Curated for content, computing, and digital experience professionals

Tag: big data (Page 1 of 3)

Gilbane Advisor 4-25-18 — deep learning value, martech size, no-click searches

Notes from the AI frontier: Applications and value of deep learning

In 2011 as the excitement about Big Data was becoming mainstream, McKinsey published what was the most useful early report for executives. Big data: The next frontier for innovation, competition, and productivity, took a smart and measured look at use cases and value across industries. Given the symbiotic relationship between data and AI / machine learning, companies who were paying attention and invested in Big Data then are likely positioned well ahead of others to benefit from today’s advances in machine learning technologies and techniques.

AI performance improvement by industry

McKinsey’s new report provides a knowledgeable overview using accurate terminology in their “… analysis of more than 400 use cases across 19 industries and nine business functions highlights the broad use and significant economic potential of advanced AI techniques.” Highly recommended. Read More

A flaw-by-flaw guide to Facebook’s new GDPR privacy changes

Josh Constine provides a useful take on the changes rolling out now to European users illustrated with screen shots. But I think it’s safe to say that whether they are meeting the “letter of the GDPR law” is still a matter for debate.

Overall, it seems like Facebook is complying with the letter of GDPR law, but with questionable spirit…Facebook struck the right balance in some places here. But the subtly pushy designs seem intended to steer people away from changing their defaults. Read More

Marketing Technology Landscape Supergraphic (2018)

Scott Brinker has just released the latest update to his famous “Supergraphic”. The number of marketing technology vendors continues to grow. As Scott puts it, “Water continues to flow into the martech tub faster than it’s draining out.” Check out his post on what it all means and to see/download the graphic and a spreadsheet. Read More

Uh oh, click counts count less

Click quality and measurement has always been a bit iffy. But the biggest challenge to click value yet may come from a combination of mobile trends and Google’s strategy of reducing the need to click away from the search results page. Rand Fishkin’s post, New Data: How Google’s Organic & Paid CTRs Have Changed 2015-2018, looks at some interesting numbers. Back to brand marketing banners?
No-click searches desktop vs mobile

Ultimately, I think this data shows us that the future of SEO will have to account for influencing searchers without earning a click, or even knowing that a search happened. That’s going to be very frustrating for a lot of organizations. Read More

Also…

The Gilbane Advisor curates content for content, computing, and digital experience professionals. We focus on strategic technologies. We publish more or less twice a month except for August and December. See all issues

What big companies are doing with big data today

The Economist has been running a conference largely focused on Big Data for three years. I wasn’t able to make it this year, but the program looks like it is still an excellent event for executives to get their hands around the strategic value, and the reality, of existing big data initiatives from a trusted source. Last month’s conference, The Economist’s Ideas Economy: Information Forum 2013, included an 11 minute introduction to a panel on what large companies are currently doing and on how boardrooms are looking at big data today that is almost perfect for circulating to c-suites. The presenter is Paul Barth, managing partner at NewVantage Partners.

Thanks to Gil Press for pointing to the video on his What’s The Big Data? blog.

Big data and decision making: data vs intuition

There is certainly hype around ‘big data‘, as there always has been and always will be about many important technologies or ideas – remember the hype around the Web? Just as annoying is the backlash anti big data hype, typically built around straw men – does anyone actually claim that big data is useful without analysis?

One unfair characterization both sides indulge in involves the role of intuition, which is viewed either as the last lifeline for data-challenged and threatened managers, or as the way real men and women make the smart difficult decisions in the face of too many conflicting statistics.

Robert Carraway, a professor who teaches Quantitative Analysis at UVA’s Darden School of Business, has good news for both sides. In a post on big data and decision making in Forbes, “Meeting the Big Data challenge: Don’t be objective” he argues “that the existence of Big Data and more rational, analytical tools and frameworks places more—not less—weight on the role of intuition.”

Carraway first mentions Corporate Executive Board’s findings that of over 5000 managers 19% were “Visceral decision makers” relying “almost exclusively on intuition.” The rest were more or less evenly split between “Unquestioning empiricists” who rely entirely on analysis and “Informed skeptics … who find some way to balance intuition and analysis.” The assumption of the test and of Carraway was that Informed skeptics had the right approach.

A different study, “Frames, Biases, and Rational Decision-Making in the Human Brain“, at the Institute of Neurology at University College London tested for correlations between the influence of ‘framing bias’ (what it sounds like – making different decisions for the same problem depending on how the problem was framed) and degree of rationality. The study measured which areas of the brain were active using an fMRI and found the activity of the the most rational (least influenced by framing) took place in the prefrontal cortex, where reasoning takes place; the least rational (most influenced by framing / intuition) had activity in the amygdala (home of emotions); and the activity of those in between (“somewhat susceptible to framing, but at times able to overcome it”) in the cingulate cortex, where conflicts are addressed.

It is this last correlation that is suggestive to Carraway, and what he maps to being an informed skeptic. In real life, we have to make decisions without all or enough data, and a predilection for relying on either data or intuition can easily lead us astray. Our decision making benefits by our brain seeing a conflict that calls for skeptical analysis between what the data says and what our intuition is telling us. In other words, intuition is a partner in the dance, and the implication is that it is always in the dance — always has a role.

Big data and all the associated analytical tools provide more ways to find bogus patterns that fit what we are looking for. This makes it easier to find false support for a preconception. So just looking at the facts – just being “objective” – just being “rational” – is less likely to be sufficient.

The way to improve the odds is to introduce conflict – call in the cingulate cortex cavalry. If you have a pre-concieved belief, acknowledge it and and try and refute, rather than support it, with the data.

“the choice of how to analyze Big Data should almost never start with “pick a tool, and use it”. It should invariably start with: pick a belief, and then challenge it. The choice of appropriate analytical tool (and data) should be driven by: what could change my mind?…”

Of course conflict isn’t only possible between intuition and data. It can also be created between different data patterns. Carraway has an earlier related post, “Big Data, Small Bets“, that looks at creating multiple small experiments for big data sets designed to minimize identifying patterns that are either random or not significant.

Thanks to Professor Carraway for elevating the discussion. Read his full post.

Enterprise Search Strategies: Cultivating High Value Domains

At the recent Gilbane Boston Conference I was happy to hear many remarks positioning and defining “Big Data” and the variety of comments. Like so much in the marketing sphere of high tech, answers begin with technology vendors but get refined and parsed by analysts and consultants, who need to set clear expectations about the actual problem domain. It’s a good thing that we have humans to do that defining because even the most advanced semantics would be hard pressed to give you a single useful answer.

I heard Sue Feldman of IDC give a pretty good “working definition” of big data at the Enterprise Search Summit in May, 2012. To paraphrase is was:

  • > 100 TB up to petabytes, OR
  • > 60% growth a year of unstructured and unpredictable content, OR
  • Ultra high streaming content

But we then get into debates about differentiating data from unstructured content when using a phrase like “big data” and applying it to unstructured content, which knowledge strategists like me tend to put into a category of packaged information. But never mind, technology solution providers will continue to come up with catchy buzz phrases to codify the problem they are solving, whether it makes semantic sense or not.

What does this have to do with enterprise search? In short, “findability” is an increasingly heavy lift due to the size and number of content repositories. We want to define quality findability as optimal relevance and recall.

A search technology era ago, publishers, libraries, content management solution providers were focused on human curation of non-database content, and applying controlled vocabulary categories derived from decades of human managed terminology lists. Automated search provided highly structured access interfaces to what we now call unstructured content. Once this model was supplanted by full text retrieval, and new content originated in electronic formats, the proportion of human categorized content to un-categorized content ballooned.

Hundreds of models for automatic categorization have been rolled out to try to stay ahead of the electronic onslaught. The ones that succeed do so mostly because of continued human intervention at some point in the process of making content available to be searched. From human invented search algorithms, to terminology structuring and mapping (taxonomies, thesauri, ontologies, grammar rule bases, etc.), to hybrid machine-human indexing processes, institutions seek ways to find, extract, and deliver value from mountains of content.

This brings me to a pervasive theme from the conferences I have attended this year, the synergies among text mining, text analytics, extractor/transformer/loader (ETL), and search technologies. These are being sought, employed and applied to specific findability issues in select content domains. It appears that the best results are delivered only when these criteria are first met:

  • The business need is well defined, refined and narrowed to a manageable scope. Narrowing scope of information initiatives is the only way to understand results, and gain real insights into what technologies work and don’t work.
  • The domain of content that has high value content is carefully selected. I have long maintained that a significant issue is the amount of redundant information that we pile up across every repository. By demanding that our search tools crawl and index all of it, we are placing an unrealistic burden on search technologies to rank relevance and importance.
  • Apply pre-processing solutions such as text-mining and text analytics to ferret out primary source content and eliminate re-packaged variations that lack added value.
  • Apply pre-processing solutions such as ETL with text mining to assist with content enhancement, by applying consistent metadata that does not have a high semantic threshold but will suffice to answer a large percentage of non-topical inquiries. An example would be to find the “paper” that “Jerry Howe” presented to the “AMA” last year.

Business managers together with IT need to focus on eliminating redundancy by utilizing automation tools to enhance unique and high-value content with consistent metadata, thus creating solutions for special audiences needing information to solve specific business problems. By doing this we save the searcher the most time, while delivering the best answers to make the right business decisions and innovative advances. We need to stop thinking of enterprise search as a “big data,” single engine effort and instead parse it into “right data” solutions for each need.

Integrating External Data & Enhancing Your Prospects

Most companies with IT account teams and account selling strategies have a database in a CRM system and the company records in that database generally have a wide range of data elements and varying degrees of completeness. Beyond the basic demographic information, some records are more complete than others with regard to providing information that can tell the account team more about the drivers of sales potential. In some cases, this additional data may have been collected by internal staff, in other cases, it may be the result of purchased data from organizations like Harte-Hanks, RainKing, HG Data or any number of custom resources/projects.

There are some other data elements that can be added to your database from freely available resources. These data elements can enhance the company records by showing which companies will provide better opportunities. One simple example we use in The Global 5000 database is the number of employees that have a LinkedIn profile. This may be an indicator that companies with a high percentage of social media users are more likely to purchase or use certain online services. That data is free to use. Obviously, that indicator does not work for every organization and each company needs to test the data correlation between customers and the attributes, environment or product usage.

Other free and interesting data can be found in government filings. For example, any firm with benefit and 401k plans must file federal funds and that filing data is available from the US government. A quick scan of the web site data.gov  shows a number of options and data sets available for download and integration into your prospect database. The National Weather Center, for example, provides a number of specific long term contracts which can be helpful for anyone selling to the agriculture market.

There are a number things that need to be considered when importing and appending or modeling external data. Some of the key aspects include:

  • A match code or record identifier whereby external records can be matched to your internal company records. Many systems use the DUNS number from D&B rather than trying to match on company names which can have too many variations to be useful.
  • The CRM record level needs to be established so that the organization is focused on companies at a local entity level or at the corporate HQ level.  For example, if your are selling multi-national network services, having lots of site recrods is probably not helpful when you most likely have to sell at the corporate level.
  • De-dupe your existing customers. When acquiring and integrating an external file — those external sources won’t know your customer set and you will likely be importing data about your existing customers. If you are going to turn around and send this new, enhanced data to your team, it makes sense to identify or remove existing clients from that effort so that your organization is not marketing to them all over again.
  • Identifying the key drivers that turn the vast sea of companies into prospects and then into clients will provide a solid list of key data attributes that can be used to append to existing records.  For example, these drivers may include elements such as revenue growth, productivity measures such as revenue per employee, credit ratings, multiple locations or selected industries.

In this era of marketing sophistication with increasing ‘tons’ of Big Data being available and sophisticated analytical tools coming to market every company has the opportunity to enhance their internal data by integrating external data and going to market armed with more insight than ever before.

Learn more about more the Global 5000 database

 

Frank Gilbane interview on Big Data

Big data is something we cover at our conference and this puzzles some given our audience of content managers, digital marketers, and IT, so I posted Why Big Data is important to Gilbane Conference attendees on gilbane.com to explain why. In the post I also included a list of the presentations at Gilbane Boston that address big data. We don’t have a dedicated track for big data at the conference but there are six presentations including a keynote.

I was also interviewed on the CMS-Connected internet news program about big data the same week, which gave me an opportunity to answer some additional questions about big data and its relevance to the same kind of  audience. There is still a lot more to say about this, but the post and the interview combined cover the basics.

The CMS-Connected show was an hour long and also included Scott and Tyler interviewing Rob Rose on big data and other topics. You can see the entire show here, or just the 12 twelve minute interview with me below.

Why Big Data is important to Gilbane Conference attendees

If you think there is too much hype, and gratuitous use of the term, big data, you haven’t seen anything yet. But don’t make the mistake of confusing the hype with how fundamental and how transformational big data is and will certainly be. Just turn your hype filter to high and learn enough about it to make your own judgements about how it will affect your business and whether it is something you need to do something about now, or monitor for future planning.

As I said yesterday in a comment on a post by Sybase CTO Irfan Khan Gartner dead wrong about big data hype cycle (with a response from Gartner):

However Gartner’s Hype Cycle is interpreted I think it is safe to say that most, including many analysts, underestimate how fundamental and how far-reaching big data will be. How rapidly its use will evolve, and in which applications and industries first, is a more difficult and interesting discussion. The twin brakes of a shortage of qualified data scientist skills and the costs and complexities of IT infrastructure changes will surely slow things down and cause disillusionment. On the other hand we have all been surprised by how fast some other fundamental changes have ramped up, and BDaaS (Big Data as a Service) will certainly help accelerate things. There is also a lot more big data development and deployment activity going on than many realize – it is a competitive advantage after all.

There is also a third “brake” which is all the uncertainty around privacy issues. There is already a lot of consumer data that is not being fully used because of fear of customer backlash or new regulation and, one hopes, because of a degree of respect for consumer’s privacy.

Rob Rose expanded on some specific concerns of marketers in a recent post Big Data & Marketing – It’s A Trap!, including the lack of resources for interpreting even the current mostly website analytics data marketers already have. It’s true, and not just for smaller companies. In addition there are at least four requirements for making big data analytics accessible to marketers that are largely beyond the reach of most current organizations.

Partly to the rescue is Big Data as a Service BDaaS (one of the more fun-sounding acronyms). BDaaS is going to be a huge business. All the big technology infrastructure firms are getting involved and all the analytics vendors will all have cloud and big data services. There are also many new companies including some surprises. For example, after developing its own Hadoop-based big data analytics expertise Sears created subsidiary MetaScale to provide BDaaS to other enterprises. Ajay Agarwal from Bain Capital Ventures predicts that the confluence of big data and marketing will lead to several new multi-billion dollar companies and I think he is right.

But while big data is important for the marketers, content managers, and IT who attend our conference because of the potential for enhanced predictive analytics and content marketing. The reach and value of big data applications is far broader than marketing – executives need to understand the potential for new efficiencies, products and businesses. The well-known McKinsey report “Big Data: The Next Frontier for Innovation, Competition, and Productivity” (free) is a good place to start. If you are in the information business I focus on that in my report Big-Data: Big Deal or Just Big Buzz? (not free).

Big data presentations at Gilbane Boston

This year we have six presentations on big data, two devoted to big data and marketing and all chosen with an eye towards the needs of our audience of marketers, content strategists, and IT. You can find out more about these presentations, including their date and time on the conference program.

Keynote

Bill Simmons, CTO, DataXu
Why Marketing Needs Big Data

Main Conference Presentations

Tony Jewitt, VP Big Data Solutions at Avalon Consulting, LLC
“Big Data” 101 for Business

Bryan Bell, Vice President, Enterprise Solutions, Expert System
Semantics and the Big Data Opportunity

Brian Courtney, General Manager of Operations Data Management, GE Intelligent Platforms
Leveraging Big Data Analytics

Darren Guarnaccia, Senior VP, Product Marketing, Sitecore
Big Data: What’s the Promise and Reality for Marketers?

Stefan Andreasen, Founder and Chief Technology Officer, Kapow Software
Big Data: Black Hole or Strategic Value?

Update: There is now a video of me being interviewed on big data by CMS-Connected.

Right Fitting Enterprise Search: Content Must Fit Like a Glove

This story brought me up short: Future of Data: Encoded in DNA by Robert Lee Hotz in the Wall Street Journal, Aug. 16, 2012. It describes how “…researchers encoded an entire book into the genetic molecules of DNA, the basic building block of life, and then accurately read back the text.” The article then went on to quote Harvard University’s project senior researcher, molecular geneticist, George Church as saying, “A device the size of your thumb could store as much information as the whole Internet. While this concept intrigues and excites me for the innovation and creative thinking, it stimulates another thought, as well. Stop the madness of content overload first – force it to be managed responsibly.

While I have been sidelined from blogging for a couple of months, industry pundits have been contributing their comments, reflections and guidance on three major topics. Big Data tops the list, with analytics a close second, rounded out by contextual relevance as an ever present content findability issue. In November at Gilbane Boston the program features a study conducted by Findwise, Enterprise Search and Findability Survey,2012, which you can now download. It underscores a disconnect between what enterprise searchers want and how search is implemented (or not), within their organizations. As I work to assemble content, remarks and readings for an upcoming graduate course on “Organizing and Accessing Information and Knowledge,” I keep reminding myself what knowledge managers need to know about content to make it accessible.

So, how would experts for our three dominant topics solve the problems illustrated in the Findwise survey report?

For starters, organizations must be more brutal with content housekeeping, or more specifically housecleaning. As we debate whether our country is as great at innovation as in generations past, consider big data as a big barrier. Human beings, even brilliant ones, can only cope with so much information in their waking working hours. I posit that we have lost the concept of primary source content, in other words content that is original, new or innovative. It is nearly impossible to hone in on information that has never been articulated in print or electronically disseminated before, excluding all the stuff we have seen, over and over again. Our concept of terrific search is to be able to traverse and aggregate everything “out there” with no regard for what is truly conceptually new. How much of that “big data” is really new and valuable? I am hoping that other speakers at Gilbane Boston 2012 can suggest methods for crunching through the “big” to focus search on the best, most relevant and singular primary source information.

Second, others have commented, and I second the idea, that analytic tools can contribute significantly to cleansing search domains of unwanted and unnecessary detritus. Search tools that auto-categorize and cross-categorize content, whether the domain is large or small should be employed during any launch of a new search engine to organize content for quick visual examination, showing you where metadata is wrong, mis-characterized, or poorly tagged. Think of a situation where templates are commonly used for enterprise reports and the name of the person who created the template becomes the “author” of every report. Spotting this type of problem and taking steps to remediate and cleanse metadata, before deploying the search system is a fundamental practice that will contribute to better search outcomes. With thoughtful management, this type of exercise will also lead to corrective actions on the content governance side by pointing to how metadata must be handled. Analytics functions that leverage search to support cleaning up data stores are among the most practical tools now packaged with newer search products.

Finally, is the issue of vocabulary management and assigning terminology that is both accurate and relevant for a specific community that needs to find content quickly and without multiple versions, or without content that is just a re-hash of earlier findings published by the originator. Original publication dates, source information and proper author attribution are key elements of metadata that must be in place for any content that is targeted for crawling and indexing. When metadata is complete and accurate, a searcher can expect the best and most relevant content to rise to the top of a results page.

I hope others in a position to do serious research (perhaps a PhD dissertation) will take up my challenge to codify how much of “big data” is really worthy of being found – again, again, and again. In the meantime, use the tools you have in the search and content management technologies to get brutal. Weed the unwanted and unnecessary content so that you can get down to the essence of what is primary, what is good, and what is needed.

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