Learming Hub
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            [post_content] => OK so what’s a data structure scientist I hear you ask! Yes, we have all heard of that “sexy” data scientist role but perhaps never of the data structure scientist. You might imagine it to be a role demanding significant technical know-how; well nothing could be further from the truth in fact. Let me share.

A data structure scientist is a business person who unlocks business value from having conversations with their business data in order to draw pictures about that data.

No that’s not a typo, I do in fact say ‘having conversations with their data’! So being a data structure scientist is as much about being an artist as it is a scientist.   1  Some things are often best explained with an example, so let me ‘show’ you what I mean. Imagine you are the HR manager of a business and you are interested in your employee roles. To get this picture of employees and their roles you query the data on the HR system. Now you know you currently have 100 employees but the result of your query returns 135 employees. Something is wrong! It’s obvious you asked the wrong how many question of the data! So a quick call to your techie colleague and you ask them to check this out for you and to your surprise you get confirmation that “135 employees” is in fact the correct number. BUT your IT colleague clarifies, with perhaps a sense of “I thought you would already know this”, while the business has only 100 employees, the HR system has 135 employee data records because some employees perform multiple roles in the business! So you ask yourself, why is the HR data telling us we have 135 employees and not 100? Enter your need to be a data structure scientist. In short, if some of the 100 employees perform multiple roles (as confirmed by your IT colleague) and you have 135 data records, it is most likely that you have redundancy in your data caused by an inappropriate data structure. Figure 1 below is the most likely structure that exists. The assumption built into this data structure is that an employee performs only 1 role. However, to work-around this inappropriate assumption, because the business needs employees to perform multiple roles, another instance of an employee data record will be created, for an already existing employee, to accommodate an additional role. As it happens if this employee also performs a further role, then a 3rd instance of an employee data record will be created.  


Figure 1

  So how do you overcome this data structure problem and remove the undesirable data redundancy? The answer is captured in Figure 2 where you introduce an associative entity (Employee Role). You need to introduce a more dynamic data structure that is appropriate for the business model and the logic that an employee can perform 1 or many roles. This associative entity now provides the appropriate structure where an employee is matched with their role or roles over time.  


Figure 2

  The business value of this data structure (Figure 2) is very simple. When you now ask the how many employees question, you will get 100 employees, but 135 Employee Role data records will also exist to cater for an employee performing 1 or many roles. So the next time you spot an anomaly in your business data (for example the wrong answer to a how many question), have a conversation with your data in order to draw a picture about the data structure. This behaviour will present a challenge to the IT side of the house and start you on a journey of treating your business data as an asset!   Dr.David Sammon and Dr. Tadhg Nagle are programme directors on the IMI/UCC MSc in Data Business. Dave is a Senior Lecturer in Business Information Systems at University College Cork and Tadhg is a Lecturer in Business Information Systems at University College Cork.   [post_title] => Are you a Data Structure Scientist? [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => data-structure-scientist [to_ping] => [pinged] => [post_modified] => 2020-05-11 20:47:19 [post_modified_gmt] => 2020-05-11 20:47:19 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.imi.ie/?p=11405 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [1] => WP_Post Object ( [ID] => 18911 [post_author] => 7 [post_date] => 2017-05-05 11:19:48 [post_date_gmt] => 2017-05-05 11:19:48 [post_content] => [post_title] => Bringing Data Modelling into the Boardroom [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => bringing-data-modelling-boardroom [to_ping] => [pinged] => [post_modified] => 2020-05-16 09:43:40 [post_modified_gmt] => 2020-05-16 09:43:40 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.imi.ie/?p=18911 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 12166 [post_author] => 68 [post_date] => 2015-10-07 11:00:35 [post_date_gmt] => 2015-10-07 11:00:35 [post_content] =>
Yves Morieux is a Senior Partner and Managing Director at The Boston Consulting Group, a BCG fellow and director of the BCG Institute for Organisation.Yves' Six Simple Rules of Smart Simplicity, has helped CEOs with their most critical challenges, for instance, moving their companies from quasi bankruptcy to industry leadership. He will be a keynote speaker at the IMI National Management Conference on 8 October 2015

1. What is the chief thing that managers/leaders get wrong about what effective leadership means today, in your experience?

Managers often don't understand what their teams really do. They understand the structures, the processes, the systems. But this is not what people do – it is what people are supposed to do.  A company's performance or a department's performance is what it is because people do what they do, because of their actions, decisions and interactions – their "behaviours".  Because we don't understand what people do, we create solutions – new structures, processes, systems, scorecards, incentives, training, and communication – that don't address the root causes. We don't solve the problem, we simply add more internal complicatedness. And the more complicatedness we create, the less we understand what is really happening, the thicker the smoke screen, and then the more rules we add. This is the vicious circle of modern management. This is why the first rule of what I call Smart Simplicity is "understand what people really do at work."

2. Do leadership principles work best when understood as a top-down process, or is this understanding of leadership out of touch with the modern workplace?

From collaboration to performance to employee engagement, everything we know about work is changing – but our businesses are seemingly slow to respond. People are more attuned to sharing posts, writing blogs, and providing instant feedback through ‘likes’ and ‘favourites’ than they are to completing surveys, so why does our approach to employee engagement still centre on a set of fixed statements and a rating scale? In their personal lives people collaborate naturally with those around them and have an amazing propensity to share even when there is no immediate benefit to them, hence the success of crowdsourcing sites like Wikipedia. So, why do we spend so much time and energy in organisations on encouraging people to practice these seemingly natural behaviours at work? The challenge for businesses is to disrupt every process and practice in the organisation by asking: Why does it exist? What are we trying to achieve? If we were to start the organisation from scratch, would we choose to create this? And perhaps most tellingly of all, would this practice exist if we trusted our employees? iqmatrix

3. A core feature of your approach to leadership and better workplace productivity is the concept of ‘Smart Simplicity’. How does this play out in a world where the data available to companies now – be it through consumer feedback, predictive modelling, data analytics etc – has surged? Does the effective use of all of this data necessitate more complexity, rather than simplicity?

The environment is more complex – the problems to resolve in order to attract and retain customers, in order to create value and build competitive advantage – are more demanding than in the past. This is a fact of life. Based on our analysis, complexity has been multiplied by 6 over the last 60 years. The real problem is not business complexity. The real problem is internal complicatedness – the solutions companies typically use to try to respond to this complexity: a proliferation of cumbersome structures, interfaces, coordination bodies and committees, procedures, rules, metrics, key performance indicators and scorecards. Based on our analysis this complicatedness has been multiplied by 35! This complicatedness creates obstacles to productivity and innovation. People spend their time writing reports, in meetings. There is more and more work on work, and less and less work! A lot of data, a lot of information is always good. The difficulty – and the value-added – is sense-making, to derive meaning and knowledge from the data, so that companies can interpret and act on the data. But complicatedness makes it increasingly difficult for companies to make sense of the data. There is at the same time a data indigestion and a knowledge deprivation.

4. When it comes to Irish businesses, how do their workplace dynamics compare with other countries and what would be your principal advice to them on what to change?

Irish businesses face the same problems as other mature economies. They need to manage the new business complexity without getting complicated. Smart Simplicity is not about becoming simplistic, we cannot ignore the new complexity of business. This is why I refer to "Smart" simplicity. The six rules of Smart Simplicity concern Irish businesses because Irish businesses are also confronted to a greater complexity.

5. Should business leaders focus more on improving employee productivity per se, or should this be balanced with also ensuring that staff are happy at what they do and not afraid to be creative? How does one strike an effective balance?

We must not strike a balance here! We must break the compromise between productivity and happiness or creativity. We must not improve one at the expense of the other. In fact organizational complicatedness hinders productivity while demotivating people and making them suffer at work. They lose direction, purpose and meaning in the labyrinth. They have to work longer and longer, harder and harder, but on less and less value-adding activities. This is why Smart Simplicity and removing complicatedness simultaneously increases performance and satisfaction at work: because you remove the root-cause common obstacles that hinder both.

6. What do you think are the key organisational challenges that face a country like Ireland over the next few years, for both business managers/leaders and their staff?

Organizations are going through a deep revolution in their ways of working. We are going through a new economic revolution, and every economic revolution entails and organizational revolution. The organizational solutions on which we have built profitable growth over the last 30 years are obsolete.  Irish managers and employees will have to invent new ways of working. Smart Simplicity provides guidelines for this, but what mainly matters is boldness and courage in breaking with conventional wisdom. Irish people are certainly well placed in this respect! NMC 2015 A4 HEADER Yves Morieux is a keynote speaker at the IMI National Management Conference taking place on Thursday 8 October. Apologies but this event has now reached maximum capacity.  [post_title] => "Understand what people do at work" Six Word Wisdom from Yves Morieux [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => understand-people-work-six-word-wisdom-yves-morieux [to_ping] => [pinged] => [post_modified] => 2020-05-11 20:38:20 [post_modified_gmt] => 2020-05-11 20:38:20 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.imi.ie/?p=12166 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [3] => WP_Post Object ( [ID] => 14214 [post_author] => 55 [post_date] => 2016-04-07 10:45:50 [post_date_gmt] => 2016-04-07 10:45:50 [post_content] =>

Blockchain is rapidly emerging as the next multi-billion euro digital technology market.

Digitally Generated Image of Online Security Concept

Source: www.forbes.com

It is estimated that spending by organisations on Blockchain projects will exceed $1bn in 2017, which will make it one of the fastest developing digital technologies of all time.

It is receiving much attention due to its potential to disrupt and transform industry sectors. Blockchain has implications for multiple industry sectors including technology, financial services and healthcare. In financial services, institutions such as the Bank of England, Citibank, State Street and the NASDAQ are exploring its business potential. In healthcare, Blockchain technology has the potential to address issues regarding access, security, scalability and privacy of electronic medical records as well as enabling extensive healthcare research. Over the last few months, there have been a number of notable Blockchain “Proof of Concept” initiatives. For example, on New Year’s Eve, NASDAQ enabled the first-ever private securities issuance on their new Blockchain technology platform, Nasdaq Linq. It is purported that Blockchain holds the potential for 99% reduced settlement time and risk exposure in capital markets. Yet, among the broader business community, there remains a lack of understanding of the fundamental concepts of Blockchain, an issue which needs to be addressed if organisations are to benefit from its disruptive capabilities and develop transformative use cases.


Blockchain is a distributed ledger - a continuously growing list of records that are hardened against tampering and revision. Fundamentally, Blockchain can be seen as a peer-to-peer infrastructure where nodes in the network coordinate to play a vital role in processing transactions. Bitcoin is the most widely known application which operates on Blockchain, with the technology being used as the public ledger of transactions for cryptocurrencies. However, it is in domains beyond cryptocurrencies that most disruptive and transformative use cases are emerging.


A Blockchain implementation consists of two parts: 1) Transactions 2) Blocks 1. Transactions: the actual data to be stored in the Blockchain. Participants create a transaction using the system (When someone initiates sending of cryptocurrency to another person for example). 2. Blocks: Blocks are records that confirm when and in what sequence certain transactions became journaled as a part of the Blockchain ledger. These blocks are created by parties known as "miners" who use software designed specifically to create blocks. There are two key players in the network that play a role in executing Blockchain events: (a) Miners: Masternodes that have the ability to create/process transactions (b) Name nodes: Nodes that have the ability to store data within a chain Miners create blocks that confirm and incorporate those transactions into the Blockchain. bbc

Source: www.bbc.co.uk

Blockchain: Characteristics and Advantages

1. Decentralisation: Transparency and Resilience Every node has a complete or partial copy of the Blockchain. This avoids the need to have a centralized database. This decentralised approach removes single point failure for transactions, potentially facilitating greater resilience and transparency. 2. Double spend solution: Trusted Third party not required Cryptocurrencies, for example, use various time stamping schemes such as proof-of-work, to avoid the need for a trusted third party to timestamp transactions added to the Blockchain. This avoids anyone easily double-spending the currency and the need for a third party intermediary to validate business transactions. This can serve to reduce transaction costs, realise significant cost savings while enhancing transparency. 3. Other advantages of Blockchain include:
  • The ability for a large number of nodes to converge on a single consensus of the most up-to-date version of a record.
  • The ability for any node that creates a transaction to, after a certain period of time, determine with a reasonable level of certainty whether the transaction is valid and became final (i.e. that there were no conflicting transactions confirmed elsewhere in the Blockchain that would make the transaction invalid, such as the same currency units: "double-spend").
  • An automated form of resolution that ensures that conflicting transactions (such as two or more attempts to spend the same balance in different places) never become part of the confirmed record set.

Business Innovation: The potential to disrupt and transform industries.

There are still many issues to be overcome before Blockchain is widely adopted. Issues pertaining to network design (permissioned vs permissionless), scalability and business models need to be addressed. There is no “one size fits all” solution.

What does this all mean for business? Opportunity!

In addition to the areas widely being discussed in relation to Blockchain, including payments (cryptocurrency), fraud (Everledger) and trading (NASDAQ), some other domains where Blockchain technology can be applied in driving business innovation include: 1. Auditing: With a single set of transparent records, Blockchain has the ability to fundamentally change auditing processes worldwide. 2. Insurance: A single set of transparent records, for example relating to building certification, fire safety, engineers reports etc. could potentially transform the insurance industry. 3. Business Records: A single set of searchable records pertaining to company directorships, asset ownership, property transactions and judgements has the potential to completely transform practices within the banking and legal professions. 4. Healthcare: Smart health systems, with functionality to include admittance and validation of patient’s identity. Other potential use cases could include a universal ledger for medical research.

Therefore, Blockchain presents numerous business opportunities for organisation’s to innovate, disrupt and transform industry sectors. However, they need to act now towards ensuring that they are leading the digital transformation within their sectors.

Philip O Reilly
Dr. Philip O’Reilly is a Senior Lecturer at University College Cork and is the Programme Director for the MBS in Digital Business. Philip has delivered keynotes and workshops to numerous multinational companies and at leading practitioner events including the Banking & Payments Federation of Ireland National Conference. He recently received the Stafford Beer Medal in recognition of the most outstanding contribution to the philosophy, theory and practice of Information Systems (IS) from the Operational Research (OR) Society at an Awards Ceremony. _____________________________________ [post_title] => Blockchain: Digitally Disrupting and Transforming Business Ecosystems [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => blockchain-digitally-disrupting-transforming-business-ecosystems [to_ping] => [pinged] => [post_modified] => 2020-05-11 20:12:21 [post_modified_gmt] => 2020-05-11 20:12:21 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.imi.ie/?p=14214 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) )
Tadhg Nagle

Tadhg Nagle

19th Apr 2018

Tadhg Nagle is joint Programme Director of the IMI Diploma in Data Business and IMI MSc in Data Business.

Related Articles

Are you a Data Structure Scientist?
Bringing Data Modelling into the Boardroom
"Understand what people do at work" Six Word Wisdom from Yves Morieux
Blockchain: Digitally Disrupting and Transforming Business Ecosystems

Future Jobs: A Map of Data Related Roles

New Data Job Titles

With data top of the agenda for pretty much every organisation, there has been flurry of new data job titles. Some of these titles relate to new roles, while others are a rebranding of existing ones. With the aim of bringing some clarity to the subject I have visualised all data related roles based on core competencies using the Data Value Map.

In a sense, all data related roles should touch on all aspects of the Data Value Map, indeed, a visual could be mapped for each one individually. However, for simplicity the visualisation has focused on the core competencies of each role.

So, if you are planning a data related career or looking to do a data course that will align with that career, the map should help you navigate the jargon and focus on what’s important. If nothing else, it will give you an insight into what all the data people in your organisation actually do.

Big data is creating new job roles around the world – what are they likely to be?
(Photo source)

.The Human Architecture – Roles to Use Data Effectively 

Chief Data Officer (CDO) – this is a new role and reflects the strategic importance organisations are placing on data. The CDO is responsible for all data activities and sits above managers of specialist data departments (e.g. Head of Data Analytics). To do this a CDO needs a significant understanding of all data aspects to ultimately develop and leverage it as a strategic asset. A CDO must be expert in building a data vision or strategic business cases as well as understanding how to lead the organisation in successfully executing this vision through the promotion of appropriate data behaviours.

Data Architect – a senior data position in the landscape of data roles, a data architect’s primary objective is to ensure effective integration of an organisations data to support operational and strategic goals. Focusing on data integration and by extension data acquisition from organisational information systems, key capabilities would involve data modelling and an understanding of the data structures within those systems.

Data Scientist – this is the current title to what was previously labelled as a data miner, data analyst, data analyst or business intelligence analyst. While, different types of data and the introduction of different technologies has played a part in defining the various titles (just put ‘big’ in front of your title), the role is essentially the same across all of them. This primarily includes applying analytics on datasets to deliver historic, predictive or prescriptive insights into business operations. However, this is based on the assumption of access to good quality integrated data. If this is not the case, the role will involve a significant amount of data cleaning.

Database Administrator – one the of the most common data roles in organisations, it involves the management of database software and the tuning of databases for performance. This involves the modelling of data but in a form that aligns with the technology upon which it is implemented. Other tasks include, making sure the database software is up-to-date and that all security/backup/access functions are correctly applied.

Data entry – these are very often entry roles or positions that handle the input and collection of data. This can be in the form of manual entry and require a certain amount of aggregation through spreadsheets. In addition, these roles are often the stepping-stone to higher-level roles in the business or data domain.

Data Infrastructure Engineer – this role is more positioned in the technology domain, to which there is a whole other ecosystem to be mapped. However, technology does play a significant (and complicated) part in supporting an organisations data capability. A data infrastructure engineer looks after the technical infrastructure that supports the four stages (acquisition to delivery) and keeps up-to-date with the latest technological innovations.

Data Governor – one of the least known roles, a data governor promotes the development of routines that ensure data reliability and compliance with regulation. The role is coming more into focus as organisations grapple with new regulations such as GDPR and PSD2.

Mathematician – more specialised than a data scientist, a mathematician (or statistician) focuses on the development of analytical models and mathematical algorithms. Drawing heavily on statistical theories the models/algorithms are utilised in a wide range of areas that span from the pricing of insurance premiums to the powering of artificial intelligence applications.

Data Savvy Manager – while all types of managers recognise the importance of data, they still struggle with leveraging it. A data savvy manager has a good understanding of how data impacts on their business and are able to define and guide data initiatives across all four stages (acquisition to delivery) to improve business performance.

Data Visualiser – this role involves presenting the data in a form that will best fulfil the needs of the user (e.g. explaining or exploring). Another title for such a role is data storyteller and includes the ability to effectively communicate the key messages in analysed datasets.


Tadhg Nagle is joint Programme Director of the IMI Diploma in Data Business and IMI MSc in Data Business.

He is also a lecturer and researcher in Information Systems at University College Cork.

With a background in financial services his expertise is in strategic innovation and emerging and disruptive technologies.