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Archive for the ‘Information Development’ Category

by: Bsomich
19  Jul  2014

Community Update.

Missed what’s been happening in the MIKE2.0 community? Check out our bi-weekly update:

 

 
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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
  • 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
  • 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:

Big Data 101

Big data. What exactly is it?

Big data has been hitting the headlines in 2014 more so than any other year. For some people that’s no surprise, and they have a grasp on what big data really is. For others, the two words “big” and “data” don’t conjure up of a meaning of any kind, but instead they stir confusion and many times, misunderstanding.

Read more.

Dinosaurs, Geologists, and the IT-Business Divide

“Like it or not, we live in a tech world, from Apple to Hadoop to Zip files. You can’t ignore the fact that technology touches every facet of our lives. Better to get everything you can, leveraging every byte and every ounce of knowledge IT can bring.”

So write Thomas C. Redman and Bill Sweeney on HBR. Of course, they’re absolutely right. But the tech gap between what organizations can and actually accomplish remains considerable.

Read more.

Data Quality Profiling Considerations

Data profiling is an excellent diagnostic method for gaining additional understanding of the data. Profiling the source data helps inform both business requirements definition and detailed solution designs for data-related project, as well as enabling data issues to be managed ahead of project implementation.

Read more.

 

  

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Category: Information Development
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by: Alandduncan
19  Jul  2014

Data Quality Profiling Considerations

Data profiling is an excellent diagnostic method for gaining additional understanding of the data. Profiling the source data helps inform both business requirements definition and detailed solution designs for data-related project, as well as enabling data issues to be managed ahead of project implementation.

Profiling of a data set will be measured with reference to and agreed Data Quality Dimensions (e.g. per those proposed in the recent DAMA white paper).

Profiling may be required at several levels:

• Simple profiling with a single table (e.g. Primary Key constraint violations)
• Medium complexity profiling across two or more interdependent tables (e.g. Foreign Key violations)
• Complex profiling across two or more data sets, with applied business logic (e.g. reconciliation checks)

Note that field-by-field analysis is required to truly understand the data gaps.

Any data profiling analysis must not only identify the issues and underlying root causes, but must also identify the business impact of the data quality problem (measured by effectiveness, efficiency, risk inhibitors). This will help identify any value in remediating the data – great for your data quality Business Case. Root cause analysis also helps identify any process outliers and and drives out requirements for remedial action on managing any identified exceptions.

Be sure to profile your data and take baseline measures before applying any remedial actions – this will enable you to measure the impact of any changes.

I strongly recommend Data Quality Profiling and root-cause analysis to be undertaken as an initiation activity as part of all data warehouse, master data and application migration project phases.

Category: Business Intelligence, Data Quality, Enterprise Data Management, Information Development, Information Governance, Information Management, Information Strategy, Information Value, Metadata
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by: Bsomich
12  Jul  2014

Community Update.

Missed what’s been happening in the MIKE2.0 community? Check out our biweekly update for the latest blog posts, wiki articles and information management resources:

 

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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:

Information Requirements Gathering: The One Question You Must Never Ask 

Over the years, I’ve tended to find that asking any individual or group the question “What data/information do you want?” gets one of two responses:

“I don’t know.” Or;
“I don’t know what you mean by that.”

End of discussion, meeting over, pack up go home, nobody is any the wiser. Result? IT makes up the requirements based on what they think the business should want, the business gets all huffy because IT doesn’t understand what they need, and general disappointment and resentment ensues. Clearly for Information Management & Business Intelligence solutions, this is not a good thing.

Read more.

Overcoming Outcome Bias

What is more important, the process or its outcome? Information management processes, like those described by the MIKE2.0 Methodology, drive the daily operations of an organization’s business functions as well as support the tactical and strategic decision-making processes of its business leaders. However, an organization’s success or failure is usually measured by the outcomes produced by those processes.

As Duncan Watts explained in his book Everything Is Obvious: How Common Sense Fails Us, “rather than the evaluation of the outcome being determined by the quality of the process that led to it, it is the observed nature of the outcome that determines how we evaluate the process.” This is known as outcome bias.

Read more.

Evernote’s Three Laws of Data Protection

“It’s all about bucks, kid. The rest is conversation.” –Michael Douglass as Gordon Gekko, Wall Street (1987) Sporting more than 60 million users, Evernote is one of the most popular productivity apps out there these days. You may in fact use the app to store audio notes, video, pics, websites, and perform a whole host of other tasks.Read more.

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by: Alandduncan
01  Jul  2014

Information Requirements Gathering: The One Question You Must Never Ask!

Over the years, I’ve tended to find that asking any individual or group the question “What data/information do you want?” gets one of two responses:

“I don’t know.” Or;

“I don’t know what you mean by that.”

End of discussion, meeting over, pack up go home, nobody is any the wiser. Result? IT makes up the requirements based on what they think the business should want, the business gets all huffy because IT doesn’t understand what they need, and general disappointment and resentment ensues.

Clearly for Information Management & Business Intelligence solutions, this is not a good thing.

So I’ve stopped asking the question. Instead, when doing requirements gathering for an information project, I go through a workshop process that follows the following outline agenda:

Context setting: Why information management / Business Intelligence / Analytics / Data Governance* is generally perceived to be a “good thing”. This is essentially a very quick précis of the BI project mandate, and should aim at putting people at ease by answering the question “What exactly are we all doing here?”

(*Delete as appropriate).

Business Function & Process discovery: What do people do in their jobs – functions & tasks? If you can get them to explain why they do those things – i.e. to what end purpose or outcome – so much the better (though this can be a stretch for many.)

Challenges: what problems or issues do they currently face in their endeavours? What prevents them from succeeding in their jobs? What would they do differently if they had the opportunity to do so?

Opportunities: What is currently good? Existing capabilities (systems, processes, resources) are in place that could be developed further or re-used/re-purposed to help achieve the desired outcomes?

Desired Actions: What should happen next?

As a consultant, I see it as part of my role to inject ideas into the workshop dialogue too, using a couple of question forms specifically designed to provoke a response:

“What would happen if…X”

“Have you thought about…Y”

“Why do you do/want…Z”.

Notice that as the workshop discussion proceeds, the participants will naturally start to explore aspects that relate to later parts of the agenda – this is entirely ok. The agenda is there to provide a framework for the discussion, not a constraint. We want people to open up and spill their guts, not clam up. (Although beware of the “rambler” who just won’t shut up but never gets to the point…)

Notice also that not once have we actively explored the “D” or “I” words. That’s because as you explore the agenda, any information requirements will either naturally fall out of the discussion as it proceed, or else you can infer the information requirements arising based on the other aspects of the discussion.

As the workshop attendees explore the different aspects of the session, you will find that the discussion will touch upon a number of different themes, which you can categorise and capture on-the-fly (I tend to do this on sheets of butchers paper tacked to the walls, so that the findings are shared and visible to all participants.). Comments will typically fall into the following broad categories:

* Functions: Things that people do as part of doing business.
* Stakeholders: people who are involved (including helpful people elsewhere in the organisation – follow up with them!)
* Inhibitors: Things that currently prevent progress (these either become immediate scope-change items if they are show-stoppers for the current initiative, or else they form additional future project opportunities to raise with management)
* Enablers: Resources to make use of (e.g. data sets that another team hold, which aren’t currently shared)
* Constraints: “non-negotiable” aspects that must be taken into account. (Note: I tend to find that all constraints are actually negotiable and can be overcome if there is enough desire, money and political will.)
* Considerations: Things to be aware of that may have an influence somewhere along the line.
* Source systems: places where data comes from
* Information requirements: Outputs that people want

Here’s a (semi) fictitious example:

e.g. ADD: “What does your team do?”

Workshop Victim Participant #1: “Well, we’re trying to reconcile the customer account balances with the individual transactions.”

ADD: And why do you wan to do that?

Workshop Victim Participant #2: “We think there’s a discrepancy in the warehouse stock balances, compared with what’s been shipped to customers. The sales guys keep their own database of customer contracts and orders and Jim’s already given us dump of the data, while finance run the accounts receivables process. But Sally the Accounts Clerk doesn’t let the numbers out under any circumstances, so basically we’re screwed.”

Functions: Sales Processing, Contract Mangement, Order Fulfilment, Stock Management, Accounts Receivable.
Stakeholders: Warehouse team, Sales team (Jim), Finance team.
Inhibitors: Finance don’t collaborate.
Enablers: Jim is helpful.
Source Systems: Stock System, Customer Database, Order Management, Finance System.
Information Requirements: Orders (Quantity & Price by Customer, by Salesman, by Stock Item), Dispatches (Quantity & Price by Customer, by Salesman, by Warehouse Clerk, by Stock Item), Financial Transactions (Value by Customer, by Order Ref)

You will also probably end up with the attendees identifying a number of immediate self-assigned actions arising from the discussion – good ideas that either haven’t occurred to them before or have sat on the “To-Do” list. That’s your workshop “value add” right there….

e.g.
Workshop Victim Participant #1: “I could go and speak to the Financial Controller about getting access to the finance data. He’s more amenable to working together than Sally, who just does what she’s told.”

Happy information requirements gathering!

Category: Business Intelligence, Data Quality, Enterprise Data Management, Information Development, Information Governance, Information Management, Information Strategy, Information Value, Master Data Management, Metadata
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by: Ocdqblog
29  Jun  2014

Overcoming Outcome Bias

What is more important, the process or its outcome? Information management processes, like those described by the MIKE2.0 Methodology, drive the daily operations of an organization’s business functions as well as support the tactical and strategic decision-making processes of its business leaders. However, an organization’s success or failure is usually measured by the outcomes produced by those processes.

As Duncan Watts explained in his book Everything Is Obvious: How Common Sense Fails Us, “rather than the evaluation of the outcome being determined by the quality of the process that led to it, it is the observed nature of the outcome that determines how we evaluate the process.” This is known as outcome bias.

While an organization is enjoying positive outcomes, such as exceeding its revenue goals for the current fiscal period, outcome bias basks processes in a rose-colored glow. Information management processes must be providing high-quality data to decision-making processes, which business leaders are using to make good decisions. However, when an organization is suffering from negative outcomes, such as a regulatory compliance failure, outcome bias blames it on broken information management processes and poor data quality that lead to bad decision-making.

“Judging the merit of a decision can never be done simply by looking at the outcome,” explained Jeffrey Ma in his book The House Advantage: Playing the Odds to Win Big In Business. “A poor result does not necessarily mean a poor decision. Likewise a good result does not necessarily mean a good decision.”

“We are prone to blame decision makers for good decisions that worked out badly and to give them too little credit for successful moves that appear obvious after the fact,” explained Daniel Kahneman in his book Thinking, Fast and Slow.

While risk mitigation is an oft-cited business justification for investing in information management, Kahneman also noted how outcome bias can “bring undeserved rewards to irresponsible risk seekers, such as a general or an entrepreneur who took a crazy gamble and won. Leaders who have been lucky are never punished for having taken too much risk. Instead, they are believed to have had the flair and foresight to anticipate success. A few lucky gambles can crown a reckless leader with a halo of prescience and boldness.”

Outcome bias triggers overreactions to both success and failure. Organizations that try to reverse engineer a single, successful outcome into a formal, repeatable process often fail, much to their surprise. Organizations also tend to abandon a new process immediately if its first outcome is a failure. “Over time,” Ma explained, “if one makes good, quality decisions, one will generally receive better outcomes, but it takes a large sample set to prove this.”

Your organization needs solid processes governing how information is created, managed, presented, and used in decision-making. Your organization also needs to guard against outcomes biasing your evaluation of those processes.

In order to overcome outcome bias, Watts recommended we “bear in mind that a good plan can fail while a bad plan can succeed—just by random chance—and therefore judge the plan on its own merits as well as the known outcome.”

 

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Category: Data Quality, Information Development
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by: Bsomich
21  Jun  2014

Community Update.

 

 
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Have you seen our Open MIKE Series? 

The Open MIKE Podcast is a video podcast show which discusses aspects of the MIKE2.0 framework, and features content contributed to MIKE 2.0 Wiki Articles, Blog Posts, and Discussion Forums.

You can scroll through the Open MIKE Podcast episodes below:

For more information on MIKE2.0 or how to get involved with our online community, please visit www.openmethodology.org.

Sincerely,

MIKE2.0 Community  

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This Week’s Blogs for Thought:

Open and Secure Personal DataIn his book Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation, Joel Gurin explained a type of Open Data called Smart Disclosure, which was defined as “the timely release of complex information and data in standardized, machine-readable formats in ways that enable consumers to make informed decisions.”
As Gurin explained, “Smart Disclosure combines government data, company information about products and services, and data about an individual’s own needs to help consumers make personalized decisions. Since few people are database experts, most will use this Open Data through an intermediary—a choice engine that integrates the data and helps people filter it by what’s important to them, much the way travel sites do for airline and hotel booking. These choice engines can tailor the options to fit an individual’s circumstances, budget, and priorities.”

Read more.

Careers in Technology: Is there a future?

Is there a future for careers in Information Technology?  Globally, professional societies such as the British Computer Society and the Australian Computer Society have long argued that practitioners need to be professionals.  However, there is a counter-argument that technology is an enabler for all professions and is more generally a capability of many rather than a profession of the few.

Read more.

An Open Source Solution for Better Performance Management 

Today, many organizations are facing increased scrutiny and a higher level of overall performance expectation from internal and external stakeholders. Both business and public sector leaders must provide greater external and internal transparancy to their activities, ensure accounting data faces up to compliance challenges, and extract the return and competitive advantage out of their customer, operational and performance information: Managers, investors and regulators have a new perspective on performance and compliance.

Read more.

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Category: Information Development
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by: Ocdqblog
17  Jun  2014

Open and Secure Personal Data

In his book Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation, Joel Gurin explained a type of Open Data called Smart Disclosure, which was defined as “the timely release of complex information and data in standardized, machine-readable formats in ways that enable consumers to make informed decisions.”

As Gurin explained, “Smart Disclosure combines government data, company information about products and services, and data about an individual’s own needs to help consumers make personalized decisions. Since few people are database experts, most will use this Open Data through an intermediary—a choice engine that integrates the data and helps people filter it by what’s important to them, much the way travel sites do for airline and hotel booking. These choice engines can tailor the options to fit an individual’s circumstances, budget, and priorities.”

Remember (if you are old enough) what it was like to make travel arrangements before websites like Expedia, Orbitz, Travelocity, Priceline, and Kayak existed, and you can imagine the immense consumer-driven business potential for applying Smart Disclosure and choice engines to every type of consumer decision.

“Smart Disclosure works best,” Gurin explained, “when it brings together data about the services a company offers with data about the individual consumer. Smart Disclosure includes giving consumers data about themselves—such as their medial records, cellphone charges, or patterns of energy use—so they can choose the products and services uniquely suited to their needs. This is Open Data in a special sense: it’s open only to the individual whom the data is about and has to be released to each person under secure conditions by the company or government agency that holds the data. It’s essential that these organizations take special care to be sure the data is not seen by anyone else. Many people may balk at the idea of having their personal data released in a digital form. But if the data is kept private and secure, giving personal data back to individuals is one of the most powerful aspects of Smart Disclosure.”

Although it sounds like a paradox, the best way to secure our personal data may be to make it open. Currently most of our own personal data is closed—especially to us, which is the real paradox.

Some of our personal data is claimed as proprietary information by the companies we do business with. Data about our health is cloaked by government regulations intended to protect it, but which mostly protects doctors from getting sued while giving medical service providers and health insurance companies more access to our medical history than we have.

If all of our personal data was open to us, and we controlled the authorization of secure access to it, our personal data would be both open and secure. This would simultaneously protect our privacy and improve our choice as consumers.

 

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Category: Information Development
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by: Bsomich
07  Jun  2014

Community Update.

Missed what’s been happening in the MIKE2.0 community? Check out our bi-weekly update:

 

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Available for Order: Information Development Using MIKE2.0

Have you heard? Our new book, “Information Development Using MIKE2.0” is available for order.

The vision for Information Development and the MIKE2.0 Methodology have been available in a collaborative, online fashion since 2006, and are now made available in print publication to a wider audience, highlighting key wiki articles, blog posts, case studies and user applications of the methodology.

Authors for the book include Andreas Rindler, Sean McClowry, Robert Hillard, and Sven Mueller, with additional credit due to Deloitte, BearingPoint and over 7,000 members and key contributors of the MIKE2.0 community. The book has been published in paperback as well as all major e-book publishing platforms.

Get Involved:

To get your copy of the book, visit our order page on Amazon.com. For more information on MIKE2.0 or how to get involved with our online community, please visitwww.openmethodology.org.

Sincerely,

MIKE2.0 Community  

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This Week’s Blogs for Thought:

Open and Big Data

In his book Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation, Joel Gurin explained that Open Data and Big Data are related but very different.

While various definitions exist, Gurin noted that “all definitions of Open Data include two basic features: the data must be publicly available for anyone to use, and it must be licensed in a way that allows for its reuse.

Read more.

Careers in Technology: Is there a future?

Is there a future for careers in Information Technology?  Globally, professional societies such as the British Computer Society and the Australian Computer Society have long argued that practitioners need to be professionals.  However, there is a counter-argument that technology is an enabler for all professions and is more generally a capability of many rather than a profession of the few.

Read more.

Data Quality Profiling: Do you trust in the dark arts? 

Why estimating Data Quality profiling doesn’t have to be guess-work. 

Data Management lore would have us believe that estimating the amount of work involved in Data Quality analysis is a bit of a “Dark Art,” and to get a close enough approximation for quoting purposes requires much scryingharuspicy and wet-finger-waving, as well as plenty of general wailing and gnashing of teeth. (Those of you with a background in Project Management could probably argue that any type of work estimation is just as problematic, and that in any event work will expand to more than fill the time available).

Read more.

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Category: Information Development
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by: Ocdqblog
28  May  2014

Open Data and Big Data

In his book Open Data Now: The Secret to Hot Startups, Smart Investing, Savvy Marketing, and Fast Innovation, Joel Gurin explained that Open Data and Big Data are related but very different.

While various definitions exist, Gurin noted that “all definitions of Open Data include two basic features: the data must be publicly available for anyone to use, and it must be licensed in a way that allows for its reuse. Open Data should also be in a form that makes it relatively easy to use and analyze, although there are gradations of openness. And there’s general agreement that Open Data should be free of charge or cost just a minimal amount.”

“Big Data involves processing very large datasets to identify patterns and connections in the data,” Gurin explained. “It’s made possible by the incredible amount of data that is generated, accumulated, and analyzed every day with the help of ever-increasing computer power and ever-cheaper data storage. It uses the data exhaust that all of us leave behind through our daily lives. Our mobile phones’ GPS systems report back on our location as we drive; credit card purchase records show what we buy and where; Google searches are tracked; smart meters in our homes record our energy usage. All are grist for the Big Data mill.”

Private and Passive versus Public and Purposeful

Gurin explained that Big Data tends to be private and passive, whereas Open Data tends to be public and purposeful.

“Big Data usually comes from sources that passively generate data without purpose, without direction, or without even realizing that they’re creating it. And the companies and organizations that use Big Data usually keep the data private for business or security reasons. This includes the data that large retailers hold on customers’ buying habits, that hospitals hold about their patients, and that banks hold about their credit card holders.”

By contrast, Open Data “is consciously released in a way that anyone can access, analyze, and use as he or she sees fit. Open Data is also often released with a specific purpose in mind—whether the goal is to spur research and development, fuel new businesses, improve public health and safety, or achieve any number of other objectives.”

“While Big Data and Open Data each have important commercial uses, they are very different in philosophy, goals, and practice. For example, large companies may use Big Data to analyze customer databases and target their marketing to individual customers, while they use Open Data for market intelligence and brand building.”

Big and Open Data

Gurin also noted, however, that some of the most powerful results arise when Big Data and Open Data overlap.

“Some government agencies have made very large amounts of data open with major economic benefits. National weather data and GPS data are the most often-cited examples. U.S. census data and data collected by the Securities and Exchange Commission and the Department of Health and Human Services are others. And nongovernmental research has produced large amounts of data, particularly in biomedicine, that is now being shared openly to accelerate the pace of scientific discovery.”

Data Open for Business

Gurin addressed the apparent paradox of Open Data: “If Open Data is free, how can anyone build a business on it? The answer is that Open Data is the starting point, not the endpoint, in deriving value from information.” For example, even though weather and GPS data have been available for decades, those same Open Data starting points continue to spark new ideas, generating new, and profitable, endpoints.

While data privacy still requires sensitive data not be shared without consent and competitive differentiation still requires an organization’s intellectual property not be shared, that still leaves a vast amount of other data which, if made available as Open Data, will make more data open for business.

 

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Category: Information Development
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by: Robert.hillard
25  May  2014

Careers in Technology

Is there a future for careers in Information Technology?  Globally, professional societies such as the British Computer Society and the Australian Computer Society have long argued that practitioners need to be professionals.  However, there is a counter-argument that technology is an enabler for all professions and is more generally a capability of many rather than a profession of the few.

At the same time, many parents, secondary school teachers and even tertiary educators have warned students that a Technology career is highly risky with many traditional roles being moved to lower cost countries such as India, China and The Philippines.  Seeing headlines in the newspapers in recent years headlining controversy over the use imported works in local Technology roles has only served to further unsettle potential graduates.

Technologists as agents of change

Organisations increasingly realise that if they don’t encourage those who have information and insight about the future of technology in their business, they be creating a lumbering hierarchy that is incapable of change.

How should companies seek out those innovations that will enable the future business models that haven’t been invented yet?  Will current technology savings cause “pollution” that will saddle future business initiatives with impossible complexity?  Is the current portfolio of projects simply keeping the lights on or is it preparing for real change?  Does the organisation have a group of professionals driving change in their business in the years to come or do they have a group of technicians who are responding without understanding why?

These questions deeply trouble many businesses and are leading to a greater focus on having a group of dedicated technology professionals at every level of the organisation and often dispersed through the lines of business.

The recognition of the need for these change agents should answer the question on the future of the profession.  At a time when business needs innovation which can only achieved through technology, society is increasingly worried about a future where their every interaction might be tracked.

While the Information Technology profession has long talked about the ethics of information and privacy, it is only recently that society is starting to care.  With the publicity around the activities of government and big business starting to cause wide concern, it is likely that the next decade will see a push towards greater ownership of data by the customer, more sophisticated privacy and what is being dubbed “forget me” legislation where companies need to demonstrate they can completely purge all record of an individual.

While every business will have access to advice at senior levels, it is those who embed Information Technology professionals at every level through their organisation that will have the ability to think ahead to the consequences of each decision.

A professional’s perspective

These decisions often form branches in the road.  While requirements can often be met in different, but apparently similar paths, the difference between the fastest route and the slowest is sometimes measured in orders of magnitude.  Sometimes these decisions turn out to be difference between success and failure.  A seemingly innocuous choice to pick a particular building block, external interface or language can either be lauded or regretted many years later.

Ours is a career that has invited many to join from outside and the possibilities that the digital and information economy create had enticed many who have tinkered to make Information Technology their core focus.  While this is a good thing, it is critical that those relying on technology skills can have confidence in the decisions that are being made both now and in the future.

Practitioners who have developed their knowledge in an ad-hoc way, without the benefit of testing their wider coverage of the discipline, are at risk of making decisions that meet immediate requirements but which cut-off options for the future or leave the organisation open to structural issues which only become apparent in decades to come.  In short, these people are often good builders but poor architects.

But is there a future at all?

Casual observers of the industry can be forgiven for thinking that the constant change in technology means that skills of future practitioners will be so different to those of today as to make any professional training irrelevant.  Anyone who holds this view would be well served by reading relevant Technology articles from previous eras such as the 1980s when there was a popular perception that so-called “fourth generation languages” would mean the end of computer programming.

While the technical languages of choice today are different to those of the 1970s, 80s and subsequent decades, the fundamental skills are the same.  What’s more, anyone who has developed professional (as opposed to purely technical) skills as a developer using any language can rapidly transition to any new language as it becomes popular.  True Technology professionals are savvy to the future using the past as their guide and make good architecture their goal.

The way forward

Certainly the teaching and foundations of Technology need to change.  There has been much too much focus on current technical skills.  The successful Technologist has a feel for the trends based on history and is able to pick-up any specific skill as needed through their career.

Senior executives, regardless of their role, express frustration about the cost and complexity of doing even seemingly simple things such as preparing a marketing campaign, adding a self-service capability or combining two services into one.  No matter which way you look at it, it costs more to add or change even simple things in organisations due to the increasing complexity that a generation of projects have left behind as their legacy (see Value of decommissioning legacy systems).

It should come as no surprise that innovation seems to come from Greenfield start-ups, many of which have been funded by established companies whose own legacy stymies experimentation and agility.

This need to start again is neither productive nor sustainable.  Once a business accepts the assertion that complexity caused by the legacy of previous projects is the enemy of agility, then they need to ask whether their Technology capabilities are adding to the complexity while solving immediate problems or if they are encouraging Technology professionals to create solutions that not only meet a need but also simplify the enterprise in preparation for an unknown tomorrow.

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Category: Information Development, Information Governance
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