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Category: Computing & Data (page 3 of 8)

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.

The Analyst’s Lament: Big Data Hype Obscures Data Management Problems in the Enterprise

I’ve been a market and product analyst for large companies. I realize that my experiences are a sample of one, and that I can’t speak for my analyst peers. But I suspect some of them would nod in recognition when I say that in those roles, I spent only a fraction of my time in these analyst roles actually conducting data analysis.  With the increase in press that Big Data has received, I started seeing a major gap between what I was reading about enterprise data trends, and my actual experiences working with enterprise data.

A more accurate description of what I spent large amounts of time doing was data hunting. And data gathering, and data cleaning, and data organizing, and data checking.  I spent many hours trying to find the right people in various departments who “owned” different data sources. I then had to get locate definitions (if they existed – this was hit or miss) and find out what quirks the data had so I could clean it without losing records (for example, which of the many data fields with the word “revenue” in it would actually give me revenue). In several cases I found myself begging fellow overworked colleagues to please, please, pull the data I needed from that database which I in theory should have had access to but was shut out of due to multiple layers of bureaucracy and overall cluelessness as to what data lived where within the organization.

Part of me thought, “Well, this is the lot of an analyst in a large company. It is the job.” And this was confirmed by other more senior managers – all on the business side, not in the IT side – who asserted that, yes, being a data hunter/gatherer/cleaner/organizer/checker was indeed my job. But another part of me was thinking, “These are all necessary tasks in dealing with data. I will always need to clean data no matter what. I will need to do some formatting and re-checking to make sure what I have is correct. But should this be taking up such a large chunk of my time? This is not the best way I can add value here. There are too many business questions I could potentially be trying to help solve; there has got to be a better way.”

So initially I thought, not being an IT professional, that this was an issue of not having the right IT tools. But gradually I came to understand that technology was not the problem. More often than not, I had access to best-in-class CRM systems, database and analytics software, and collaboration tools at my disposal. I had the latest versions of Microsoft Office and a laptop or desktop with decent processing power. I had reliable VPN connectivity when I was working remotely and often a company-supplied mobile smartphone. It was the processes and people that were the biggest barriers to getting the information I needed in order to provide fact-based research that could be used to solve business-critical decisions.

Out of sheer frustration, I started doing some research to see if there was indeed a better way for enterprises to manage their data. Master Data Management (MDM), you’ve been around for over a decade, why haven’t I ever encountered you?  A firm called the Information Difference, a UK-based consultancy which specializes in MDM, argues that too often, decisions about data management and data governance are left solely to the IT department. The business should also be part of any MDM project, and the governance process should be sponsored and led by C-level business management. Talk about “aha” moments.  When I read this, I actually breathed a sigh of relief. It isn’t just me that thinks there has to be a better way to go, so that the not-cheap business and market analysts that enterprises the world over employ can actually spend more of their time solving problems and less time data wrangling!

That’s why when I read the umpteenth article/blog post/tweet about how transformative Big Data is and will be, I cannot help but groan.  Before enterprises begin to think about new ways about structuring and distributing data, they need to do an audit of how existing data is already used within and between different businesses.  In particular, they should consider MDM if that has not already been implemented. There is so much valuable data that already exists in the enterprise, but the business and IT have to actually work together to deploy and communicate about data initiatives. They also need to evaluate if and how enterprise data is being used effectively for business decisions, and if that usage meets compliance and security rules.

I suspect that many senior IT managers know this and agree. I also suspect that getting counterparts in the business to be active and own decisions about enterprise data, and not just think data is an IT issue, can be a challenge. But in the long run, if this doesn’t happen more often, there’s going to be a lot of overpaid, underutilized data analysts out there and missed business opportunities. So if you are an enterprise executive wondering “do I have to worry about this Big Data business?” please take a step back and look at what you already have.  And if you know any seasoned data analysts in your company, maybe even talk to them about what would make them more effective and faster at their job. The answer may be simpler than you think.

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.

Customer experiences, communications, and analytics

three epicenters of innovation in modern marketing
I recently discovered Scott Brinker’s Chief Marketing Technologist blog and recommend it as a useful resource for marketers. The Venn diagram above is from a recent post, 3 epicenters of innovation in modern marketing. It was the Venn diagram that first grabbed my attention because I love Venn diagrams as a communication tool, it reminded me of another Venn diagram well-received at the recent Gilbane Conference, and most of the conference discussions map to someplace in the illustration.

As good as the graphic is on its own, you should read Scott’s post and see what he has to say about the customer experience “revolution”.

Lest you think Scott is a little too blithe in his acceptance of the role of big data, see his The big data bubble in marketing — but a bigger future, where the first half of the (fairly long) post talks about all the hype around big data. But you should read the full post because he is right on target in describing the role of big data in marketing innovation, and in his conclusion that data-driven organizations will need to make use of big data though these data-driven and data-savvy organizations will take some time to build.

So don’t let current real or perceived hype about the role of big data in marketing lead you to discount its importance – it’s a matter of when, not if. “When” is not easy to predict, but will certainly be different depending on an organizations’ resources and ability to deal with complexity, and organizational and infrastructure changes.

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

 

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.

Harry Henry’s Global 5000 Insights

Colleague and market research expert Harry Henry is filling a hole in the company research market with his Global 5000 database of the 5000 largest global companies, including both public and private businesses. This is already an important resource for marketers who need to understand global market opportunities more than they ever have before – and that most likely means you, since most of our readers are from mid-to-large size companies who either are or should be growing their international business.

While we focus on the information technology strategies for reaching and engaging with customers and colleagues everywhere, you still need to decide which markets and regions, which industries, and which leading companies to target for growth. Harry has generously agreed to provide regular posts providing insights from his database to help inform those decisions.

Read Harry’s first post China Eyes Canadian Energy Resources. You can follow Harry’s posts on this blog at http://bluebillinc.com/author/hhenry/. Or you can reach him directly.

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