Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

November 10, 2013

Are You A Facebook Slacker? (Or, “Don’t “Like” Me, Support Me!”)

Filed under: Facebook,Marketing,Psychology,Social Media — Patrick Durusau @ 8:09 pm

Their title reads: The Nature of Slacktivism: How the Social Observability of an Initial Act of Token Support Affects Subsequent Prosocial Action by Kirk Kristofferson, Katherine White, John Peloza. (Kirk Kristofferson, Katherine White, John Peloza. The Nature of Slacktivism: How the Social Observability of an Initial Act of Token Support Affects Subsequent Prosocial Action. Journal of Consumer Research, 2013; : 000 DOI: 10.1086/674137)

Abstract:

Prior research offers competing predictions regarding whether an initial token display of support for a cause (such as wearing a ribbon, signing a petition, or joining a Facebook group) subsequently leads to increased and otherwise more meaningful contributions to the cause. The present research proposes a conceptual framework elucidating two primary motivations that underlie subsequent helping behavior: a desire to present a positive image to others and a desire to be consistent with one’s own values. Importantly, the socially observable nature (public vs. private) of initial token support is identified as a key moderator that influences when and why token support does or does not lead to meaningful support for the cause. Consumers exhibit greater helping on a subsequent, more meaningful task after providing an initial private (vs. public) display of token support for a cause. Finally, the authors demonstrate how value alignment and connection to the cause moderate the observed effects.

From the introduction:

We define slacktivism as a willingness to perform a relatively costless, token display of support for a social cause, with an accompanying lack of willingness to devote significant effort to enact meaningful change (Davis 2011; Morozov 2009a).

From the section: The Moderating Role of Social Observability: The Public versus Private Nature of Support:

…we anticipate that consumers who make an initial act of token support in public will be no more likely to provide meaningful support than those who engaged in no initial act of support.

Four (4) detailed studies and an extensive review of the literature are offered to support the author’s conclusions.

The only source that I noticed missing was:

10 Two men went up into the temple to pray; the one a Pharisee, and the other a publican.

11 The Pharisee stood and prayed thus with himself, God, I thank thee, that I am not as other men are, extortioners, unjust, adulterers, or even as this publican.

12 I fast twice in the week, I give tithes of all that I possess.

13 And the publican, standing afar off, would not lift up so much as his eyes unto heaven, but smote upon his breast, saying, God be merciful to me a sinner.

14 I tell you, this man went down to his house justified rather than the other: for every one that exalteth himself shall be abased; and he that humbleth himself shall be exalted.

King James Version, Luke 18: 10-14.

The authors would reverse the roles of the Pharisee and the publican, to find the Pharisee contributes “meaningful support,” and the publican has not.

We contrast token support with meaningful support, which we define as consumer contributions that require a significant cost, effort, or behavior change in ways that make tangible contributions to the cause. Examples of meaningful support include donating money and volunteering time and skills.

If you are trying to attract “meaningful support” for your cause or organization, i.e., avoid slackers, there is much to learn here.

If you are trying to move beyond the “cheap grace” (Bonhoeffer)* of “meaningful support” and towards “meaningful change,” there is much to be learned here as well.

Governments, corporations, ad agencies and even your competitors are manipulating the public understanding of “meaningful support” and “meaningful change.” And acceptable means for both.

You can play on their terms and lose, or you can define your own terms and roll the dice.

Questions?


* I know the phrase “cheap grace” from Bonhoeffer but in running a reference to ground, I saw a statement in Wikipedia that Bonhoeffer learned that phrase from Adam Clayton Powell, Sr.. Homiletics have never been a strong interest of mine but I will try to run down some sources on sermons by Adam Clayton Powell, Sr.

November 5, 2013

A Letter Regarding Native Graph Databases

Filed under: Graphs,Marketing — Patrick Durusau @ 5:17 pm

A Letter Regarding Native Graph Databases by Matthias Broecheler.

From the post:

It’s fun to watch marketers create artificial distinctions between products that grab consumer attention. One of my favorite examples is Diamond Shreddies. Shreddies, a whole wheat cereal, has a square shape and was always displayed as such. So an ingenious advertiser at Kraft foods thought to advertise a new and better Diamond Shreddies. It’s a fun twist that got people’s attention and some consumers even proclaimed that Diamond Shreddies tasted better though they obviously ate the same old product.

Such marketing techniques are also used in the technology sector — unfortunately, at a detriment to consumers. Unlike Kraft’s playful approach, there are technical companies that attempt to “educate” engineers on artificial distinctions as if they were real and factual. An example from my domain is the use of the term native graph database. I recently learned that one graph database vendor decided to divide the graph database space into non-native (i.e. square) and native (i.e. diamond) graph databases. Obviously, non-native is boring, or slow, or simply bad and native is exciting, or fast, or simply good.

Excellent push back against vendor hype on graph databases.

As well written as it is, people influenced by graph database hype are unlikely to read it.

I suggest you read it so you can double down if you encounter a graph fraudster.

False claims about graph databases benefits the fraudster at the expense of the paradigm.

That’s not a good outcome.

October 29, 2013

A Checklist for Creating Data Products

Filed under: Data,Marketing — Patrick Durusau @ 6:30 pm

A Checklist for Creating Data Products by Zach Gemignani.

From the post:

Are you are sitting on a gold mine — if only you could transform your unique data into a valuable, monetizable data product?

Over the years, we’ve worked with dozens of clients to create applications that refine data and package the results in a form users will love. We often talk with product managers early in the conception phase to help define the target market and end-user needs, even before designing interfaces for presenting and visualizing the data.

In the process, we’ve learned a few lessons and gather a bunch of useful resources. Download our Checklist for Product Managers of Data Solutions. It is divided into four sections:

  1. Audience: Understand the people who need your data
  2. Data: Define and enhance the data for your solution
  3. Design: Craft an application that solves problems
  4. Delivery: Transition from application to profitable product

Zach and friends have done a good job packing this one page checklist with helpful hints.

No turn-key solution to riches but may spark some ideas that will move you closer to a viable data product.

October 26, 2013

Pitch Advice For Entrepreneurs

Filed under: Funding,Marketing — Patrick Durusau @ 8:16 pm

Pitch Advice For Entrepreneurs: LinkedIn’s Series B Pitch to Greylock by Reid Hoffman.

From the post:

At Greylock, my partners and I are driven by one guiding mission: always help entrepreneurs. It doesn’t matter whether an entrepreneur is in our portfolio, whether we’re considering an investment, or whether we’re casually meeting for the first time.

Entrepreneurs often ask me for help with their pitch decks. Because we value integrity and confidentiality at Greylock, we never share an entrepreneur’s pitch deck with others. What I’ve honorably been able to do, however, is share the deck I used to pitch LinkedIn to Greylock for a Series B investment back in 2004.

This past May was the 10th anniversary of LinkedIn, and while reflecting on my entrepreneurial journey, I realized that no one gets to see the presentation decks for successful companies. This gave me an idea: I could help many more entrepreneurs by making the deck available not just to the Greylock network of entrepreneurs, but to everyone.

Today, I share the Series B deck with you, too. It has many stylistic errors — and a few substantive ones, too — that I would now change having learned more, but I realized that it still provides useful insights for entrepreneurs and startup participants outside of the Greylock network, particularly across three areas of interest:

  • how entrepreneurs should approach the pitch process
  • the evolution of LinkedIn as a company
  • the consumer internet landscape in 2004 vs. today

Read, digest, and then read again.

I first saw this in a tweet by Tim O’Reilly.

October 22, 2013

Healthcare.gov website ‘didn’t have a chance in hell’

Filed under: Health care,Marketing — Patrick Durusau @ 6:26 pm

Healthcare.gov website ‘didn’t have a chance in hell’ by Patrick Thibodeau.

From the post:

A majority of large IT projects fail to meet deadlines, are over budget and don’t make their users happy. Such is the case with Healthcare.gov.

The U.S. is now racing to fix Healthcare.gov, the Affordability Care Act (ACA) website that launched Oct 1, by bringing in new expertise to fix it.

Healthcare.gov’s problems include site availability due to excessive loads, incorrect data recording among other things.

President Barack Obama said Monday that there is “no excuse” for the problems at the site.

But his IT advisors shouldn’t be surprised — the success rate for large, multi-million dollar commercial and government IT projects is very low.

The Standish Group, which has a database of some 50,000 development projects, looked at the outcomes of multimillion dollar development projects and ran the numbers for Computerworld.

Of 3,555 projects from 2003 to 2012 that had labor costs of at least $10 million, only 6.4% were successful. The Standish data showed that 52% of the large projects were “challenged,” meaning they were over budget, behind schedule or didn’t meet user expectations. The remaining 41.4% were failures — they were either abandoned or started anew from scratch.

“They didn’t have a chance in hell,” said Jim Johnson, founder and chairman of Standish, of Healthcare.gov. “There was no way they were going to get this right – they only had a 6% chance,” he said.

There is one reason that wasn’t offered for the Healthcare.gov failure.

Let me illustrate that reason.

In the Computer World article I quoted above, the article mentions the FBI tanking the $170 million virtual case initiative.

Contractor: SAIC.

Just last month I saw this notice:

XXXXXXXXX, San Diego, Calif., was awarded a $35,883,761 cost-plus-incentive-fee contract for software engineering, hardware, integration, technical support, and training requirements of the Integrated Strategic Planning and Analysis Network targeting function, including the areas of National Target Base production and National Desired Ground Zero List development. Work is being performed at Offut Air Force Base, Neb., with an expected completion date of Sept. 30 2018. This contract was a competitive acquisition and two offers were received. No funds have been obligated at time of award. The 55th Contracting Squadron at Offut Air Force Base, Neb., is the contracting activity. (FA4600-13-D-0001) [From Defense News and Career Advice, September 19, 2013.]

Can you guess who the contractor is in that $35 million award?

If you guessed SAIC, you would be correct!

Where is the incentive to do a competent job on any contract?

If you fail on a government contract, you get to keep the money.

Not to mention that you are still in line for more $multi-million dollar contracts.

I’m not on that gravy train but I don’t think that is what bothers me.

Doing poor quality work, in software projects or anywhere else, diminishes all the practitioners in a particular profession.

The first step towards a solution is for government and industry to stop repeating business with software firms that fail.

If smaller firms can’t match the paperwork/supervision required by layers of your project management, that’s a clue you need to do internal house cleaning.

Remember the quote about what is defined by doing the same thing over and over and expecting a different result?

October 18, 2013

Semantic Web adoption and the users

Filed under: Marketing,Semantic Web — Patrick Durusau @ 6:58 pm

Semantic Web adoption and the users by Lars Marius Garshol.

From the post:

A hot topic at ESWC 2013, and many other places besides, was the issue of Semantic Web adoption, which after a decade and a half is still less than it should be. The thorny question is: what can be done about it? David Karger did a keynote on the subject at ESWC 2013 where he argued that the Semantic Web can help users manage their data. I think he’s right, but that this is only a very narrow area of application. In any case, end users are not the people we should aim for if adoption of Semantic Web technologies is to be the goal.

End users and technology

In a nutshell, end users do not adopt technology, they choose tools. They find an application they think solves their problem, then buy or install that. They want to keep track of their customers, so they buy a CRM tool. What technology the tool is based on is something they very rarely care about, and rightly so, as it’s the features of the tool itself that generally matters to them.

Thinking about comparable cases may help make this point more clearly. How did relational databases succeed? Not by appealing to end users. When you use an RDBMS-based application today, are users aware what’s under the hood? Very rarely. Similarly with XML. At heart it’s a very simple technology, but even so it was not end users who bought into it, but rather developers, consultants, software vendors, and architects.

If the Semantic Web technologies ever succeed, it will be by appealing to the same groups. Unfortunately, the community is doing a poor job of that now.

Lars describes the developer community as being a hard sell for technology, in part because it is inherently conservative.

But what is the one thing that both users and developers have in common?

Would you say that both users and developers are lazy?

Semantic technologies of all types, take more effort, more thinking, than the alternatives. Even rote tagging takes some effort. Useful tagging/annotation takes a good bit more.

Is adoption of semantic technologies or should I say non-adoption of semantic technologies, another example of Kahneman’s System 1?

You may recall the categories are:

  • System 1: Fast, automatic, frequent, emotional, stereotypic, subconscious
  • System 2: Slow, effortful, infrequent, logical, calculating, conscious

In the book, Thinking, Fast and Slow, Kahneman makes a compelling case that without a lot of effort, we all tend to lapse into System 1.

If that is the case, Lars’s statement:

[Users] find an application they think solves their problem, then buy or install that.

could be pointing us in the right direction.

Users aren’t interested in building a solution (all semantic technologies) but are interested in buying a solution.

By the same token:

Developers aren’t interested in understanding and documenting semantics.

All of which makes me curious:

Why do semantic technology advocates resist producing semantic products of interest to users or developers?

Or is producing a semantic technology easier than producing a semantic product?

October 14, 2013

Findability As Value Proposition

Filed under: Marketing,Topic Maps — Patrick Durusau @ 10:33 am

Seth Maislin has some interesting statistics on findability as a value proposition:

Findability is something most people are willing to pay for. One industry estimate suggests that 14% of our workdays are spent looking for information; others say it’s more like 23%, 25%, 30%, or even 35%. IBM suggests that 42% of people use wrong information to make decisions, while IDC suggests that 40% of corporate users can’t find the information they need at all – and that 50% of intranet searches are abandoned. This is the world into which every document is born. Improving findability with a user-focused information strategy can give all of your documents a huge boost in value – or, if you prefer, those few documents you think deserve special treatment. Remember: If you can’t find it, you might as well not have it. (From: Improving the Value of Fixed Content.

I rather like his conclusion:

“Remember: If you can’t find it, you might as well not have it.”

To make the numbers more concrete, chart your prospective client’s payroll hours X 14%, 23%, 25%, 30% and 35%.

That should be a real eye opener!

The survey I have not seen though is one that tracks how many employees are searching for the same information.

A “many employees searching for the same information” number would be valuable for two reasons:

  1. It would quantify how much duplicate search effort a topic map could eliminate, and
  2. The information area they are searching would be the logical focus of topic mapping efforts. Why topic map ten year old corporate minutes that no one ever searches for?

Seth mentions a webinar on precise search results:

“Attend our upcoming webinar on October 23, Driving Knowledge-Worker Performance with Precision Search Results, which is likely to address many of these ideas!”

The webinar is described as:

Intended for a non-technical audience, this webinar will focus on how to identify and prioritize where these solutions can deliver value.

If nothing else, you may pick up some good examples and rhetoric on the value of better search capabilities.

October 11, 2013

What’s Your Elevator Speech for Topic Maps?

Filed under: Marketing,Topic Maps — Patrick Durusau @ 6:01 pm

I ask because Sam Hunting uncovered and posted a note about: How to Write an Elevator Pitch by Babak Nivi.

See the post for the template but be mindful of this comment from the post:

Your e-mail should be no longer than this example, which is already too long. Challenge yourself to keep the pitch under 100 words. And keep the product description brief — this pitch describes the product in one paragraph with 29 words.

100 words!? I don’t think I can introduce Steve Newcomb in less than 100 words. 😉

I don’t have a 100 word topic map pitch, at least not yet.

Look for an early cut on one sometime next week.

Start polishing yours because that is going to be my next question.

September 26, 2013

Big data and the “Big Lie”:…

Filed under: BigData,Marketing — Patrick Durusau @ 7:13 pm

Big data and the “Big Lie”: the challenges facing big brand marketers by Renee DiResta.

We’ve talked about the NSA and others gathering data like it is meaningful. Renee captures another point of inaccuracy in data:

However, the flip side of “social” is what’s come to be called The Big Lie: “the gap between social norm and private reality, between expressed opinions and inner motions.” We ensure that our Facebook and LinkedIn profiles present us in our best light. Our shared audio playlists highlight the artists we’re proud to call ourselves fans of — and conceal the mass-market pop that we actually listen to when we’re alone. We use Instagram to share our most gourmet dining experiences, not our Oreo habit. There’s an important distinction between user-generated data and user-volunteered data. Targeting someone using data they generated but did not volunteer can put a brand squarely into the “creepy” zone.

To emphasize the critical point:

“the gap between social norm and private reality, between expressed opinions and inner motions.”

Although that doesn’t account for self-deception/delusion, which I suspect operates a good deal of the time.

You need only to watch the evening news to see allegedly competent people saying things that are inconsistent with commonly shared views of reality.

I think Renee’s bottom line is that turning the crank on “big data” isn’t going to result in sales. It’s a bit harder than that.

See Renee’s post for more details.

September 19, 2013

Pixar’s 22 Rules of Storytelling

Filed under: Communication,Marketing — Patrick Durusau @ 6:26 pm

Pixar’s 22 Rules of Storytelling by DinoIgnacio.

From the webpage:

Former Pixar story artist Emma Coats tweeted this series of “story basics” in 2011. https://twitter.com/lawnrocket These were guidelines that she learned from her more senior colleagues on how to create appealing stories. I superimposed all 22 rules over stills from Pixar films to help me remember them. All Disney copyrights, trademarks, and logos are owned by The Walt Disney Company.

If you find the rules hard to read with the picture backgrounds (I do), see the text version: http://imgur.com/a/MRfTb.

While cast as rules for “storytelling,” these are rules for effective communication in any context.

September 10, 2013

How To Capitalize on Clickstream data with Hadoop

Filed under: Hadoop,Marketing — Patrick Durusau @ 10:17 am

How To Capitalize on Clickstream data with Hadoop by Cheryle Custer.

From the post:

In the last 60 seconds there were 1,300 new mobile users and there were 100,000 new tweets. As you contemplate what happens in an internet minute Amazon brought in $83,000 worth of sales. What would be the impact of you being able to identify:

  • What is the most efficient path for a site visitor to research a product, and then buy it?
  • What products do visitors tend to buy together, and what are they most likely to buy in the future?
  • Where should I spend resources on fixing or enhancing the user experience on my website?

In the Hortonworks Sandbox, you can run a simulation of website Clickstream behavior to see where users are located and what they are doing on the website. This tutorial provides a dataset of a fictitious website and the behavior of the visitors on the site over a 5 day period. This is a 4 million line dataset that is easily ingested into the single node cluster of the Sandbox via HCatalog.

The first paragraph is what I would call an Economist lead-in. It captures your attention:

…60 seconds…1300 new mobile users …100,000 new tweets. …minute…Amazon…$83,000…sales.

If the Economist is your regular fare, your pulse rate went up at “1300 new mobile users” and by the minute/$83,000 you started to tingle. 😉

How to translate that for semantic technologies in general and topic maps in particular?

Remember The Monstrous Cost of Work Failure graphic?

Where we read that 58% of employees spend one-half of a workday “filing, deleting, or sorting information.”

Just to simplify the numbers, one-quarter (1/4) of your total workforce hours are spent on “filing, deleting, or sorting information.”

Divide your current payroll figure by four (4).

Does that result get your attention?

If not, call emergency services. You are dead or having a medical crisis.

Use that payroll division as:

A positive, topic maps can help you recapture some of that 1/4 of your payroll, or

A negative, topic maps can help you stem the bleeding from non-productive activity,

depending on which will be more effective with a particular client.

BTW, do read Cheryle’s post.

Hadoop’s capabilities are more limited by your imagination than any theoretical CS limit.

September 9, 2013

The Monstrous Cost of Work Failure

Filed under: Marketing,Project Management,Topic Maps — Patrick Durusau @ 3:34 pm

Failure Infographic

I first saw this posted by Randy Krum.

The full sized infographic at AtTask.

Would you care to guess what accounts for 60% to 80% of project failures?

According to the ASAPM (American Society for the Advancement of Project Management):

According to the Meta Group, 60% – 80% of project failures can be attributed directly to poor requirements gathering, analysis, and management. (emphasis added)

Requirements, what some programmers are too busy coding to collect and some managers fear because of accountability.

Topic maps can’t solve your human management problems.

Topic maps can address:

  • Miscommunication between business and IT – $30 Billion per year
  • 58% of workers spending half of each workday, filing, deleting, sorting information

Reducing information shuffling is like adding more staff for the same bottom line.

Interested?

September 8, 2013

Think Big… Right Start Big Data Projects [Religion]

Filed under: Marketing,Topic Maps — Patrick Durusau @ 4:45 pm

Think Big… Right Start Big Data Projects by Rod Bodkin (Think Big Analytics).

Rod lists three “Must Dos:”

  1. Test and learn
  2. Incremental adoption
  3. Change management

I won’t try to summarize Rod’s points. You will be better off reading the original post.

I would this point: “Leave your technology religion at the door.”

Realize most customers have no religious convictions about software. And they are not interested in having religious convictions about software or knowing yours.

You may well be convinced your software or approach will be the salvation of the human race, solar system or even the galaxy.

My suggestion is you keep that belief to yourself. Unless your client wants world salvation rhetoric for fund raising or some other purpose.

Clients, from your local place of worship to Wall Street, from the NSA to the KGB, and everywhere in between have some need other than paying you for your products or services.

The question you need to answer is how does your product or service met that need?

August 31, 2013

Do You Mansplain Topic Maps?

Filed under: Data Science,Marketing,Topic Maps — Patrick Durusau @ 3:54 pm

Selling Data Science: Common Language by Sean Gonzalez.

From the post:

What do you think of when you say the word “data”? For data scientists this means SO MANY different things from unstructured data like natural language and web crawling to perfectly square excel spreadsheets. What do non-data scientists think of? Many times we might come up with a slick line for describing what we do with data, such as, “I help find meaning in data” but that doesn’t help sell data science. Language is everything, and if people don’t use a word on a regular basis it will not have any meaning for them. Many people aren’t sure whether they even have data let alone if there’s some deeper meaning, some insight, they would like to find. As with any language barrier the goal is to find common ground and build from there.

You can’t blame people, the word “data” is about as abstract as you can get, perhaps because it can refer to so many different things. When discussing data casually, rather than mansplain what you believe data is or what it could be, it’s much easier to find examples of data that they are familiar with and preferably are integral to their work. (emphasis added)

Well? Your answer here:______.

Let’s recast that last clause to read:

…it’s much easier to find examples of subjects they are familiar with and preferably are integral to their work.

So that the conversation is about their subjects and what they want to say about them.

As a potential customer, I would find that more compelling.

You?

August 28, 2013

the BOMB in the GARDEN

Filed under: Marketing,W3C,WWW — Patrick Durusau @ 6:17 pm

the BOMB in the GARDEN by Matthew Butterick.

From the post:

It’s now or nev­er for the web. The web is a medi­um for cre­ators, in­clud­ing de­sign­ers. But af­ter 20 years, the web still has no cul­ture of de­sign ex­cel­lence. Why is that? Because de­sign ex­cel­lence is in­hib­it­ed by two struc­tur­al flaws in the web. First flaw: the web is good at mak­ing in­for­ma­tion free, but ter­ri­ble at mak­ing it ex­pen­sive. So the web has had to rely large­ly on an ad­ver­tis­ing econ­o­my, which is weak­en­ing un­der the strain. Second flaw: the process of adopt­ing and en­forc­ing web stan­dards, as led by the W3C, is hope­less­ly bro­ken. Evidence of both these flaws can be seen in a) the low de­sign qual­i­ty across the web, and b) the speed with which pub­lish­ers, de­vel­op­ers, and read­ers are mi­grat­ing away from the web, and to­ward app plat­forms and me­dia plat­forms. This ev­i­dence strong­ly sug­gests that the web is on its way to be­com­ing a sec­ond-class plat­form. To ad­dress these flaws, I pro­pose that the W3C be dis­band­ed, and that the lead­er­ship of the web be re­or­ga­nized around open-source soft­ware prin­ci­ples. I also en­cour­age de­sign­ers to ad­vo­cate for a bet­ter web, lest they find them­selves confined to a shrink­ing ter­ri­to­ry of possibilities.

Apologies to Matthew for my mangling of the typography of his title.

This rocks!

This is one of those rare, read this at least once a month posts.

That is if you want to see a Web that supports high quality design and content.

If you like the current low quality, ad driven Web, just ignore it.

August 27, 2013

Selling Data Science [Topic Maps]

Filed under: Marketing,Topic Maps — Patrick Durusau @ 7:36 pm

Selling Data Science by Sean Gonzalez.

From the post:

Data Science is said to include statisticians, mathematicians, machine learning experts, algorithm experts, visualization ninjas, etc., and while these objective theories may be useful in recognizing necessary skills, selling our ideas is about execution. Ironically there are plenty of sales theories and guidelines, such as SPIN selling, the iconic ABC scene from boiler room, or my personal favorite from Glengarry Glenross, that tell us what we should be doing, what questions we should be asking, how a sale should progress, and of course how to close, but none of these address the thoughts we may be wrestling with as we navigate conversations. We don’t necessarily mean to complicate things, we just become accustomed to working with other data science types, but we still must reconcile how we communicate with our peers versus people in other walks of life who are often geniuses in their own right.

First in what Sean promises is a series of posts on how to sell data science.

I am sure the lessons will be equally applicable to selling topic maps.

I am not expecting magic bullets but it is a series of posts that I will follow.

You?

August 20, 2013

The Curse of Enterprise Search… [9% Solutions]

Filed under: Marketing,Search Requirements,Searching — Patrick Durusau @ 2:23 pm

The Curse of Enterprise Search and How to Break It by Maish Nichani.

From the post:

The Curse

Got enterprise search? Try answering these questions: Are end users happy? Has decision-making improved? Productivity up? Knowledge getting reused nicely? Your return-on-investment positive? If you’re finding it tough to answer these questions then most probably you’re under the curse of enterprise search.

The curse is cast when you purchase an enterprise search software and believe that it will automagically solve all your problems the moment you switch it on. You believe that the boatload of money you just spent on it justifies the promised magic of instant findability. Sadly, this belief cannot be further from the truth.

Search needs to be designed. Your users and content are unique to your organisation. Search needs to work with your users. It needs to make full use of the type of content you have. Search really needs to be designed.

Don’t believe in the curse? Consider these statistics from the Enterprise Search and Findability Survey 2013 done by Findwise with 101 practitioners working for global companies:

  • Only 9% said it was easy to find the right information within the organisation
  • Only 19% said they were happy with the existing search application in their organisation
  • Only 20% said they had a search strategy in place

Just in case you need some more numbers when pushing your better solution to enterprise search.

I wonder how search customers would react to an application that made it easy to find the right data 20% of the time?

Just leaving room for future versions and enhancements. 😉

Maish isn’t handing out silver bullets but a close read will improve your search application (topic map or not).

August 15, 2013

Five tips for Delivering a Presentation

Filed under: Communication,Marketing — Patrick Durusau @ 3:29 pm

Five tips for Delivering a Presentation by Hugh E. Williams.

From the post:

I wrote a few weeks ago on writing a presentation. This week, I offer a few thoughts on delivering one – in no particular order. I’m working on my sequel to my post on performance reviews — expect it next week!

Hugh covers:

  1. Eye Contact
  2. Body Language
  3. Don’t Read Notes (or Memorize)
  4. Don’t Read Slides
  5. It’s (almost) Impossible to Speak Too Slowly

Same top tips that we covered in my first speech class in high school.

Well, except for the one about reading slides. 😉 It would have been:

Don’t Read the Overhead Slides.

Technology has changed but poor presenting has not.

The same is true for poor sales technique.

Such as trying to sell customers what you are interested in selling, not what the customer is interested in buying.

That sounds like a bad plan to me.

August 12, 2013

How videos go viral on Twitter – Three stories

Filed under: Advertising,Marketing,Topic Maps — Patrick Durusau @ 3:35 pm

How videos go viral on Twitter – Three stories by Gordon MacMillan.

From the post:

What is it that makes videos go viral? It is one of the big questions in digital marketing. While there is no single magic formula, we’ve come up with some key insights after tracking the stories behind three recent viral videos.
(…)

  1. Twitter users love video
  2. Videos are easily shareable
  3. Promoted products amplify your reach
  4. Get creative with Vine

See Gordon’s post for the details. Although I warn you up front that there is no special sauce that makes a video go viral.

What would you show about topic maps in six seconds?

August 3, 2013

Semantic Search… [Call for Papers]

Filed under: Marketing,Publishing,Semantics,Topic Maps — Patrick Durusau @ 3:52 pm

Semantic Search – Call for Papers for special issue of Aslib Journal of Information Management by Fran Alexander.

From the post:

I am currently drafting the Call for Papers for a special issue of the Aslib Journal of Information Management (formerly Aslib Proceedings) which I am guest editing alongside Dr Ulrike Spree from the University of Hamburg.

Ulrike is the academic expert, while I am providing the practitioner perspective. I am very keen to include practical case studies, so if you have an interesting project or comments on a project but have never written an academic paper before, don’t be put off. I will be happy to advise on style, referencing, etc.

Suggested Topics

Themes Ulrike is interested in include:

  • current trends in semantic search
  • best practice – how far along the road from ‘early adopters’ to ‘mainstream users’ has semantic search gone so far
  • usability of semantic search
  • visualisation and semantic search
  • the relationship between new trends in knowledge organisation and semantic search, such as vocabulary norms (like ISO 25964 “Thesauri for information retrieval“) and the potential of semantic search from a more critical perspective – what, for example, are the criteria for judging quality?

Themes I am interested in include:

  • the history of semantic search – how the latest techniques and technologies have come out of developments over the last 5, 10, 20, 100, 2000… years
  • how semantic search techniques and technologies are being used in practice
  • how semantic technologies are fostering a need for cross-industry collaboration and standardization
  • practical problems in brokering consensus and agreement – defining terms and classes, etc.
  • differences between web-scale, enterprise scale, and collection-specific scale techniques
  • curation and management of ontologies.

However, we are open to suggestions, especially as it is such a broad topic, there are so many aspects that could be covered.

Fran doesn’t mention a deadline but I will ask and update here when I get it.

Sounds like a venue that would welcome papers on topic maps.

Yes?

Big Data and Influencers [Top 200 Influencers]

Filed under: BigData,Marketing — Patrick Durusau @ 3:40 pm

Big Data and Influencers by Flemming Madsen.

From the post:

Big Data is a hot topic and I have looked at overall influencers on this topic before here and here.

If you are a vendor in the Big Data market place you are probably either already running influencer programs or strongly considering it.

The Big Data market place is quite complex with many vendors and a huge number of complex issues to digest and take into account. It’s a classic example where influencers, including analysts, journalists, bloggers, commentators and technical evangelists are influencing the awareness and viewpoints of prospective clients.

However, if you want to run an influencer program on Big Data, how do you go about it?

First you need to identify who are influential in the debate on Big Data.

(…)

Partially a promo for an analytics product but it also includes a list of the top 200 big data influencers.

Could be useful if you need to nudge the rhetoric one way or the other.

Such as asking where are semantics in the “big data” tools?

July 30, 2013

Turning visitors into sales: seduction vs. analytics

Filed under: Analytics,Marketing — Patrick Durusau @ 3:00 pm

Turning visitors into sales: seduction vs. analytics by Mirko Krivanek.

From the post:

The context here is about increasing conversion rate, from website visitor to active, converting user. Or from passive newsletter subscriber to a lead (a user who opens the newsletter, clicks on the links, and converts). Here we will discuss the newletter conversion problem, although it applies to many different settings.

flower

Of course, to maximize the total number of leads (in any situation), you need to use both seduction and analytics:

sales = f(seduction, analytics, product, price, competition, reputation)

How to assess the weight attached to each factor in the above formula, is beyond the scope of this article. First, even measuring “seduction” or “analytics” is very difficult. But you could use a 0-10 scale, with seduction = 9 representing a company doing significant efforts to seduce prospects, and analytics = 0 representing a company totally ignoring analytics.

I did not add a category for “seduction.” Perhaps if someone writes a topic map on seduction I will. 😉

Mirko’s seduction vs. analytics resonates with Kahneman’s fast versus slow thinking.

“Fast” thinking takes less effort by a reader and “slow” thinking takes more.

Forcing your readers to work harder, for marketing purposes, sounds like a bad plan to me.

“Fast”/seductive thinking should be the goal of your marketing efforts.

July 23, 2013

I’m Feeling Lucky

Filed under: Marketing,Topic Maps — Patrick Durusau @ 4:04 pm

You have to feel lucky to use an Internet search engine or play the lottery.

Similar activities.

With the lottery, you and the lottery invest very little effort in the result, but you are hopeful of a high payoff.

With an Internet search, you and the search provider invest very little in semantics, but you hope for a “smoking gun” result for your search.

“Something for nothing” is a popular wish but it is just that, a wish.

Not to say some searches do turn up “the” best answer. But then some people do win the lottery.

Not to mention it is hard to not “win” with a search. Even an obscure one will (reported to the user) result in several hundred thousand search “hits.”

See how good you are! How much information there is on your subject! But nobody ever looks at the 100,000th “hit” to see.

If you do “win” a search, by finding the perfect answer/data/document, do you keep it where others can find it?

Or do you toss it back into the sea of data for someone else to be lucky enough to find?

Is your retirement plan based on playing the lottery?

If not, why is your business plan based on Internet searches?

July 15, 2013

Corporate Culture Clash:…

Filed under: Communication,Diversity,Heterogeneous Data,Language,Marketing,Semantics — Patrick Durusau @ 3:05 pm

Corporate Culture Clash: Getting Data Analysts and Executives to Speak the Same Language by Drew Rockwell

From the post:

A colleague recently told me a story about the frustration of putting in long hours and hard work, only to be left feeling like nothing had been accomplished. Architecture students at the university he attended had scrawled their frustrations on the wall of a campus bathroom…“I wanted to be an architect, but all I do is create stupid models,” wrote students who yearned to see their ideas and visions realized as staples of metropolitan skylines. I’ve heard similar frustrations expressed by business analysts who constantly face the same uphill battle. In fact, in a recent survey we did of 600 analytic professionals, some of the biggest challenges they cited were “getting MBAs to accept advanced methods”, getting executives to buy into the potential of analytics, and communicating with “pointy-haired” bosses.

So clearly, building the model isn’t enough when it comes to analytics. You have to create an analytics-driven culture that actually gets everyone paying attention, participating and realizing what analytics has to offer. But how do you pull that off? Well, there are three things that are absolutely critical to building a successful, analytics-driven culture. Each one links to the next and bridges the gap that has long divided analytics professionals and business executives.

Some snippets to attract you to this “must read:”

(…)
In the culinary world, they say you eat with your eyes before your mouth. A good visual presentation can make your mouth water, while a bad one can kill your appetite. The same principle applies when presenting data analytics to corporate executives. You have to show them something that stands out, that they can understand and that lets them see with their own eyes where the value really lies.
(…)
One option for agile integration and analytics is data discovery – a type of analytic approach that allows business people to explore data freely so they can see things from different perspectives, asking new questions and exploring new hypotheses that could lead to untold benefits for the entire organization.
(…)
If executives are ever going to get on board with analytics, the cost of their buy-in has to be significantly lowered, and the ROI has to be clear and substantial.
(…)

I did pick the most topic map “relevant” quotes but they are as valid for topic maps as any other approach.

Seeing from different perspectives sounds like on-the-fly merging to me.

How about you?

July 9, 2013

How To Unlock Business Value from your Big Data with Hadoop

Filed under: BigData,Hadoop,Marketing,Topic Maps — Patrick Durusau @ 6:36 pm

How To Unlock Business Value from your Big Data with Hadoop by Jim Walker.

From the post:

By now, you’re probably well aware of what Hadoop does: low-cost processing of huge amounts of data. But more importantly, what can Hadoop do for you?

We work with many customers across many industries with many different specific data challenges, but in talking to so many customers, we are also able to see patterns emerge on certain types of data and the value that could bring to a business.

We love to share these kinds of insights, so we built a series of video tutorials covering some of those scenarios:

The tutorials cover social media, server logs, clickstream data, geolocation data, and others.

This is a brilliant marketing move.

Hadoop may be the greatest invention since sliced bread but if it isn’t shown to help you, what good is it?

These tutorials answer that question for several different areas of potential customer interest.

We should do something very similar for topic maps.

Something that focuses on a known need or interest of customers.

July 8, 2013

…Creating Content That Gets Shared

Filed under: Marketing,Topic Maps — Patrick Durusau @ 3:44 pm

Social Media and Storytelling, Part 3: Creating Content That Gets Shared by Cameron Uganec.

From the post:

In the previous posts, I explored how social media and storytelling can be used by marketers to engage with an audience and build relationships. It turns out that there is another benefit to following a brand storytelling approach; it can increase the shareability of your content. In fact the potential to build relationships coupled with the “viral effect” is what makes storytelling and social media powerful tools for marketers.

My team creates a lot of content. Our content marketing takes many forms: Tweets, Facebook posts, contributed articles, infographics, videos, blog posts etc. In order to unlock the potential value of the ‘earned media’ component of social media we endeavour to make every piece of content shareable. So it’s important that we understand why people share content.

Why People Share

The NYTimes Insights Group published a study that looked at the key factors that influence people to share content. Unsurprisingly, they discovered that sharing is all about relationships. They outlined these key motivations for people to share:

  • To bring valuable and entertaining content to others.
  • To define ourselves to others.
  • To grow and nourish relationships.
  • To get the word out about causes and brands I care about.

When you are creating content it’s important to be mindful of what the motivation of your audience is. When planning each piece of content our team answers these questions:

  • How does this add value for our audience?
  • How will this help or entertain them?
  • Why will they share it?

(…)

I am going to pick up the prior posts in this series and suggest that you do the same.

At least if you are interested in other people marketing your product, services, topic maps for you.

Sounds like a good deal to me.

Other posts in this series:

Social Media and Storytelling, Part 1: Why Storytelling?

Social Media and Storytelling, Part 2: Back to the Future

Social Media and Storytelling, Part 3: Creating Content That Gets Shared (subject of this post)

Social Media + Storytelling = Awesomesauce (presentation by Cameron at Marketo’s 2013 Summit Conference in San Francisco)

June 27, 2013

Getting $erious about $emantics

Filed under: Finance Services,Marketing,Semantics — Patrick Durusau @ 6:31 pm

State Street’s Chief Scientist on How to Tame Big Data Using Semantics by Bryan Yurcan.

From the post in Bank Systems & Technology:

Financial institutions are accumulating data at a rapid pace. Between massive amounts of internal information and an ever-growing pool of unstructured data to deal with, banks’ data management and storage capabilities are being stretched thin. But relief may come in the form of semantic databases, which could be the next evolution in how banks manage big data, says David Saul, Chief Scientist for Boston-based State Street Corp.

The semantic data model associates a meaning to each piece of data to allow for better evaluation and analysis, Saul notes, adding that given their ability to analyze relationships, semantic databases are particularly well-suited for the financial services industry.

“Our most important asset is the data we own and the data we act as a custodian for,” he says. “A lot of what we do for our customers, and what they do with the information we deliver to them, is aggregate data from different sources and correlate it to make better business decisions.”

Semantic technology, notes Saul, is based on the same technology “that all of us use on the World Wide Web, and that’s the concept of being able to hyperlink from one location to another location. Semantic technology does the same thing for linking data.”

Using a semantic database, each piece of data has a meaning associated with it, says Saul. For example, a typical data field might be a customer name. Semantic technology knows where that piece of information is in both the database and ununstructured data, he says. Semantic data would then allow for a financial institutions to create a report or dashboard that shows all of their interactions with that customer.

“The way it’s done now, you write data extract programs and create a repository,” he says. “There’s a lot of translation that’s required.”

Semantic data can also be greatly beneficial for banks in conducting risk calculations for regulatory requirements, Saul adds.

“That is something regulators are constantly looking for us to do, they want to know what our total exposure is to a particular customer or geographic area,” he says. “That requires quite a bit of development effort, which equals time and money. With semantic technology, once you describe the data sources, you can do that very, very quickly. You don’t have to write new extract programs.”

(…)

When banks and their technology people start talking about semantics, you know serious opportunities abound.

A growing awareness of the value of the semantics of data and data structures can’t help but create market opportunities for topic maps.

Big data needs big semantics!

June 26, 2013

Better Content on Memory Stick?

Filed under: Hadoop,Hortonworks,Marketing — Patrick Durusau @ 9:40 am

Sandbox on Memory Stick (pic)

There was talk over at LinkedIn about marketing for topic maps.

Here’s an idea.

No mention of topic maps on the outside but without an install, configuring paths, etc. the user gets a topic map engine plus content.

Topical content for the forum where the sticks are being distributed.

Plug and compare results to your favorite search sewer.

Limited range of data.

But if I am supposed to be searching SEC mandated financial reports and related data, not being able to access Latvian lingerie ads is probably ok. With management at least.*

I first saw this in a tweet by shaunconnolly.

Suggestions for content?


* Just an aside but curated content could provide not only better search results but also eliminate results that may distract staff from the task at hand.

Better than filters, etc. Other content would simply not be an option.

June 25, 2013

The Problem with RDF and Nuclear Power

Filed under: Marketing,RDF,Semantic Web — Patrick Durusau @ 3:09 pm

The Problem with RDF and Nuclear Power by Manu Sporny.

Manu starts his post:

Full disclosure: I am the chair of the RDFa Working Group, the JSON-LD Community Group, a member of the RDF Working Group, as well as other Semantic Web initiatives. I believe in this stuff, but am critical about the path we’ve been taking for a while now.

(…)

RDF shares a number of these similarities with nuclear power. RDF is one of the best data modeling mechanisms that humanity has created. Looking into the future, there is no equally-powerful, viable alternative. So, why has progress been slow on this very exciting technology? There was no public mis-information campaign, so where did this negative view of RDF come from?

In short, RDF/XML was the Semantic Web’s 3 Mile Island incident. When it was released, developers confused RDF/XML (bad) with the RDF data model (good). There weren’t enough people and time to counter-act the negative press that RDF was receiving as a result of RDF/XML and thus, we are where we are today because of this negative perception of RDF. Even Wikipedia’s page on the matter seems to imply that RDF/XML is RDF. Some purveyors of RDF think that the public perception problem isn’t that bad. I think that when developers hear RDF, they think: “Not in my back yard”.

The solution to this predicament: Stop mentioning RDF and the Semantic Web. Focus on tools for developers. Do more dogfooding.

Over the years I have become more and more agnostic towards data models.

The real question for any data model is whether it fits your requirements. What other test would you have?

For merging data held in different data models or data models that don’t recognize the same subject identified differently, then subject identity and its management comes into play.

Subject identity and its management not being an area that has only one answer for any particular problem.

Manu does have concrete suggestions for how to advance topic maps, either as a practice of subject identity or a particular data model:

  1. The message shouldn’t be about the technology. It should be about the problems we have today and a concrete solution on how to address those problems.
  2. Demonstrate real value. Stop talking about the beauty of RDF, theoretical value, or design. Deliver production-ready, open-source software tools.
  3. Build a network of believers by spending more of your time working with Web developers and open-source projects to convince them to publish Linked Data. Dogfood our work.

A topic map version of those suggestions:

  1. The message shouldn’t be about the technology. It should be about the problems we have today and a concrete solution on how to address those problems.
  2. Demonstrate real value. Stop talking about the beauty of topic maps, theoretical value, or design. Deliver high quality content from merging diverse data sources. (Tools will take care of themselves if the content is valuable enough.)
  3. Build a network of customers by spending more of your time using topic maps to distinguish your content from content from the average web sewer.

As an information theorist I should be preaching to myself. Yes?

😉

As the semantic impedance of the “Semantic Web,” “big data,” “NSA Data Cloud,” increases, the opportunities for competitive, military, industrial advantage from reliable semantic integration will increase.

Looking for showcase opportunities.

Suggestions?

June 18, 2013

Twitter Analytics Platform Gives Data Back to Users

Filed under: Marketing,Tweets — Patrick Durusau @ 1:03 pm

Twitter Analytics Platform Gives Data Back to Users

From the post:

Previously reserved for advertising partners, Twitter Analytics now shows all users an overview of their timeline activity, reveals more detailed information about their followers and lets them download it all as a CSV.

Presented in a month-long timeline of activity, Twitter Analytics visualizes mentions, follows and something previously much harder to track: unfollows. Even this additional context makes Twitter Analytics a useful tool for any user.

The tool also lists out a complete record of your tweets with a few helpful columns added on; Favorites, Retweets and Replies. As you scroll, the timeline becomes fixed to the top of the browser and you can see the relationship between the content of a tweet and the response it got (if it happened in the last 30 days).

Embedded within the tweets column are some additional metrics detailing the number of clicks on links and some callouts highlighting extended reach of individual tweets. Unsurprisingly, this feed-oriented analytics interface reminds me of the ideas in Anil Dash’s Dashboards Should be Feeds. It certainly works well here.

(…)

If the government is going to have your data, then so should you! 😉

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