3
Mar

Has the Harvard Business Review embraced the notion of controlled free market competition for the tech sphere?

2-Color-Design-Hi-Res-100px-width On Monday, March 2, 2015, the online edition of the Harvard Business Review (HBR) published an article written by Kira Radinsky titled Data Monopolists Like Google Are Threatening the Economy (https://hbr NULL.org/2015/03/data-monopolists-like-google-are-threatening-the-economy).

Does it make sense for anyone reading this article to tightly associate (perhaps in a Pavlovian manner) the opinion expressed in it with the Harvard Business Review, itself? Did Radinsky intend to capitalize on the opportunity of publishing this article on as ostensibly a prestigious web site as HBR for some reason?

I hope readers will not find themselves somehow adrift as they ponder the above questions. The questions are not coming out of the void. Because the position Radinsky presents in this article is actually consistent, as I read it, with a Socialist view of how tech businesses should be regulated by the government to ensure “fair” competition.

In fact, a review of Radinsky’s public profile on LinkedIn reveals her management position in a company based in Israel. So why is the HBR publishing her article? Is it not fair to assume the average reader could misconstrue the article and its position on the HBR site as a tacit endorsement of some new test to genuine American free market capitalism (credit to Larry Kudlow for coining this phrase).

So with this preamble in place, let me now dive into what I think really matters. Radinsky presents the following “fact”: “Today, the most prominent factors are historical search query logs and their corresponding search result clicks. Studies show that the historical search improves search results up to 31%.” Sure, if the technology is predicated on personalization techniques and “cookies”, etc.

There is no reason why competitors to Google (for example) couldn’t approach the same objective from a completely different angle. In fact, given the growing public concern about personalization and its dependence on activities of the invasion of privacy kind, there is, perhaps, a palpable imperative to find just this kind of new way of approaching the task.

Free market capitalism always rewards the “better mousetrap”. So why argue for a controlled marketplace where stakeholders in one approach are penalized just because the “better mousetrap” has yet to be found?

Granted, we have yet to witness the introduction of this “better mousetrap”, but I would argue the recent successes Facebook has reported over the last several business quarters are indicative of a real shift away from the kind of traditional search engine marketing for which Google is renowned.

In my opinion the editors at HBR should have thought a bit more about Radinsky’s article before agreeing to publish it.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2015 All Rights Reserved

23
Jan

SQL remains a useful foundation for building tools to analyze data

2-Color-Design-Hi-Res-100px-widthA lot of editorial content about data, and tools built for data analysis, includes a Pavlov-like association between “big data” and “modern” computing. Relational database approaches to addressing data in a form built to accommodate analysis with Structured Query Language (SQL) tools are treated as a dated approach, somehow behind the times.

But much of this content fails to inform the people reading it about just how these “modern” computing systems actually work. For better or worse, Relational databases, which provide a structure (perhaps backbone would be a better word) for information, are, at some point in the process of analyzing electronic information (data), indispensable.

As a rule of thumb, the best examples of editorial content written on topics relevant to this subject, will incorporate some respect for SQL. David Chappell of Chappell & Associates has written a white paper for Microsoft, titled Introducing DocumentDB A NoSQL Database for Microsoft Azure (http://azure NULL.microsoft NULL.com/en-us/documentation/articles/documentdb-whitepaper-chappell/), which, follows this route. Chappell writes: “To support applications with lots of users and lots of data, DocumentDB is designed to scale: a single database can be spread across many different machines . . . .DocumentDB also provides a query language based on SQL, along with the ability to run JavaScript code directly in the database as stored procedures and triggers with atomic transactions.”

From Chappell’s description it should be clear DocumentDB has been built to replicate some of the core planks of Relational Database Management Systems (RDBMS) best practices. These certainly include SQL tools along with stored procedures, and triggers. Enterprise consumers of RDBMS and/or NoSQL collections of data will approve of the end of Chappell’s sentence: “atomic transactions”. This phrase provides these readers with an important assurance: DocumentDB has been built with ACID “Atomicity, Consistency, Isolation and Durability” transaction process in mind. ACID data communications is the floor supporting today’s commercial quality electronic transactions. Without an ACID compliant structure on both sides of a commerce transaction, businesses are not likely to exchange information. The negative ramifications of such a condition are great, so “modern” best practices have been built with an assumption of ACID compliance as a given.

Unfortunately non relational database systems are challenged to demonstrate ACID compliance. This fact is not lost on Chappell. The white paper he has written for Microsoft presents a balance between big data, NoSQL and SQL and RDBMS concepts in a coherent presentation. In my opinion other technical writers would benefit from his approach. I suspect Chappell’s success at his effort is a direct result of his technical understanding of how these systems actually work.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2015 All Rights Reserved

18
Dec

A Microsoft Perspective on NoSQL and Document Databases

2-Color-Design-Hi-Res-100px-widthIn November, 2011, Julie Lerman wrote a post for Microsoft’s MSDN Magazine on Document Databases. The title of her post is What the Heck Are Document Databases? (http://msdn NULL.microsoft NULL.com/en-us/magazine/hh547103 NULL.aspx) Her post may provide business sponsors of NoSQL database projects with useful information about the notion of NoSQL, and, therefore is recommended reading material.

What prompts me to recommend this post for business stakeholders in NoSQL projects (aka Gartner’s “Citizen Developers”) is the comparative lack of abstraction characterizing Lerman’s presentation. She quickly identifies document databases as one of several types of NoSQL databases (she also presents “key-value pair” databases and points to Azure Table Storage as an example). Here’s a great example of the simplicity of Lerman’s presentation of the notion of NoSQL: “The term is used to encompass data storage mechanisms that aren’t relational and therefore don’t require using SQL for accessing their data.”

For some business readers even this short definition may be challenging. Just what does she mean when she presents her notion of “data storage mechanisms that aren’t relational?” It would, perhaps, have been helpful for the audience I have targeted to add an additional sentence, to simply illustrate how rows and columns in tables, which are, defacto, “relational” components (or structure) actually offer users a method of storing information. Kind of like “I know where you are, therefore, dear data, you have been stored SOMEWHERE”.

But the business user is likely not Lerman’s intended audience. This post appears in Microsoft’s MSDN (Microsoft Developer Network) Magazine, so the intended audience, I would assume, are coders working with Microsoft tools (.NET, C#) via VisualStudio. Nevertheless, sections of the post (like the one’s I’ve quoted, above) are certainly worth a read by the audience I have in mind, as well.

Here’s more useful information. As I wrote last week, the definition of NoSQL, “Not Only Structured Query Language” is a useful text string to keep in mind when grappling with hype about “radically different” approaches to managing data, or “getting rid of” relational databases. Back in November, 2011, when Lerman published her post, she drills down into defining the NoSQL acronym, too, by pointing her readers to a post by Brad Holt of the CouchDB (http://couchdb NULL.apache NULL.org/) project. The title of Holt’s post is Addressing the NoSQL Criticism (http://bradley-holt NULL.com/2011/07/addressing-the-nosql-criticism/), which he handles by noting “First, NoSQL is horrible name. It implies that there’s something wrong with SQL and it needs to be replaced with a newer and better technology. If you have structured data that needs to be queried, you should probably use a database that enforces a schema and implements Structured Query Language. I’ve heard people start redefining NoSQL as “not only SQL”. This is a much better definition and doesn’t antagonize those who use existing SQL databases. An SQL database isn’t always the right tool for the job and NoSQL databases give us some other options.” (this quote is excerpted, in entirety, from Brad Holt’s post. I’ve provided a link here to the complete post and encourage readers to read the post in entirety.).

So if you need to get a good understanding about the Document Database type of NoSQL structure, I recommend reading Lerman and Holt’s posts.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

17
Dec

Google Debuts Cloud Dataflow at Google I/O 2014

2-Color-Design-Hi-Res-100px-widthAt the end of a 2.5 hr plus webcast of the Keynote Presentation from Google I/O 2014 (https://www NULL.google NULL.com/events/io#wtLJPvx7-ys) can be found the debut of Google Cloud Dataflow, the replacement for Google MapReduce. Readers unfamiliar with MapReduce, but avidly interested in the big data enterprise computing trend, need to understand MapReduce as the application at the foundation of today’s Apache Hadoop project. Without MapReduce, the Apache Hadoop project would not exist. So Google MapReduce is a software package worth some study, as is Cloud Dataflow.

But wait, there’s more. As Urs Hölze, Senior Vice President, Technical Infrastructure, introduces Google Cloud Dataflow, his audience is also informed about Google’s role in the creation of another of today’s biggest enterprise data analytics approaches — NoSQL (“Not only SQL”). He casually informs his audience (the segue is a simple “by the way”) Google invented NoSQL.

I hope readers will get a feel for where I’m headed with these comments about these revelations about Google’s historical role in the creation of two of the very big trends in enterprise computing in late 2014. I’m perplexed at why Google would, literally, bury this presentation at the very end of the Keynote. Why would Google prefer to cover its pioneering role in these very hot computing trends with a thick fog? Few business decision-makers, if any, will be likely to pierce this veil of obscurity as they search for best-in-class methods of incorporating clusters of servers in a parallel processing role (in other words “big data”) to better address the task of analyzing text data scraped from web pages for corporate sites (“NoSQL”).

On the other hand, I’m also impressed by the potential plus Google can realize by removing this fog. Are they likely to move in this direction? I think they are, based upon some of the information they reported to the U.S. SEC in their most recent 10Q filing for Q3 2014. Year-over-year, the “Other Revenues” segment of Google’s revenue stream grew by 50% from $1,230 (in 000s) in 2013, to $1,841 in 2014. Any/all revenue Google realizes from Google Cloud and its related components (which, by the way, include Cloud Dataflow) are included in this “Other Revenues” segment of the report. For the nine months ending September 30, 2014, the same revenue segment increased from $3,325 in 2013, to $4,991 in 2014. Pretty impressive stuff, and not likely to diminish with a revamped market message powering “Google at Work”, and Amit Singh (late of Oracle) at the head of the effort.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

15
Dec

Who’s losing sleep over NoSQL?

One of the biggest challenges facing product marketing within any business is successfully identifying a market segment. I would argue more businesses fail because they either:

  1. don’t understand their market niche
  2. or can’t articulate a message intelligible to their market niche
  3. The next step is to put together a portrait of an ideal prospect within this segment. Over time, if a business is lucky enough to succeed, this portrait will likely change (perhaps scale is a better word). After all, early adopters will spread the word to more established prospects. The latter are more conservative, and proceed at a different pace, based upon different triggers.

The 3 steps I’ve just identified are no less a mandatory path forward for early stage ISVs than they are for restaurants, convenience stores, or any other early stage business.

But a lot of the marketing collateral produced by early stage ISVs offering NoSQL products and solutions, in my opinion, doesn’t signal a successful traverse of this path. In an interview published on December 12, 2014, Bob Wiederhold, CEO of CouchBase presents the first and second phases of what he refers to as “NoSQL database adoption” by businesses. Widerhold’s comments are recorded in an article titled Why 2015 will be big for NoSQL databases: Couchbase CEO (http://www NULL.zdnet NULL.com/article/why-2015-will-be-big-for-nosql-databases-couchbase-ceo/).

My issue is with Wiederhold’s depiction of the first adopters of NoSQL Databases: “Phase one started in 2008-ish, when you first started to see commercial NoSQL products being available. Phase one is all about grassroots developer adoption. Developers would go home one weekend, and they’ll have heard about NoSQL, they download the free software, install it, start to use it, like it, and bring it into their companies”.

But it’s not likely these developers would have brought the software to their companies unless somebody was losing sleep over some problem. Nobody wants to waste time trying something new simply because it’s new. No insomnia, no burning need to get a good night’s rest. What I needed to hear about was just what was causing these early adopters to lose sleep.

I’m familiar with the group of developers Wiederhold portrays in the above quote. I’ve referred to them differently for other software products I’ve marketed. These people are the evangelists who spread the word about a new way of doing something. They are the champions. Any adoption campaign has to target this type of person.

But what’s missing is a portrait of the tough, mission-critical problem driving these people to make their effort with a new, and largely unknown piece of software.

It’s incumbent on CouchBase and its peers to do a better job depicting the type of organization with a desperate need for a NoSQL solution in its marketing communications and public relations efforts.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

12
Dec

The job of classifying large amounts of text data becomes easier with JSON

The final cloud-like computing theme contributing to the unfortunate fog around the notion of “big data” is JSON (http://www NULL.json NULL.org/). In my opinion, enterprise consumers of big data solutions built with NOSQL databases aren’t going to be able to connect the dots from the presentation on the JSON open-source project homepage.

More intelligible information about JSON for the non programmer can be found on the web site of the Apache CouchDB project (http://couchdb NULL.apache NULL.org/). “CouchDB is a database that completely embraces the web. Store your data with JSON documents. Access your documents and query your indexes with your web browser, via HTTP” (quoted from the first sentence of editorial content published on the site). Quering indexes with your web browser, hmmm . . . might this have something with Chrome’s Omnibox (https://www NULL.google NULL.com/search?sourceid=chrome-psyapi2&ion=1&espv=2&ie=UTF-8&q=omnibox%20json)? In fact, as any reader following the link just provided will note, it does.

So now with this flexibility in mind, it might provide enterprise computing consumers with more of a rationale for calling for the implementation of databases conforming to JSON, which will lend themselves to analytics built with NOSQL tools. If the process of collecting data on some aspect of a business process can be reduced down to little more than punching some keywords into Chrome’s Omnibox (a version of which is now available for Firefox and Internet Explorer), then Lines of Business (LoBs) can count on their personnel getting to the data they need, when they need it, from any device (mobile, desktop, laptop) whenever they need it without the need for any proprietary solution.

Pretty cool. The cool factor increases when one reads more about the CouchDB project. JSON represents an alternative to XML, which requires substantially more verbosity (meaning lines of code) to express the same programming statement. Lots of lines of code contribute to a slower web, where pages can take forever to load. So the comparatively lighter weight promised by using JSON to express steps in a program makes a lot of sense. The intention of JSON and XML are the same, namely to provide a method of data exchange (http://www NULL.idealware NULL.org/articles/data_exchange_alpha_soup NULL.php).

JSON produces “JSON Documents”. Here’s an example of what IBM© is doing with JSON: Search JSON documents with Informix (https://www NULL.ibm NULL.com/developerworks/community/blogs/idsdoc/entry/search_json_documents_with_informix?lang=en).

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

10
Dec

Use Hadoop to collect and analyze data with clusters of computer servers

Customers with large amounts of data, who are capable of supporting a distributed server architecture, as clusters, can benefit from a decision to implement Apache Hadoop® as the solution. The key operant principle is the notion of clusters. Readers eager to learn more about this benefit may want to take a few moments to review a short animation, titled Hadoop* Server Clusters with 10 Gigabit Intel® Ethernet, which is available for public viewing on a web site published by Intel.

I’m not recommending the video for the presentation of Intel’s high speed gigabit networking hardware. This segment takes up approximately the last 1-2 mins of the animation. But the opening section does more to present viewers with information about how Apache Hadoop is uniquely capable of adding value to any effort to implement data management and analytics architectures over comparatively lower cost server hardware than most of the hype otherwise available online on the notion of “big data”.

For readers looking for even more help drilling down to just what the value-add may amount to should a decision be made to implement Hadoop, a quick visit to a page on the MapR© web site titled What is Apache™ Hadoop®? (https://www NULL.mapr NULL.com/products/apache-hadoop) will likely be worth the effort. The short presentation on the page, in my opinion, provides useful information about why clusters of servers are uniquely capable of servicing as the repository for an enormous number of web pages filled with information.

Certainly market consumers have opted to implement Hadoop for a lot of other purposes than its original “reason to be” as an evolution of “a new style of data processing known as MapReduce” (which was developed by Google) as the MapR presentation points out. These implementations provide a lot of the support for arguments for the notion of “big data”, at least the ones short on hype and long on sensibility.

What’s missing from the MapR presentation are customer success stories/case studies. Fortunately anyone looking for this type of descriptive content on just how real life businesses can benefit from an implementation of Hadoop can simply visit a page of the Hortonworks web site titled They Do Hadoop (http://hortonworks NULL.com/customers/) and watch some of the videos.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

9
Dec

Hadoop attracts support from Microsoft and Intel

The Apache Hadoop project (http://hadoop NULL.apache NULL.org/#What+Is+Apache+Hadoop%3F) “develops open-source software for reliable, scalable, distributed computing” (quoted from the “What is Apache Hadoop?” section of the site). So it makes sense for Microsoft and Intel to enthusiastically support the project. Microsoft is deeply committed to its cloud, IaaS effort, Azure (http://www NULL.azure NULL.com), and one of the prime revenue generators for Intel is its Data Center Business (http://www NULL.intel NULL.com/content/www/us/en/search NULL.html?keyword=data%20center). Azure and Intel’s Data Center business are both all about lots and lots of computer servers. The former consumes servers, while the latter provides the CPUs driving them.

As I wrote in the previous post to this blog, it’s likely a majority of the enterprise consumer segment of the tech reader community maintains a questionable understanding of the notion of “big data”. But, when correctly understood, it should not be a stretch for readers to understand why the Apache Hadoop project (or its OpenStack (http://www NULL.openstack NULL.org) competitor) are positioned at the very core of this technology trend.

Microsoft and Intel are not the only mature ISVs looking to benefit from big data. IBM and EMC are two other champions with solutions on the market to add value for enterprises looking to implement Hadoop.

Intel ostensibly understands the ambiguity of the notion of “big data”, and the imperative of providing the enterprise business consumer with a clearer understanding of just what this buzzword is really all about. A section of the Intel web site, titled Big Data, What It Is, Why You Should Care, and How Companies Gain Competitive Advantage (http://www NULL.intel NULL.com/content/www/us/en/big-data/big-data-101-animation NULL.html) is an attempt to provide this information.

But Intel’s effort to educate the consumer, in my opinion, falls into the same swamp as a lot of the other hype before it can deliver on its promise. The amount of data may be growing exponentially, as the opening of the short Intel animation on the topic contends, but there are a lot of mature ISVs (Oracle, IBM, Microsoft, etc) with relational database management systems, designed for pricey big server hardware, which are capable of providing a columnar structure for the data.

Even when “unstructured data” is mentioned, the argument is shaky. there are solutions for enterprise consumers like Microsoft SharePoint (specifically, The Term Store service), which are designed to build a method of effectively pouring text data into an RDBMS, for example SQL Server (the terms are added to SQL Server and are used to tag the text strings identified in unstructured data).

I am not arguing for the sole use of traditional RDBMSs, with SQL tools to manage a data universe experiencing exponential growth. Rather, I think big data proponents (and Hadoop champions) need to perform a closer study on what the real benefits are of clustering servers and then articulate the message for their enterprise computing audience.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

10
Nov

Online conversations become even more valuable data as consumers implement new analytics designed to work with big data

On October 29, 2014, IBM and Twitter announced a partnership (https://www-03 NULL.ibm NULL.com/press/us/en/pressrelease/45265 NULL.wss). Under the terms of this partnership, Twitter will provide IBM with data. In turn, IBM will permit customers to use its IBM Watson Analytics to work with Twitter data.

The Twitter data is often referred to as the “fire hose”. According to Statistic Brain (http://www NULL.statisticbrain NULL.com/twitter-statistics/), an average day sees some 58,000,000 Tweets. So it should be fair to say any effort to collect this volume of information, and, then, to analyze it, falls into the big data and analytics category.

So just who would be interested in the Twitter “fire hose”, and why? Reading further in the IBM press release one finds a clue: “The first joint solution will integrate Twitter data with IBM ExperienceOne customer engagement solutions, allowing sales, marketing, and customer service professionals to map sentiment and behavior to better engage and support their customers.” A brief look at IBM’s web site for its ExperienceOne (http://www-01 NULL.ibm NULL.com/software/marketing-solutions/experienceone/) service reveals a data analytics offer targeted to Chief Marketing Officer (CMOs), who usually lead “marketing, merchandising, sales, and customer service” (quoted from the ExperienceOne web site).

For an ISV like IBM to offer data collection, analytics, and even predictive analytics solutions, and the services required to successfully implement them, to a target market of CMOs from Lines of Business (LoBs), represents a major shift in focus from IBM’s familiar market of CIOs and enterprise IT organizations. In turn, the ExperienceOne offer stands as a testimony as to how the path by which technology innovation enters the enterprise has shifted away from the CIO and over to leaders from LoBs. Bottom line, this deal is a further indicator of why CIOs and their enterprise IT organizations are playing much more catch up than used to be the case in the past. It also can be interpreted as an indicator of a bigger enterprise need for Enterprise Device Management (EDM) and Mobile Device Management (MDM) solutions.

In this writer’s opinion the IBM Twitter partnership is a milestone in the evolution of the value of online user data. The daily production of enormous volumes of unstructured data from Tweets becomes a commodity, which Twitter can profit from in an entirely different manner than other social media sites have been able to achieve in the past. One can argue Facebook is doing much the same thing. But there is no IBM in the middle of how Facebook interacts with its customers. The data collection, warehousing, analytics, and, finally, predictive analytics capabilities a player like IBM brings to the process substantially elevates the potential represented by the Twitter fire hose for the CMOs who will ultimately consume it.

There is certainly room for firms competing with IBM to attempt to apply the same structure (with, presumably, Twitter competitors) for consumers with, perhaps, similar objectives in mind. The important point for anyone following the businesses owning the data (meaning Twitter and its competitors) is the likely need to factor in a higher valuation, should this IBM Twitter partnership pay off.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved

31
Oct

Big Data, Business Intelligence, and Predictive Analytics all make up a segment of the Productivity theme of computing in late 2014

An important segment of the productivity theme for computing in the fall of 2014 is composed of big data, business intelligence and predictive analytics. Early stage ISVs with solutions in one of these three high demand needs will benefit by crafting market messages around the same productivity theme articulated by their more mature ISV siblings.

Here’s why:

Big Data

the information filtering implicit to the productivity theme, as this writer presented in a prior post to this blog, is a mission-critical component of the complete solution. So Big Data methods of collecting, reposing, categorizing, and, ultimately, processing information are invaluable to a successful effort to enhance productivity for the entire computing “ecosystem” from individual user to collections of organizations.

Business Intelligence (BI)

The BI toolset provides the user interface for the same range of computing users (meaning from individual to sets of organizations) to depict the comparative importance of segments of information and, subsequently, to assimilate it. The charts and other dashboard elements typical of BI presentations render the information into a form users can easily understand. This information, in turn, provides users with bases of action, as required.

Predictive Analytics (PA)

Machine learning is a popular term, which is widely used by players in the productivity market. Machine learning can be applied to the PA computing task. But PA can also be manually expressed by users. The objective of PA is consistently expressed across most productivity messaging as an effort to heighten the value of computing activity, and, ultimately, to increase return on investment and the value of computing activity.

The above points are merely suggestions for how an early stage ISV with a solution in one, or all three portions of this brand segment might choose to articulate a message. If you would like to hear more about how your business might benefit by building your brand within the context of the productivity theme articulated by each of the major ISVs, please don’t hesitate to contact us. We would be eager to learn more about what you are after. As well, we pursue opportunities to contribute to the success of this kind of marketing communications effort on a consulting basis.

Ira Michael Blonder

© IMB Enterprises, Inc. & Ira Michael Blonder, 2014 All Rights Reserved