“Data is the new oil” is a phrase that is becoming popular. It was started in a post by Michael Palmer for the ANA (Association of National Advertisers) back in 2006:

“Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

This is how we ended Part 1 of our look at Market Research as part of our Creative Destruction 7 Act Play series. It is not that it is becoming much easier to ask people for EXPLICIT information. The time factor for the responder is still a problem. It may be easier to fill out a form online than on paper, but we all have less time now (more work or more distractions or both).

But the volume of IMPLICIT information has exploded. Tweets, links, clicks, diggs/likes, comments, ecommerce and so on all drive a massive increase in data. So today’s challenge is the interpretation of that data, turning raw data into actionable data.

This is where we see the innovation.

If you like the image, you can buy the badge here – the geekiest badge in your neighborhood?

“Data is the new oil” is a phrase that is becoming popular. It was started in a post by Michael Palmer for the ANA (Association of National Advertisers) back in 2006:

“Data is just like crude. It’s valuable, but if unrefined it cannot really be used. It has to be changed into gas, plastic, chemicals, etc to create a valuable entity that drives profitable activity; so must data be broken down, analyzed for it to have value.”

This is how we ended Part 1 of our look at Market Research as part of our Creative Destruction 7 Act Play series. It is not that it is becoming much easier to ask people for EXPLICIT information. The time factor for the responder is still a problem. It may be easier to fill out a form online than on paper, but we all have less time now (more work or more distractions or both).

But the volume of IMPLICIT information has exploded. Tweets, links, clicks, diggs/likes, comments, ecommerce and so on all drive a massive increase in data. So today’s challenge is the interpretation of that data, turning raw data into actionable data.

This is where we see the innovation.

If you like the image, you can buy the badge here – the geekiest badge in your neighborhood?

Market Research Or Business Intelligence or Marketing Automation?

Once again the Internet is taking a giant wrecking ball to categories that we used to think of as distinct.

At first it look as if there was not a lot of innovation in Market Research.

As Simon Kendrick notes in his post on “If data is the new oil, we need a bigger drill”:

“The research industry is particularly poor in making the most of available data.”

He goes on to describe in some detail why the “formal” research companies:

“are already losing ground to analytics in the online area, and this problem will only get worse unless action is taken.”

But that may be because the analysis has been appropriated by different departments/categories/segments (call them what you will).

These all look like 4 distinct market segments:

1. Market Research.

2. Business Intelligence.

3. Marketing Automation.

4. Web Analytics.

In large companies they are still probably distinct. There will be departments and VPs who zealously guard those departmental budgets. So change comes slowly to big companies, accelerating only when a crisis gives a leader the cover to make the changes that everybody knows are long overdue.

But no start-up would view those as distinct:

“Err, it’s all just data about our users that we use to drive our actions and create revenue”.

Connecting Dots Between “Market Research” And Web Analytics

Simon Kendrick’s post looks at why “market research” is missing in action. The section that resonates is where he talks about metadata:

“In online surveys, some metadata is collected but its uses tend to be related to data quality. Examples include

* Average length of questionnaire
* Ensuring there is a certain gap between survey invitations
* Measuring drop-out rates
* Removing respondents that continually “straight-line” or answer in obvious patterns

Yet there are many ways in which metadata can be used for analysis – either within the survey itself or for general knowledge on segmenting the types of panel users. Examples include

* Deriving geodemographic data from IP addresses
* Measuring word count and time spent answering open-ended questions
* Using time spent making decisions (such as in conjoint surveys) to calculate “velocity of opinion”*
* Segmenting those that tend to complete surveys shortly after the initial invitation into a “fast response” panel
* Calculating preferences from the order in which modular surveys are completed (though most surveys remain linear)”

Or, to put it in our semantic web terms: publish data as Linked Data and all this analysis becomes feasible.

“Data Is Not Insight”

That is a quote from Michael Palmer’s original post on “Data Is The New Oil“.

He gives some good examples of marketers that took one piece of data out of context and made serious mistakes.

Big data is not just more data. It is fundamentally different. It requires totally different tools. That is why we see the most important innovation in the semantic web coming from user experience designers. The phase of the semantic web we have been in for a long time is about building the infrastructure – getting the standards defined, building the tools, getting a critical mass of linked data out in the wild.

That phase won’t end. There will always be innovation at that level. But what really matters now are are the user experiences that enable people to gain insights from the data.

We are not seeing this coming out of the market research industry or the publishing industry. But we are seeing a lot of innovation in an adjacent area – Business Intelligence.

Semantic Business Intelligence?

Wikipedia has an entry on “Business Intelligence 2.0″ that defines it as:

“Business Intelligence 2.0 (BI 2.0) is a term that refers to new tools and software for business intelligence, beginning in the mid-2000s, that enable, among other things, dynamic querying of real-time corporate data by employees, and a more web- and browser-based approached to such data, as opposed to the proprietary querying tools that had characterized previous business intelligence software.

This change is partly due to the popularization of service-oriented architectures (SOA), which enables for a flexible, composable and adaptive middleware. Also, open standards for exchanging data such as XBRL (Extensible Business Reporting Language) and various Semantic Web ontologies enable using data external to an organization, such as benchmarking type information.

Business Intelligence 2.0 is most likely named after Web 2.0, although it takes elements from both Web 2.0 (a focus on user empowerment and community collaboration, technologies like RSS and the concept of mashups), and the Semantic Web, sometimes called “Web 3.0″ (semantic integration through shared ontologies to enable easier exchange of data).

According to analytics expert Neil Raden, BI 2.0 also implies a move away from the standard data warehouse that business intelligence tools have used, which “will give way to context, contingency and the need to relate information quickly from many sources.”

The seminal article was written by Neil Raden in 2005. This picture has the essence:

This will resonate with the semantic web community. A core assumption is that Linked Data is on different data stores – the idea of an enterprise data warehouse is meaningless in semantic web terms. Whether the data sources are properly tagged (the bottom up approach) or the existing unstructured mess is searched using top down methodologies does not matter. What matters is layers of tools and techniques for gaining insights from disparate sources of data.

Big enterprise vendors such as IBM are making major investments in Business Intelligence. We also see a lot of innovative SaaS startups in the BI market.

The BI companies that really leverage semantic web standards will emerge as winners. If companies publish their internal data as Linked Data, this can replace the old enterprise data warehouses with a more flexible, loosely coupled approach.

The market research industry appears to be left on the sidelines as this innovation rolls out.
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CONVERT BREAKS: __default__

Market Research Or Business Intelligence or Marketing Automation?

Once again the Internet is taking a giant wrecking ball to categories that we used to think of as distinct.

At first it look as if there was not a lot of innovation in Market Research.

As Simon Kendrick notes in his post on “If data is the new oil, we need a bigger drill”:

“The research industry is particularly poor in making the most of available data.”

He goes on to describe in some detail why the “formal” research companies:

“are already losing ground to analytics in the online area, and this problem will only get worse unless action is taken.”

But that may be because the analysis has been appropriated by different departments/categories/segments (call them what you will).

These all look like 4 distinct market segments:

1. Market Research.

2. Business Intelligence.

3. Marketing Automation.

4. Web Analytics.

In large companies they are still probably distinct. There will be departments and VPs who zealously guard those departmental budgets. So change comes slowly to big companies, accelerating only when a crisis gives a leader the cover to make the changes that everybody knows are long overdue.

But no start-up would view those as distinct:

“Err, it’s all just data about our users that we use to drive our actions and create revenue”.

Connecting Dots Between “Market Research” And Web Analytics

Simon Kendrick’s post looks at why “market research” is missing in action. The section that resonates is where he talks about metadata:

“In online surveys, some metadata is collected but its uses tend to be related to data quality. Examples include

* Average length of questionnaire
* Ensuring there is a certain gap between survey invitations
* Measuring drop-out rates
* Removing respondents that continually “straight-line” or answer in obvious patterns

Yet there are many ways in which metadata can be used for analysis – either within the survey itself or for general knowledge on segmenting the types of panel users. Examples include

* Deriving geodemographic data from IP addresses
* Measuring word count and time spent answering open-ended questions
* Using time spent making decisions (such as in conjoint surveys) to calculate “velocity of opinion”*
* Segmenting those that tend to complete surveys shortly after the initial invitation into a “fast response” panel
* Calculating preferences from the order in which modular surveys are completed (though most surveys remain linear)”

Or, to put it in our semantic web terms: publish data as Linked Data and all this analysis becomes feasible.

“Data Is Not Insight”

That is a quote from Michael Palmer’s original post on “Data Is The New Oil“.

He gives some good examples of marketers that took one piece of data out of context and made serious mistakes.

Big data is not just more data. It is fundamentally different. It requires totally different tools. That is why we see the most important innovation in the semantic web coming from user experience designers. The phase of the semantic web we have been in for a long time is about building the infrastructure – getting the standards defined, building the tools, getting a critical mass of linked data out in the wild.

That phase won’t end. There will always be innovation at that level. But what really matters now are are the user experiences that enable people to gain insights from the data.

We are not seeing this coming out of the market research industry or the publishing industry. But we are seeing a lot of innovation in an adjacent area – Business Intelligence.

Semantic Business Intelligence?

Wikipedia has an entry on “Business Intelligence 2.0″ that defines it as:

“Business Intelligence 2.0 (BI 2.0) is a term that refers to new tools and software for business intelligence, beginning in the mid-2000s, that enable, among other things, dynamic querying of real-time corporate data by employees, and a more web- and browser-based approached to such data, as opposed to the proprietary querying tools that had characterized previous business intelligence software.

This change is partly due to the popularization of service-oriented architectures (SOA), which enables for a flexible, composable and adaptive middleware. Also, open standards for exchanging data such as XBRL (Extensible Business Reporting Language) and various Semantic Web ontologies enable using data external to an organization, such as benchmarking type information.

Business Intelligence 2.0 is most likely named after Web 2.0, although it takes elements from both Web 2.0 (a focus on user empowerment and community collaboration, technologies like RSS and the concept of mashups), and the Semantic Web, sometimes called “Web 3.0″ (semantic integration through shared ontologies to enable easier exchange of data).

According to analytics expert Neil Raden, BI 2.0 also implies a move away from the standard data warehouse that business intelligence tools have used, which “will give way to context, contingency and the need to relate information quickly from many sources.”

The seminal article was written by Neil Raden in 2005. This picture has the essence:

This will resonate with the semantic web community. A core assumption is that Linked Data is on different data stores – the idea of an enterprise data warehouse is meaningless in semantic web terms. Whether the data sources are properly tagged (the bottom up approach) or the existing unstructured mess is searched using top down methodologies does not matter. What matters is layers of tools and techniques for gaining insights from disparate sources of data.

Big enterprise vendors such as IBM are making major investments in Business Intelligence. We also see a lot of innovative SaaS startups in the BI market.

The BI companies that really leverage semantic web standards will emerge as winners. If companies publish their internal data as Linked Data, this can replace the old enterprise data warehouses with a more flexible, loosely coupled approach.

The market research industry appears to be left on the sidelines as this innovation rolls out.
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• Don’t forget to propose your startup for our Semantic Web Impact Awards. The deadline is Sept. 15.

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