Bluebill Blog | Content and information technologies

Leveraging Search in Small Enterprises

A mantra for a small firm or start-up in the 1970s when “Big Blue” was the standard for top notch sales and selling was we need to out-IBM the IBMers.

Search is just one aspect of being able to find what you need to leverage knowledge assets in your work, whether you are in a small firm, a part of a small group in a large organization or an individual consultant seeking to maximize the masses of content and information surrounding you in work.

My thoughts are inspired by the question asked by Andreas Gruber of Informations und Wissensmanagement in this recent post on Enterprise Search Engine Professionals, LinkedIn group. He posed a request for information stating: For enterprise search solutions for (very) small enterprises (10 to 200 employees), I find it hard to define success factors and it seems, that there are not many examples available. If you follow e.g. the critical success factors from the Martin White’s Enterprise Search book, most of them doesn’t seem to work for a small company – simply because none of them can/will investment in a search team etc.

The upcoming Enterprise Search Europe meeting (May 14-16, 2013) in London is one focus of my attention at present. Since Martin White is the Chairman and principal organizer, Andreas’ comments resonated immediately. Concurrently, I am working on a project for a university department, which probably falls in the category of “small enterprise”. The other relevant project on my desk is a book I am co-authoring on “practical KM” and we certainly aim to appeal to the individual practitioner or groups limited by capital resources. These areas of focus challenge me to respond to Andreas’ comments because I am certain they are top of mind for many and the excellent comments already at the posting show that others have good ideas about the topic, as well.

Intangible capital is particularly significant in many small firms, academia, and for independent consultants, like me. Intensive leveraging of knowledge in the form of expertise, relationships, and processes is imperative in these domains. Intangible capital, as a percent of most businesses currently surpasses tangible capital in value, according to Mary Adams founder of Smarter-Companies. Because intangible capital takes more thought and effort to identify, find or aggregate than hard assets, tools are needed to uncover, discover and pinpoint it.

Let’s take the example of expertise, an indisputable intangible asset of any professional services. For any firm, asking expert staff to put an explicit value on their knowledge, competencies or acumen for tackling the type of problem that you need to have solved may give you a sense of value but you need more. The firm or professional you want to hire must be able to back up its value by providing explicit evidence that they “know their stuff” and can produce. For you, search is a tool to lead you to public or published evidence. For the firm being asked to bid on your work, you want them to be able to produce additional evidence. Top quality firms do put both human and technology search resources to work to service existing projects and clients, and to provide evidence of their qualifications, when being asked to retrieve relevant work or references. Search tools and content management methods are diverse and range from modest to very expensive in scope but no firm can exist for long without technology to support the findability of its intangible capital.

To summarize, there are three principal ways that search pays off in the small-medium business (SMB) sector. Citing a few examples of each they are:

  • Finding expertise (people): potential client engagement principal or team member, answers to questions to fulfill a clients engagement, spurring development or an innovation initiative
  • Retrieving prior work: reuse of know-how in new engagements, discovery of ideas previously tabled, learning, documentation of products and processes, building a proposal, starting point for new work, protecting intellectual property for leverage, when patenting, or participating in mergers and acquisitions.
  • Creating the framework for efficiency: time and speed, reinforcing what you know, supporting PR, communications, knowledge base, portraying the scope of intellectual capital (if you are a target for acquisition), the extent of your partnerships that can expand your ability to deliver, creating new offerings (services) or products.

So, to conclude my comment on Andreas’ posting, I would assert that you can “out-IBM the IBMers” or any other large organization by employing search to leverage your knowledge, people and relationships in smart and efficient ways. Excellent content and search practices can probably reduce your total human overhead because even one or two content and search specialists plus the right technology can deliver significant efficiency in intangible asset utilization.

I hope to see conference attendees who come from that SMB community so we can continue this excellent discussion in London, next month. Ask me about how we “ate our own dog-food” (search tools) when I owned a small software firm in the early 1980s. The overhead was minimal compared to the savings in support headcount.

Read More

E-discovering Language to Launch Your Taxonomy

New enterprise initiatives, whether for implementing search solutions or beginning a new product development program, demand communication among team leaders and participants. Language matters; defining terminology for common parlance is essential to smooth progress toward initiative objectives.

Glossaries, dictionaries, taxonomies, thesauri and ontologies are all mechanisms we use routinely in education and work to clarify terms we use to engage and communicate understanding of any specialized domain. Electronic social communication added to the traditional mix of shared information (e.g. documents, databases, spreadsheets, drawings, standardized forms) makes business transactional language more complex. Couple this with the use of personal devices for capturing and storing our work content, notes, writings, correspondence, design and diagram materials and we all become content categorizing managers. Some of us are better than others at organizing and curating our piles of information resources.

As recent brain studies reveal, humans, and probably any animal with a brain, have established cognitive areas in our brains with pathways and relationships among categories of grouped concepts. This reinforces our propensity for expending thought and effort to order all aspects of our lives. That we all organize differently across a huge spectrum of concepts and objects makes it wondrous that we can live and work collaboratively at all. Why after 30+ years of marriage do I arrange my kitchen gadget drawer according to use or purpose of devices while my husband attempts to store the same items according to size and shape? Why do icons and graphics placed in strange locations in software applications and web pages rarely impart meaning and use to me, while others “get it” and adapt immediately?

The previous paragraph may seem to be a pointless digression from the subject of the post but there are two points to be made here. First, we all organize both objects and information to facilitate how we navigate life, including work. Without organization that is somehow rationalized, and established accordingly to our own rules for functioning, our lives descend into dysfunctional chaos. People who don’t organize well or struggle with organizing consistently struggle in school, work and life skills. Second, diversity of practice in organizing is a challenge for working and living with others when we need to share the same spaces and work objectives. This brings me to the very challenging task of organizing information for a website, a discrete business project, or an entire enterprise, especially when a diverse group of participants are engaged as a team.

So, let me make a few bold suggestions about where to begin with your team:

  • Establish categories of inquiry based on the existing culture of your organization and vertical industry. Avoid being inventive, clever or idiosyncratic. Find categories labels that everyone understands similarly.
  • Agree on common behaviors and practices for finding by sharing openly the ways in which members of the team need to find, the kinds of information and answers that need discovering, and the conditions under which information is required. These are the basis for findability use cases. Again, begin with the usual situations and save the unusual for later insertion.
  • Start with what you have in the form of finding aids: places, language and content that are already being actively used; examine how they are organized. Solicit and gather experiences about what is good, helpful and “must have” and note interface elements and navigation aids that are not used. Harvest any existing glossaries, dictionaries, taxonomies, organization charts or other definition entities that can provide feeds to terminology lists.
  • Use every discoverable repository as a resource (including email stores, social sites, and presentations) for establishing terminology and eventually writing rules for applying terms. Research repositories that are heavily used by groups of specialists and treat them as crops of terminology to be harvested for language that is meaningful to experts. Seek or develop linguistic parsing and term extraction tools and processes to discover words and phrases that are in common use. Use histograms to determine frequency of use, then alphabetize to find similar terms that are conceptually related, and semantic net tools to group discovered terms according to conceptual relationships. Segregate initialisms, acronyms, and abbreviations for analysis and insertion into final lists, as valid terms or synonyms to valid terms.
  • Talk to the gurus and experts that are the “go-to people” for learning about a topic and use their experience to help determine the most important broad categories for information that needs to be found. Those will become your “top term” groups and facets. Think of top terms as topical in nature (e.g. radar, transportation, weapons systems) and facets as other categories by which people might want to search (e.g. company names, content types, conference titles).
  • Simplify your top terms and facets into the broadest categories for launching your initiative. You can always add more but you won’t really know where to be the most granular until you begin using tags applied to content. Then you will see what topics have the most content and require narrower topical terms to avoid having too much content piling up under a very broad category.
  • Select and authorize one individual to be the ultimate decider. Ambiguity of categorizing principles, purpose and needs is always a given due to variations in cognitive functioning. However, the earlier steps outlined here will have been based on broad agreement. When it comes to the more nuanced areas of terminology and understanding, a subject savvy and organizationally mature person with good communication skills and solid professional respect within the enterprise will be a good authority for making final decisions about language. A trusted professional will also know when a change is needed and will seek guidance when necessary.

Revisit the successes and failures of the applied term store routinely: survey users, review search logs, observe information retrieval bottlenecks and troll for new electronic discourse and content as a source of new terminology. A recent post by taxonomy expert Heather Hedden gives more technical guidance about evaluating and sustaining your taxonomy maintenance.

Read More

Mobile development strategy – platform decision update

Last April I suggested that evolving mobile platform market changes meant organizations needed to re-visit their mobile development strategy and said

“What has changed? To over simplify: Apple’s dominance continues to increase and is unassailable in tablets; RIM is not a contender; Microsoft is looking like an up-and-comer; and most surprising to many, Android is looking iffy and is a flop in tablets with the exception of the very Amazon-ized version in the Kindle Fire.”

Not surprisingly, things have changed again. Two major changes are that Samsung is now a major player, and Google has finally made progress in tablets with the Nexus 7 and the much improved Android “Jelly Bean” release. Amazon’s second Fire is also more robust. There are now real choices in tablets – personally I have an iPad, a Fire HD, and a Nexus 7, and I use all three of them, and for many purposes I just grab the closest. But businesses making a significant investment in a platform for development need to carefully evaluate its stability and staying power.

One thing that hasn’t changed is the debate among analysts over what the iOS and Android market share numbers mean – specifically, whether the larger and accelerating Android market share numbers threaten Apple’s dominance. At first glance it is natural to think that dominant market share signifies a safer bet, and indeed many analysts make this point. But it’s not so simple. Last year there was evidence that even though Android devices had a market share advantage, Apple devices accounted for much more total online activity – were used more – and it is probably safe to say that use is a requirement of product success.

More importantly, if you look at profit share, Apple continues to dominate. So the opposing view is that Apple may be the safer bet since for most values of company/product health, profit trumps revenue.

In “The Mobile Train Has Left The Windows 8 Platform Behind“, John Kirk, who doesn’t mince words, has no patience for the view that Android’s market share means it will squash Apple:

“According to Canaccord Genuity, Apple took in 69% of the handset (all mobile phones, not just smartphones) profits in 2012. Samsung took in 34%, HTC accounted for 1%…

No one not named Apple or Samsung is making any meaningful profits from the handset sector…

Many industry observers have the handset market all wrong. They opine that Andoid is destroying iOS. What is actually happening is:

  1. With 69% of the profits, iOS is doing just fine. More than fine, actually.
  2. Android destroyed every phone manufacturer not named Apple (BlackBerry, Nokia, Palm, etc.).
  3. Samsung destroyed every Android phone manufacturer not named Samsung (HTC, Motorola, Sony Erricson, etc.).

Pundits like to predict the imminent demise of iOS, but those profit numbers say just the opposite. And even as Android’s market share has increased, iOS’s profit share has increased too. Market share is no guarantor of profits. This should be self-evident. But apparently, it’s not.”

Kirk follows up with more entertaining disdain for the “church of market share” at “Does the Rise of Android’s Market Share Mean the End of Apple’s Profits?“.

In terms of tablet market share,

“According to Canalys, Apple – despite being supply constrained – sold 22.9 million tablets for 49% share, Samsung shipped 7.6 million tablets, Amazon shipped 4.6 million tablets for 18% share, and Google’s Nexus 7 and 10, combined, shipped 2.6 million tablets.”

In conclusion,

“Only Samsung and Apple are competing in phones. Only Amazon, Google, Samsung and Apple are effectively competing in tablets. The mobile “train” has left the station and companies like HP, Lenovo, Dell and Microsoft are standing on the Windows 8 platform, watching it pull away.”

For more on Microsoft see Kirk’s full post.

Mobile platforms are still evolving and the coming proliferation of new device types guarantee that there will be continuous and substantial change made to those that survive. No one responsible for a mobile development strategy should wait almost a year to evaluate their current plan. Fortunately there is no shortage of useful platform data. It just needs to be interpreted critically.

Read More

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.

Read More

How long does it take to develop a mobile app?

We have covered and written about the issues enterprises need to consider when planning to develop a mobile app, especially on choosing between native apps, mobile web apps (HTML5, etc.), or a hybrid approach that includes elements of each. And have discussed some of the choices / factors that would have an effect on the time required to bring an app to market, but made no attempt to advise or speculate on how long it should take to “develop a mobile app”. This is not a question with a straightforward answer as any software development manager with tell you.

There are many reasons estimating app development time is difficult, but there are also items outside of actual coding that need to be accounted for. For example, a key factor often not considered in measuring app development is the time involved to train or hire for skills. Since most organizations already have experience with standards such as HTML and CSS developing mobile web apps should be, ceteris paribus, less costly and quicker than developing a native app. This is especially true when the app needs to run on multiple devices with different APIs using different programing languages on multiple mobile (and possibly forked) operating systems. But there are often appealing device features that require native code expertise, and even using a mobile development framework which deals with most of this complexity requires learning something new.

App development schedules can also be at the mercy of app store approvals and not-always-predictable operating system updates.

As unlikely as it is to come up with a meaningful answer to the catchy (and borrowed) title of this post, executives need good estimates of the time and effort in developing specific mobile apps. But experience in developing mobile apps is still slim in many organizations and more non-technical managers are now involved in approving and paying for app development. So even limited information on length of effort can provide useful data points.

I found the survey that informed the Visual.ly infographic below via ReadWrite at How Long Does It Take To Build A Native Mobile App? [InfoGraphic]). It involved 100 iOS, Android and HTML5 app developers and was done by market research service AYTM for Kinvey, provider of a cloud backend platform for app developers.

Their finding? Developing an iOS or Android app takes 18 weeks. I didn’t see the survey questions so don’t know whether whether 18 weeks was an average of actual developments, opinions on what it should take, or something else.

Of course there are simple apps that can be created in a few days and some that will take much longer, but in either case the level of effort is almost always underestimated. Even with all the unanswered questions about resources etc., the infographic raises, the 18 week finding may helpfully temper somebody’s overly optimistic expectations.

Read More

Launching Your Search for Enterprise Search Fundamentals?

It’s the beginning of a new year and you are tasked with responsibility for your enterprise to get top value from the organization’s information and knowledge assets. You are the IT applications specialist assigned to support individual business units with their technology requests. You might encounter situations similar to these:

  • Marketing has a major initiative to re-write all product marketing pieces.
  • Finance is grappling with two newly acquired companies whose financial reports, financial analyses, and forecasts are scattered across a number of repositories.
  • Your Legal department has a need to categorize and analyze several thousand “idea records” that came from the acquired companies in order to be prepared for future work, patenting new products.
  • Research and development is attempting to categorize, and integrate into a single system, R&D reports from an existing repository with those from the acquisitions.
  • Manufacturing requires access to all schematics for eight new products in order to refine and retool manufacturing processes and equipment in their production area.
  • Customer support demands just-in-time retrieval and accuracy to meet their contractual obligations to tier-one customers, often from field operations, or while in transit to customer sites. The latter case often requires retrieval of a single, unique piece of documentation.

All of these groups have needs, which if not met present high risk or even exposure to lawsuits from clients or investors. You have only one specialist on staff who has had two years of experience with a single search engine, but who is currently deployed to field service operations.

Looking at just these few examples we can see that a number of search related technologies plus human activities may be required to meet the needs of these diverse constituents. From finding and assembling all financial materials across a five-year time period for all business units, to recovering scattered and unclassified emails and memos that contain potential product ideas, the initiative may be huge. A sizable quantity of content and business structural complexity may require a large scale effort just to identify all possible repositories to search for. This repository identifying exercise is a problem to be solved before even thinking about the search technologies to adopt for the “finding” activity.

Beginning the development of a categorizing method and terminology to support possible “auto-categorization” might require text mining and text analysis applications to assess the topical nomenclature and entity attributes that would make a good starting point. These tools can be employed before the adoption of enterprise search applications.

Understanding all the “use-cases” for which engineers may seek schematics in their re-design and re-engineering of a manufacturing plant is essential to selecting the best search technology for them and testing it for deployment.

The bottom line is there is a lot more to know about content and supporting its accessibility with search technology than acquiring the search application. Furthermore, the situations that demand search solutions within the enterprise are far different, and their successful application requires far greater understanding of user search expectations than Web searching for a product or general research on a new topic.

To meet the full challenge of providing the technologies and infrastructure that will deliver reliable and high value information and knowledge when and where required, you must become conversant with a boatload of search related topics. So, where do you begin?

A new primer, manageable in length and logical in order has just been published. It contains the basics you will need to understand the enterprise context for search. A substantive list of reading resources, a glossary and vendor URL list round out the book. As the author suggests, and I concur, you should probably begin with Chapter 12, two pages that will ground you quickly in the key elements of your prospective undertaking.

What is the book? Enterprise Search (of course) by Martin White, O’Reilly Media, Inc., Sebastopol, CA. © 2013 Martin White. 192p. ISBN: 978-1-449-33044-6. Also available as an online edition at: http://my.safaribooksonline.com/book/databases/data-warehouses/9781449330439

Read More

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.

Read More

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

 

Read More

Technology and IT Spending Metric Options

When planning for global market growth and sizing up the opportunities in various countries, there is often a lack of data available from various industry sources. One could look at GDP figures or population data by country – both of those have some limitations. A better gauge might be to look at those business entities that generate the most revenue in each country as they will help contribute to other businesses in the geography and in general, raise the level of B2B activity overall.

Diving into the data of the Global 5000 companies – the 5000 largest companies in the world based on revenue – we find a couple of different ways to help guide your estimates of market size and rank order.

The first list is the top 10 countries by number of firms in our Global 5000 database with HQ in the country.

  • USA – 2148
  • Japan – 334
  • China – 221
  • UK – 183
  • Canada – 124
  • Germany – 98
  • France – 84
  • Australia – 77
  • India – 76
  • Italy – 65

For each company in the database, there is an estimate for the amount spent on IT – both internal and external costs. When we take those amounts for each country and look at the average IT spending for these leading firms, we see a different order of countries which would also prove to be attractive targets.

  • France – $902 million per company
  • Germany
  • Netherlands
  • Spain
  • Venezuela
  • Italy
  • China
  • Switzerland
  • South Korea
  • New Zealand – $545 million per company

Of course, all these companies are the biggest of the big and not all companies in that country will spend at that level — but it is indicative of the relative IT spending on a country basis and again shows some of the potential for attractive markets as you eye global opportunities.

Learn more about more the Global 5000 database

Read More

Enterprise Search is Never Magic

How is it that the blockbuster deals for acquiring software companies that rank highest in their markets spaces seem to end up smelling bad several months into the deals? The latest acquisition to take on taint was written about in the Wall Street Journal today, noting that HP Reports $8.8 Billion Charge on Accounting Misstatement at Autonomy. Not to dispute the fact that enterprise search megastars Fast (acquired by Microsoft) and Autonomy had some terrific search algorithms and huge presence in the enterprise market, there is a lot more to supporting search than the algorithms.

The fact that surrounding support services have always been essential requirements for making these two products successful in deployment has been well documented over the years. Hundreds of system integrators and partner companies to Microsoft and Autonomy do very well making these systems deliver the value that has never been attainable with just out-of-the-box installations. It takes a team of content, search and vocabulary management specialists to deliver excellent results. For any but the largest corporations, the costs and time to achieve full implementation have rarely been justifiable.

Many fine enterprise search products deliver high value at much more reasonable costs, and with much more efficient packaging, shorter deployment times and lower on-going overhead. Never to be ignored is that enterprise search must be accounted for as infrastructure. Without knowing where the accounting irregularities (also true with Fast) actually lay, I suspect that HP bought the brand and the prospective customer relationships only to discover that the real money was being made by partners and integrators, and the software itself was a loss leader. If Autonomy did not bring with it a solid service and integration operation with strong revenues and work in the pipeline, HP could not have gained what it bargained for in the purchase. I “know” nothing but these are my hunches.

Reflecting back on a couple of articles (If a Vendor Spends Enough… and Enterprise Search and Collaboration…) I wrote a couple of years ago, as Autonomy began hyping its enterprise search prowess in Information Week ads, it seems that marketing is all the magic it needed to reel in the biggest fish of all – a sale to HP.

Read More
Page 3 of 19«12345»10...Last »