IBM, Orilley, McKinsey, Wired, Techcrunch, Curt Monash, ReadWhiteWeb.com, Forbes.
The total list is of course very long (several thousand stakeholders have measurable influence on this topic).
Big Data is an area of natural interest to us at Onalytica, not least because we gobble up 100 TB of data per month, process it and make the aggregates available for continuously updated market intelligence throughout the enterprise.
I spotted an announcement yesterday in Brand Republic about Peugeot signing an actor by the name John Simm to voice over a 30-second TV spot.
The announcement reveals that this is a multi-million pound campaign to “move the Peugeot brand forward”.
At Onalytica we have a new service called BrandObjective, which, among other things, analyse how similar (or dissimilar) brands in or out of category, are perceived.
By looking at how John Simm is spoken about it is easy to see why Euro RSCG (the agency behind the campaign) picked him.
John Simm represents an attractive combination of street-cred, luxury, premiumness and Britishness.
Using a bit of rocket science maths (I am not kidding – Nasa use this method to classify space debris) you can calculate the probability that two brands are perceived similar.
John Simm is most similar to brands such as Pepsi (Street), Hermes (Luxury), Carlsberg (Street), Downton Abbey (British), Rolex (Luxury), Selfridges (Premium), Burberry (Luxury, British), Harrods (Luxury, British), Dyson (British, Premium), PG Tips (British) and – here it really comes – BMW.
While there is a probability of 95% that the brand properties of John Simm are similar to those of Burberry, there is a probability of 78% of similarity with BMW's brand properties.
In contrast there is a probability of 0% of similarity between the brand properties of Jeremy Clarkson and BMW.
Currently there is a 0% chance that the brand properties of John Simm and Peugeot would be considered similar. If they are to become more similar, the way people perceive Peugeot should focus more on adjectives such as “mysterious”, “funny”, “hardy”, “shameless”, “brilliant”, “ruthless”, “charming”, “giving”, “generous”, “fancy”, “intelligent”, “candid”, “considerate”, “imaginative” and “warm”. These are all brand properties the Peugeot brand is missing when compared with John Simm's.
On the other hand, by becoming more like the John Simm brand Peugeot risk losing some brand properties that on the face of it sounds desirable, such as “smart”, “efficient”, “reliable”, “positive”, “responsible”, “independent”, “friendly”, “active”, “capable”, “confident”, “safe” and “proud”.
In time we will know how successful they will be and of course let you know here.
OECD is now forecasting that the UK economy will enter a recession (story from Telegraph).
From a macro perspective I guess it is not counter-intuitive that growth comes to a halt when the world is engaged in a massive deleveraging operation; and at the same time impacted by the increasing uncertainty caused by the public finances in the Euro Zone.
But reading the forecast from the OECD I couldn’t help think back on the previous posts on this blog about how the change in online sentiment for some time as indicated that a recession was becoming more likely.
The 2nd of August I wrote a this post that showed that those with more influence in the debate on the UK economy was becoming more concerned about a possible recession than the public in general.
Since our influence-weighted analyses usually serves as leading indicators, this was a clear warning sign.
On the 1st of November I wrote this post in which I point out that the Onalytica Recession-Index for the UK economy had reached an all-time high (since April 2010).
In October 2011 the equal-weighted Recession-Index, which represents the sentiment of the board population, actually overtook the influence-weighted, which represents the sentiment of those with more influence in the debate on the UK Economy. The gap has widened in November.
This effectively means that the broad population as a whole are now more convinced that we are heading for a recession and are likely to reign in their spending further.
The Wall Street Journal has a story that economists thinks there is a reduced chance of recession in the U.S.
According to the story 52 economists surveyed in November put the chance of a U.S. recession in the next 12 months at 1-in-4, down from 1-in-3 two months ago.
I checked the Onalytica Recession-Index for the U.S. Economy to see if I could corroborate that. The chart is shown below.
Clearly it shows that there has been a declining anticipation of a recession in the U.S. economy since September.
Comparing the Onalytica Recession-Index for the U.S. economy to the U.K. economy is interesting. The chart below shows the Recession-Index for both economies.
Notice how they were very closely linked until May 2011. After that time the recession concerns were primarily with the U.S. economy. However, after August the outlook for the U.K. economy started to become worse while the perception for the U.S. economy started to improve.
Maybe what the U.K. can hope for is that the U.S. economy starts to grow to an extent that it can help the U.K. avoid recession.
See this article for more information on how these indices are constructed.
The state and trends of the sentiment in the economic debate can be highlighted using Onalytica Indexes.
Onalytica Indexes is a collection of indexes which tracks how online media report on a range of economic and business issues.
Over the years an increasing amount of research has been done into how media reporting can be used as indicators for the state of the economy. Other research has highlighted that the way economic news is reported can explain why consumer sentiment sometimes departs from economic fundamentals.
Onalytica Indexes shares the same basic ideas as these examples; that the level and change in the attention an issue receives in the public debate can increasingly be transformed into an indicator of economic trends.
In practice this is done by continuously collecting a large sample of what is published online about, in this case, the Eurozone economy. The Inflation-Index then measures the amount of articles that make references to what can be interpreted as inflationary issues. This includes the word “inflation” itself, references to increasing prices, that goods and services are becoming more expensive, and so on.
Figure 1 (below) shows the Onalytica Recession-Index for the UK Economy. Notice the sharp and accelerating increase since the low-point in July 2011.
The Recession-Index shown in Figure 1 uses equal weight for all voices. This weighting usually makes the results correlate well with what a popular poll would show.
The interpretation of the graph is thus that the population in general thinks that there is a higher chance of a recession in the UK Economy than they have thought at any time since April 2010.
Figure 2 (below) also shows the Recession- Index, but here all voices are weighted according to their calculated influence on the topic “UK Economy”. This means that national media, influential economists and similar are weighted more than, say a personal blog.
One way of interpreting Figure 2 is that among this group of stakeholders there has also been an increasing expectation (or fear) of a recession in the UK Economy since June, but the increase seems less dramatic.
Comparing Figure 1 and Figure 2 it is clear that “the average person” regards the chance of a recession as higher; both groups regard the chances as dramatically increased since July of 2011.
Clearly, if the government was hoping that consumers were going to feel more optimistic and start spending more, the data seems to be disappointing.
Another example of how the economic sentiment can be better understood using this type of analysis is shown in Figure 3.
Figure 3 (below) shows the Onalytica Crisis-Index for the Euro Zone since April 2010. Clearly the view that a crisis is becoming more likely has been increasing since late April 2011 and although it seems to have dropped slightly in the last three weeks it will be very interesting to follow this index going forward.
A final example shows how combining indices can be very powerful.
Figure 4 (below) shows the Onalytica Recession-Index and Onalytica Inflation-Index for the US Economy since April 2010.
The Recession-Index is used as an indicator of the collective sentiment and indicates that the economy is likely to decline.
As Inflation is a key focus of a growing economy an increase in the Inflation-Index can be interpreted as an expectation of economic growth.
The idea is not that these Indexes should necessarily predict the underlying economic data, but show how the public in general (the equal weighted version) and those with more influence (the influence weighted version) perceive the economy.
I haven’t tested the indexes in Figure 4 against actual GDP or Inflation figures so I can’t comment on those correlations. I have tested them against the S&P500 Index and that looks impressive.
We have a couple of new positions in our software development team. Great opportunities for software engineers who want to work with “big data”.
Check them out here http://www.onalytica.com/careers/current-opportunities