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Archive for the ‘Information Development’ Category

by: John McClure
06  Mar  2014

Grover: A Business Syntax for Semantic English

Grover is a semantic annotation markup syntax based on the grammar of the English language. Grover is related to the Object Management Group’s Semantics of Business Vocabulary and Rules (SBVR), explained later. Grover syntax assigns roles to common parts of speech in the English language so that simple and structured English phrases are used to name and relate information on the semantic web. By having as clear a syntax as possible, the semantic web is more valuable and useful.

An important open-source tool for semantic databases is SemanticMediaWiki that permits everyone to create a personal “wikipedia” in which private topics are maintained for personal use. The Grover syntax is based on this semantic tool and the friendly wiki environment it delivers, though the approach below might also be amenable to other toolsets and environments.

Basic Approach. Within a Grover wiki, syntax roles are established for classes of English parts of speech.

  • Subject:noun(s) -- verb:article/verb:preposition -- Object:noun(s)

refines the standard Semantic Web pattern:

  • SubjectURL -- PredicateURL -- ObjectURLwhile in a SemanticMediaWiki environment, with its relative URLs, this is the pattern:
  • (Subject) Namespace:pagename -- (Predicate) Property:pagename -- (Object) Namespace:pagename.

 

nouns
In a Grover wiki, topic types are nouns, more precisely nounal expressions, are concepts. Every concept is defined by a specific semantic database query, these queries being the foundation of a controlled enterprise vocabulary. In Grover every pagename is the name of a topic and every pagename includes a topic-type prefix. Example: Person:Barack Obama and Title:USA President of the United States of America, two topics related together through one or more predicate relations, for instance “has:this”. Wikis are organized into ‘namespaces’ — its pages’ names are each prefixed with a namespace-name, which function equally as topic-type names. Additionally, an ‘interwiki prefix’ can indicate the URL of the wiki where a page is located — in a manner compatible with the Turtle RDF language.

Nouns (nounal expressions) are the names of topic-types and or of topics; in ontology-speak, nouns are class resources or nouns are individual resources but rarely are nouns defined as property resources (and thereby used as a ‘predicate’ in the standard Semantic Web pattern, mentioned above). This noun requirement is a systemic departure from today’s free-for-all that allows nouns to be part of the name of predicates, leading to the construction of problematic ontologies from the perspective of common users.verbsIn a Grover wiki, “property names” are an additional ontology component forming the bedrock of a controlled semantic vocabulary. Being pages in the “Property” namespace means these are prefixed with the namespace name, “Property”. However the XML namespace is directly implied, for instance has:this implies a “has” XML Namespace. The full pagename of this property is “Property:has:this. The tenses of a verb — infinitive, past, present and future — are each an XML namespace, meaning there are separate have, has, had and will-have XML Namespaces. The modalities of a verb are also separate XML Namespace, may and must. Lastly the negation form for verbs (involving not) are additional XML Namespaces.

The “verb” XML Namespace name is only one part of a property name. The other part of a property name is either a preposition or it is a grammatical author. Together, these comprise an enterprise’s controlled semantic vocabulary.

prepositions
As in English grammar, prepositions are used to relate an indirect object or object of a preposition, to a subject in a sentence. Example: “John is at the Safeway” uses a property named “is:at” to yield the triple Person:John -- is:at -- Store:Safeway. There are approximately about one hundred english prepositions possible for any particular verbal XML Namespace. Examples: had:from, has:until and is:in.
articles
As in English grammar, articles such as “a” and “the” are used to relate direct objects or predicate nominatives to a subject in a sentence. As for prepositions above, articles are associated with a verb XML Namespace. Example: has:a:, has:this, has:these, had:some has:some and will-have:some.

adjectivesIn a Grover wiki, definitions in the “category” namespace include adjectives, such as “Public” and “Secure”. These categories are also found in a controlled modifier vocabulary. The category namespace also includes definitions for past participles, such as “Secured” and “Privatized”. Every adjective and past participle is a category in which any topic can be placed. A third subclass of modifiers include ‘adverbs’, categories in which predicate instances are placed.

That’s about all that’s needed to understand Grover, the Business Syntax for Semantic English! Let’s use the Grover syntax to implement a snippet from the Object Management Group’s Semantics of Business Vocabulary and Rules (SBVR) which has statements such as this for “Adopted definition”:

adopted definition
Definition: definition that a speech community adopts from an external source by providing a reference to the definition.
Necessities: (1) The concept ‘adopted definition’ is included in Definition Origin. (2) Each adopted definition must be for a concept in the body of shared meanings of the semantic community of the speech community.

 

Now we can use Grover’s syntax to ‘adopt’ the OMG’s definition for “Adopted definition”.
Concept:Term:Adopted definition -- is:within -- Concept:Definition
Concept:Term:Adopted definition -- is:in -- Category:Adopted
Term:Adopted definition -- is:a -- Concept:Term:Adopted definition
Term:Adopted definition -- is:also -- Concept:Term:Adopted definition
Term:Adopted definition -- is:of -- Association:Object Management Group
Term:Adopted definition -- has:this -- Reference:http://www.omg.org/spec/SBVR/1.2/PDF/
Term:Adopted definition -- must-be:of -- Concept:Semantic Speech Community
Term:Adopted definition -- must-have:some -- Concept:Reference

This simplified but structured English permits the widest possible segment of the populace to participate in constructing and perfecting an enterprise knowledge base built upon the Resource Description Framework.

More complex information can be specified on wikipages using standard wiki templates. For instance to show multiple references on the “Term:Adopted definition” page, the “has:this” wiki template can be used:
{{has:this|Reference:http://www.omg.org/spec/SBVR/1.1/PDF/;Reference:http://www.omg.org/spec/SBVR/1.2/PDF/}}
Multi-lingual text values and resource references would be as follows, using the wiki templates (a) {{has:this}} and (b) {{skos:prefLabel}}
{{has:this |@=en|@en=Reference:http://www.omg.org/spec/SBVR/1.2/PDF/}}
{{skos:prefLabel|@=en;de|@en=Adopted definition|@de=Angenommen definition}}

One important feature of the Grover approach is its modification of our general understanding about how ontologies are built. Today, ontologies specify classes, properties and individuals; a data model emerges from listings of range/domain axioms associated with a propery’s definition. Instead under Grover, an ontology’s data models are explicitly stated with deontic verbs that pair subjects with objects; this is an intuitively stronger and more governable approach for such a critical enterprise resource as the ontology.

Category: Business Intelligence, Enterprise Content Management, Enterprise Data Management, Enterprise2.0, Information Development, Semantic Web
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by: Alandduncan
04  Mar  2014

The (Data) Doctor Is In: ADD looks for a data diagnosis…

Being a data management practitioner can be tough.

You’re expected to work your data quality magic, solve other people’s data problems, and help people get better business outcomes. It’s a valuable, worthy and satisfying profession. But people can be infuriating and frustrating, especially when the business user isn’t taking responsibility for the quality of their own data.

It’s a bit like being a Medical Doctor in general practice.

The patent presents with some early indicative symptoms. The MD then performs a full diagnosis and recommends a course of treatment. It’s then up to the patient whether or not they take their MD’s advice…

AlanDDuncan: “Doctor, Doctor. I get very short of breath when I go upstairs.”
MD: Yes, well. Your Body Mass Index is over 30, you’ve got consistently high blood pressure, your heatbeat is arrhythmic, and cholesterol levels are off the scale.”
ADD: “So what does that mean, doctor?”
MD: “It means you’re fat, you drink like a fish, you smoke like a chimney, your diet consists of fried food and cakes and you don’t do any exercise.”
ADD: “I’m Scottish.”
MD: “You need to change your lifestyle completely, or you’re going to die.”
ADD: “Oh. So, can you give me some pills?….”

If you’re going to get healthy with your data, you’ll going to have to put the pies down, step away from the Martinis and get off the couch folks.

Category: Business Intelligence, Data Quality, Information Development, Information Governance, Information Management, Information Strategy, Information Value, Master Data Management, Metadata
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by: Bsomich
01  Mar  2014

Community Update

Missed what happened in the MIKE2.0 community? Check out our bi-weekly update:

 

 
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Business Drivers for Better Metadata Management

There are a number Business Drivers for Better Metadata Management that have caused metadata management to grow in importance over the past few years at most major organisations. These organisations are focused on more than just a data dictionary across their information – they are building comprehensive solutions for managing business and technical metadata.

Our wiki article on the subject explores many factors contributing to the growth of metadata and guidance to better manage it:  

Feel free to check it out when you have a moment.

Sincerely,MIKE2.0 Community

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This Week’s Blogs for Thought:

Big Data Strategy: Tag, Cleanse, Analyze

Variety is the characteristic of big data that holds the most potential for exploitation, Edd Dumbill explained in his Forbes article Big Data Variety means that Metadata Matters. “The notion of variety in data encompasses the idea of using multiple sources of data to help understand a problem. Even the smallest business has multiple data sources they can benefit from combining. Straightforward access to a broad variety of data is a key part of a platform for driving innovation and efficiency.”

Read more.

The Race to the IoT is a Marathon

The PC era is arguably over and the age of ubiquitous computing might finally be here.  Its first incarnation has been mobility through smartphones and tablets.  Many pundits, though, are looking to wearable devices and the so-called “internet of things” as the underlying trends of the coming decade. It is tempting to talk about the internet of things as simply another wave of computing like the mainframe, mid-range and personal computer.  However there are as many differences as there are similarities.Read more.

The Data of Damocles

While the era of Big Data invokes concerns about privacy and surveillance, we still tender our privacy as currency for Internet/mobile-based services as the geo-location tags, date-time stamps, and other information associated with our phone calls, text messages, emails, and social networking status updates become the bits and bytes of digital bread crumbs we scatter along our daily paths as our self-surveillance avails companies and governments with the data needed to track us, target us with personalized advertising, and terrorize us with the thought of always being watched.

Read more.

 

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Category: Information Development
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by: Ocdqblog
25  Feb  2014

Big Data Strategy: Tag, Cleanse, Analyze

Variety is the characteristic of big data that holds the most potential for exploitation, Edd Dumbill explained in his Forbes article Big Data Variety means that Metadata Matters. “The notion of variety in data encompasses the idea of using multiple sources of data to help understand a problem. Even the smallest business has multiple data sources they can benefit from combining. Straightforward access to a broad variety of data is a key part of a platform for driving innovation and efficiency.”

But the ability to take advantage of variety, Dumbill explained, is hampered by the fact that most “data systems are geared up to expect clean, tabular data of the sort that flows into relational database systems and data warehouses. Handling diverse and messy data requires a lot of cleanup and preparation. Four years into the era of data scientists, most practitioners report that their primary occupation is still obtaining and cleaning data sets. This forms 80% of the work required before the much-publicized investigational skill of the data scientist can be put to use.”

Which begs the question Mary Shacklett asked with her TechRepublic article Data quality: The ugly duckling of big data? “While it seems straightforward to just pull data from source systems,” Shacklett explained, “when all of this multifarious data is amalgamated into vast numbers of records needed for analytics, this is where the dirt really shows.” But somewhat paradoxically, “cleaning data can be hard to justify for ROI, because you have yet to see what clean data is going to deliver to your analytics and what the analytics will deliver to your business.”

However, Dumbill explained, “to focus on the problems of cleaning data is to ignore the primary problem. A chief obstacle for many business and research endeavors is simply locating, identifying, and understanding data sources in the first place, either internal or external to an organization.”

This is where metadata comes into play, providing a much needed context for interpreting data and helping avoid semantic inconsistencies that can stymie our understanding of data. While good metadata has alway been a necessity, big data needs even better metadata. “The documentation and description of datasets with metadata,” Dumbill explained, “enhances the discoverability and usability of data both for current and future applications, as well as forming a platform for the vital function of tracking data provenance.”

“The practices and tools of big data and data science do not stand alone in the data ecosystem,” Dumbill concluded. “The output of one step of data processing necessarily becomes the input of the next.” When approaching big data, the focus on analytics, as well as concerns about data quality, not only causes confusion about the order of those steps, but also overlooks the important role that metadata plays in the data ecosystem.

By enhancing the discoverability of data, metadata essentially replaces hide-and-seek with tag. As we prepare for a particular analysis, metadata enables us to locate and tag the data most likely to prove useful. After we tag which data we need, we can then cleanse that data to remove any intolerable defects before we begin our analysis. These three steps—tag, cleanse, analyze—form the basic framework of a big data strategy.

It all begins with metadata management. As Dumbill said, “it’s not glamorous, but it’s powerful.”

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Category: Data Quality, Information Development
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by: Bsomich
15  Feb  2014

MIKE2.0 Community Update.

Missed what happened in the MIKE2.0 community this week? Read our community update below:

 
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Data Governance: How competent is your organization?

One of the key concepts of the MIKE2.0 Methodology is that of an Organisational Model for Information Development. This is an organisation that provides a dedicated competency for improving how information is accessed, shared, stored and integrated across the environment.

Organisational models need to be adapted as the organisation moves up the 5 Maturity Levels for organisations in relation to the Information Development competencies below:

Level 1 Data Governance Organisation – Aware

  • An Aware Data Governance Organisation knows that the organisation has issues around Data Governance but is doing little to respond to these issues. Awareness has typically come as the result of some major issues that have occurred that have been Data Governance-related. An organisation may also be at the Aware state if they are going through the process of moving to state where they can effectively address issues, but are only in the early stages of the programme.
Level 2 Data Governance Organisation – Reactive
  • A Reactive Data Governance Organisation is able to address some of its issues, but not until some time after they have occurred. The organisation is not able to address root causes or predict when they are likely to occur. “Heroes” are often needed to address complex data quality issues and the impact of fixes done on a system-by-system level are often poorly understood.
Level 3 Data Governance Organisation – Proactive
  • A Proactive Data Governance Organisation can stop issues before they occur as they are empowered to address root cause problems. At this level, the organisation also conducts ongoing monitoring of data quality to issues that do occur can be resolved quickly.
Level 4 Data Governance Organisation – Managed
Level 5 Data Governance Organisation – Optimal

The MIKE2.0 Solution for the the Centre of Excellence provides an overall approach to improving Data Governance through a Centre of Excellence delivery model for Infrastructure Development and Information Development. We recommend this approach as the most efficient and effective model for building these common set of capabilities across the enterprise environment.

Feel free to check it out when you have a moment and offer any suggestions you may have to improve it.

Sincerely,

MIKE2.0 Community

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This Week’s Blogs for Thought:

Share the Love… of Data Quality!

A recent news article on Information-Management.com suggested a link between inaccurate data and “lack of a centralized approach.” But I’m not sure that “lack of centralization” is the underlying issue here; I’d suggest the challenge is generally more down to “lack of a structured approach”, and as I covered in my blog post “To Centralise or not to Centralise, that is the Question”, there are organizational cultures that don’t respond well (or won’t work at all) to a centralized approach to data governance.

Read more.

Avoid Daft Definitions for Sound Semantics

A few weeks ago, while reading about the winners at the 56th Annual Grammy Awards, I saw that Daft Punk won both Record of the Year and Album of the Year, which made me wonder what the difference is between a record and an album. Then I read that Record of the Year is awarded to the performer and the production team of a single song. While Daft Punk won Record of the Year for their song “Get Lucky”, the song was not lucky enough to win Song of the Year (that award went to Lorde for her song “Royals”). My confusion about the semantics of the Grammy Awards prompted a quick trip to Wikipedia, where I learned that Record of the Year is awarded for either a single or individual track from an album.

Read more.

Social Data: Asset and Liability

In late December of 2013, Google Chairman Eric Schmidt admitted that ignoring social networking had been a big mistake. ”I guess, in our defense, we were busy working on many other things, but we should have been in that area and I take responsibility for that,” he said.
Brass tacks: Google’s misstep and Facebook’s opportunistic land grab of social media have resulted in a striking data chasm between the two behemoths. As a result, Facebook can do something that Google just can’t.

Read more.

Forward to a Friend!Know someone who might be interested in joining the Mike2.0 Community? Forward this message to a friend

Questions? Please email us at mike2@openmethodology.org.

Category: Information Development
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by: Alandduncan
11  Feb  2014

Share the love… of Data Quality

A distributed approach to Data Quality may result in better data outcomes

A recent news article on Information-Management.com suggested a link between inaccurate data and “lack of a centralized approach.”

But I’m not sure that “lack of centralization” is the underlying issue here; I’d suggest the challenge is generally more down to “lack of a structured approach”, and as I covered in my blog post “To Centralise or not to Centralise, that is the Question”, there are organizational cultures that don’t respond well (or won’t work at all) to a centralized approach to data governance.

When you then extend this to the more operational delivery processes of Data Quality Management, I’d go so far as to suggest that a distributed and end-user oriented approach to managing data quality is actually desirable, for several reasons:

* Many organisations just haven’t given data quality due consideration, and the impacts can be significant, but often hidden.
* Empowering users to thinks about and act upon data challenges can become a catalyst for a more structured, enterprise wide approach.
* By managing data quality issues locally, knowledge and expertise is maintained as close to point-of-use as possible.
* In environments where funding is hard to come by or where there isn’t appetite to establish critical mass for data quality activity, progress can still be made and value can still be delivered

I also observe two trends in business, that have been consistent in the twenty-plus years that I’ve been working, which are contributing to make a centralised delivery of data outcomes ever-more difficult:

1) Human activity has become more and more complex. We’re living in a mobile, connected, graphical, multi-tasking, object-oriented, cloud-serviced world, and the rate at which we’re collecting data is showing no sign of abatement. It may well be that our data just isn’t “controllable” in the classic sense any more, and that what’s really needed is mindfulness. (I examined this in my post “Opening Pandora’s Box“)

2) Left to their own devices, business systems and processes will tend to decay towards a chaotic state over time, and it is management’s role to keep injecting focus and energy into the organisation. If this effort can be spread broadly across the organisation, then there is an overall cultural change towards better data. (I covered aspects of this in my post “Business Entropy – Bringing Order to the Chaos“)

Add the long-standing preoccupation that management consultants have with mapping “Business Process” rather than mapping “Business Data” and you end up in the situation that data does not get nearly enough attention. (And once attention IS payed, then the skills and capabilities to do something about it are often lacking).

Change the culture, change the result – that doesn’t require centralisation to make it happen.

Category: Information Development
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by: Ocdqblog
11  Feb  2014

Avoid Daft Definitions for Sound Semantics

A few weeks ago, while reading about the winners at the 56th Annual Grammy Awards, I saw that Daft Punk won both Record of the Year and Album of the Year, which made me wonder what the difference is between a record and an album.

Then I read that Record of the Year is awarded to the performer and the production team of a single song. While Daft Punk won Record of the Year for their song “Get Lucky”, the song was not lucky enough to win Song of the Year (that award went to Lorde for her song “Royals”).

My confusion about the semantics of the Grammy Awards prompted a quick trip to Wikipedia, where I learned that Record of the Year is awarded for either a single or individual track from an album. This award goes to the performer and the production team for that one song. In this context, record means a particular recorded song, not its composition or an album of songs.

Although Song of the Year is also awarded for a single or individual track from an album, the recipient of this award is the songwriter who wrote the lyrics to the song. In this context, song means the song as composed, not its recording.

The Least Ambiguous Award goes to Album of the Year, which is indeed awarded for a whole album. This award goes to the performer and the production team for that album. In this context, album means a recorded collection of songs, not the individual songs or their compositions.

These distinctions, and the confusion it caused me, seemed eerily reminiscent of the challenges that happen within organizations when data is ambiguously defined. For example, terms like customer and revenue are frequently used without definition or context. When data definitions are ambiguous, it can easily lead to incorrect uses of data as well as confusing references to data during business discussions.

Not only is it difficult to reach consensus on data definitions, definitions change over time. For example, Record of the Year used to be awarded to only the performer, not the production team. And the definition of who exactly counts as a member of the production team has been changed four times over the years, most recently in 2013.

Avoiding semantic inconsistencies, such as the difference between a baker and a Baker, is an important aspect of metadata management. Be diligent with your data definitions and avoid daft definitions for sound semantics.

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Category: Information Development
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by: Phil Simon
09  Feb  2014

Social Data: Asset and Liability

In late December of 2013, Google Chairman Eric Schmidt admitted that ignoring social networking had been a big mistake. ”I guess, in our defense, we were busy working on many other things, but we should have been in that area and I take responsibility for that,” he said.

Brass tacks: Google’s misstep and Facebook’s opportunistic land grab of social media have resulted in a striking data chasm between the two behemoths. As a result, Facebook can do something that Google just can’t.

To his credit, Mark Zuckerberg has not been complacent with this lead. This is an age of ephemera. He is building upon his company’s lead in social data. Case in point: the launch of Graph Search.

The rationale here is pretty straightforward: Why let Google catch up? With Graph Search, Facebook users can determine which of their friends have gone to a Mexican restaurant in the last six months in San Francisco. What about which friends like the Rolling Stones or The Beatles? (Need to resell a ticket? Why use StubHub here? Maybe Facebook gets a cut of the transaction?) These are questions and problems that Google can’t address but Facebook can.

All good for Zuck et. al, right? Not really. It turns out that delivering relevant social data in a timely manner is proving remarkably elusive, even for the smart cookies at Facebook.

The New News Feeds

As Wired reported in May of 2013, Facebook “redesigned its News Feed with bolder images and special sections for friends, photos, and music, saying the activity stream will become more like a ‘personalized newspaper’ that fits better with people’s mobile lifestyles.” Of course, many users didn’t like the move, but that’s par for the course these days. You’re never going to make 1.2 billion users happy.

But Facebook quickly realized that it didn’t get the relaunch of News Feed right. Not even close. Just a few weeks before Schimdt’s revealing quote, Business Insider reported that Facebook was at it again, making major tweaks to its feed and then halting its new launch. This problem has no simple solution.

Simon Says: Big Data Is a Full-Time Job

Big Data is no picnic. “Managing” it isn’t easy, even for billion-dollar companies such as Facebook. The days of “set it and forget it” have long passed. Organizations need to be constantly monitoring the effectiveness of their data-driven products and services, to say nothing of testing for security issues. (Can someone say Target?)

Feedback

What say you?

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Category: Information Development
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by: Bsomich
01  Feb  2014

Missed what happened in the MIKE2.0 Community this week?

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Big Data Solution Offering 

Have you seen the latest offering in our Composite Solutions suite?

The Big Data Solution Offering provides an approach for storing, managing and accessing data of very high volumes, variety or complexity.

Storing large volumes of data from a large variety of data sources in traditional relational data stores is cost-prohibitive, and regular data modeling approaches and statistical tools cannot handle data structures with such high complexity. This solution offering discusses new types of data management systems based on NoSQL database management systems and MapReduce as the typical programming model and access method.

Read our Executive Summary for an overview of this solution.

We hope you find this new offering of benefit and welcome any suggestions you may have to improve it.

Sincerely,

MIKE2.0 Community

Popular Content

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What is MIKE2.0?
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This Week’s Blogs for Thought:

Your data is in the cloud when…

It’s fashionable to be able to claim that you’ve moved everything from your email to your enterprise applications “into the cloud”.  But what about your data?  Just because information is stored over the Internet, it shouldn’t necessarily qualify as being “in the cloud.”

Read more.

The Top of the Data Quality Bell Curve

“Information is the value associated with data,” William McKnight explains in his book Information Management: Strategies for Gaining a Competitive Advantage with Data.  “Information is data under management that can be utilized by the company to achieve goals.”  Does that data have to be perfect in order to realize its value and enable the company to achieve its goals?  McKnight says no.
Data quality, according to McKnight, “is the absence of intolerable defects.”

Read more.

Business Entropy: Bringing order to the chaos

In his recent post, Jim Harris drew an analogy between the interactions of atomic particles, sub-atomic forces and the working of successful collaborative teams, and coined the term ego-repulsive force. Jim’s post put me in mind of another aspect of physics that I think has parallels with our business world – the Second Law of Thermodynamics, and the concept of entropy.

In thermodynamics, “entropy” describes a measure of the number of ways a closed system can be arranged; such systems spontaneously evolve towards a state of equilibrium, which are at maximum entropy (and therefore, maximum disorder). I observe that this mechanism also holds true for the workings of organisations – and there is a law of Business Entropy at work.

Read more.

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If you have any questions, please email us at mike2@openmethodology.org.

 

Category: Information Development
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by: Phil Simon
31  Jan  2014

Google Only Gets You to a Certain Point

I love Google and in a pretty unhealthy way. In my third book, The New Small, there are oodles references to Google products and services. I use Google on a daily basis for all sorts of different things, including e-mail, document sharing, phone calls, calendars, and Hangouts.

And one more little thing: search. I can’t imagine ever “Binging” something and, at least in the U.S., most people don’t either.

Yet, there are limitations to Google and, in this post, I am going to discuss one of the main ones.

A few years ago, I worked on a project doing some data migration. I supported one line of business (LOB) for my client while another consultant (let’s call him Mark) supported a separate LOB. Mark and I worked primarily with Microsoft Access. The organization ultimately wanted to move toward an enterprise-grade database, in all likelihood SQL Server.

Relying Too Much on Google

Mark was a nice guy. At the risk of being immodest, though, his Access and data chops weren’t quite on my level. He’d sometimes ask me questions about how to do some relatively basic things, such as removing duplicates. (Answer: SELECT DISTINCT.) When he had more difficult questions, I would look at his queries and see things that just didn’t make a whole lot of sense. For example, he’d try to write one massive query that did everything, rather than breaking them up into individual parts.

Now, I am very aware that development methodologies vary and there’s no “right” one. Potato/pot-ah-to, right? Also, I didn’t mind helping Mark–not at all. I’ll happily share knowledge, especially when I’m not pressed with something urgent.

Mark did worry me, though, when I asked him if he knew SQL Server better than MS Access. “No,” he replied. “I’ll just Google whatever I need.”

Hmm.

For doing research and looking up individual facts, Google rocks. Finding examples of formulas or SQL statements isn’t terribly difficult either. But one does not learn to use a robust tool like SQL Server or even Access by merely using a search engine. You don’t design an enterprise system via Google search results. You don’t build a data model, one search at a time. These things require a much more profound understanding of the process.

In other words, there’s just no replacement for reading books, playing with applications, taking courses, understanding higher-level concepts, rather than just workarounds, and overall experience.

Simon Says

You don’t figure out how to play golf while on the course. You go to the practice range. I’d hate to go to a foreign country without being able to speak the language–or accompanied by someone who can. Yes, I could order dinner with a dictionary, but what if a doctor asked me in Italian where the pain was coming from?

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