[0] => WP_Post Object
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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] => [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] => [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [2] => WP_Post Object ( [ID] => 8958 [post_author] => 18 [post_date] => 2015-02-10 17:34:20 [post_date_gmt] => 2015-02-10 17:34:20 [post_content] => To receive updates on new blogs posts : My experience working with a wide range of young businesses, from complex financial software through to artisan food producers says, it is easy to get distracted by products and forget that the underlying success drivers are the same regardless of what you make or do. girl at wall A visit to The Climbing Wall in Sandyford, a 3 week old fledgling business already packed with happy customers on a freezing January night made me stop to think.  What gets customers in this case to a business with no marketing or advertising budget?  What separates success and disaster for a young business in the early scary days? “The wall” is an indoor state of the art climbing wall in Sandyford industrial estate. So, your business is very different, but the same answers apply and will help you succeed early.
  • Passionate attention to  all customers, including the ones future customers. I dragged along a friend who doesn’t climb, and had no intention of doing so.  She instantly felt welcome, even though climbing up the wall until then was something she only does at business meetings. Your customers may come in many forms and will have different needs. See the world from their perspective – are they confused? Scared? Stressed? Finding it hard to park? At the Wall you feel safe and at ease. And yes, of course, she climbed. And is now hooked.
  • Create a happy place where staff are as engaged as you are in looking after customers with care. Your staff must feel like a really core part of your baby business.  Get them on board and make sure to find ways of harnessing all their bright ideas about how to make your project a success
  • Know your customers intimately before you start. Alan and Brian really understand their market, and are well networked. They already understood exactly what climbers want and immediately ran simple high impact events that have built up loyalty, traffic to The Wall and loads of Word of Mouth publicity, always the most powerful form of marketing. This also helps you create a sense of community and shared values among your customer base, so your customers stay longer and believe in what you do.  Happy customers come back.
  • Be clever about how to position and communicate what you offer: .The Wall makes canny use of social media and press coverage to get the story out in a more targeted and dynamic way than any ad ever will.  Network, but be savvy about how you use that precious network.
  • Know your competition equally intimately, know when to compete (and how) and when to collaborate. Sometimes collaboration is the right strategy – work together and instead of splitting a new small market you can grow it together, creating greater awareness by acting as a group and attracting more people to a new service or product.
  • Good team - make sure all the practical stuff is under control.  The top team here includes a marketing whizz and an employment law specialist.  They have team skills to make sure the business is set up on a sound financial footing, property and planning skills and expertise to make sure design and operations are top class.
  • Finally – do something you love. The chances are you will be very good at it!
  Moira Creedon is a facilitator and consultant in Strategic Finance and has worked with both corporate and public sector clients worldwide helping decision makers at strategic level to understand finance and improve their ability to formulate and implement strategy. She teaches on IMI’s Diploma in Management and a number of Short Programmes including the Senior Executive Programme. See our Spring 2015 schedules here: IMI Diplomas Spring 2015 and IMI Short Programmes Spring 2015 [post_title] => 'Off the Wall' tips for early business success [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => wall-tips-early-business-success [to_ping] => [pinged] => [post_modified] => 2020-05-11 20:58:52 [post_modified_gmt] => 2020-05-11 20:58:52 [post_content_filtered] => [post_parent] => 0 [guid] => [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) )
Tadhg Nagle & David Sammon

Tadhg Nagle & David Sammon

2nd Aug 2017

Tadhg Nagle & David Sammon are joint Programme Director of the IMI MSc in Data Business.

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Are you a Data Structure Scientist?
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'Off the Wall' tips for early business success

Is your Data an Asset or Liability?

A recent study of 75 datasets across 30 Irish organisations (of varying types, sizes and sectors) reveals that the average data quality score is 53%. So half of our critical business data is either incomplete and/or inaccurate (aka “bad data”). That means, half of the data that we use to answer tactical, operational and strategic questions is defunct. In essence, almost half of our business data is a liability, not an asset. Anyone that has been “burned by a decision” they made on data they ‘assumed’ was correct, will know how much of a liability that data can be.

Data asset audit (Photo source)

Think of a business question that you frequently need answered, for example: What is my total sales this month? Who is my most profitable customer? Which product is delivering the highest margin? How many customers do I currently have? OK, so you get the idea. Now once you have your business question, you appreciate that having the data in your business systems is also necessary in order to even attempt to answer your business question. So assuming you have the data you may also ask yourself “am I confident that this data is up to the job”? Enter the “Data Asset Audit”, a simple tool that will build confidence in your business data and essentially give you an indication of whether your data is an asset or a liability.

The “Data Asset Audit” is a simple tool designed by Prof. David Sammon and Dr. Tadhg Nagle to assist data savvy managers start new transformative data conversations within their organisations. The tool, organised as a decision tree, comprises three simple checks. Assessing the answer to your business question against these three checks allows you and your colleagues (when used in a collaborative setting) to appreciate if your data is an asset or a liability and position your organisation at some point on the scale from 8 to 1. As a result, you may become more confident about your data quality (e.g. completeness and accuracy), its presentation for use (e.g. immediate visual impact), and its actual value when delivered (e.g. within seconds, minutes, hours, days, weeks, etc. of asking the business question). However, as is often commented: if you answer ‘NO’ to the first check on data quality, the other two checks are somewhat irrelevant thereafter. Regardless of whether this is the case or not, answering ‘NO’ to data quality does leave the organisation with a serious business problem.

As we know, in analytics, every analyst has to address data quality at some point or another. Not surprisingly then the most frequently returned score on the “Data Asset Audit” is 4. So we can see that a score of 4 translates into the first check on data quality being answered as ‘NO’. However, another frequent response from the “Data Asset Audit” tool in use is “it depends on the dataset(s) needed to answer the business question”. As a result, a range of scores (e.g. 2 and 7) is sometimes returned. In fact, while a score of 8 is not offered up too often by those using the “Data Asset Audit” tool, it matters little, as the power of the tool is in the conversation it creates.

The “Data Asset Audit” tool usually appeals to users because of its simplicity and immediacy. The fact that it focuses the mind on a business question to be answered is also appreciated. Of course good conversation and a “what happens next?” attitude is the ideal outcome from the use of the “Data Asset Audit” tool in order to start the journey toward becoming a data-driven organisation. The “Data Asset Audit” tool is one of many discursive templates used on the Data Business programme in order to change the data conversation. The “Data Asset Audit” tool is not a silver bullet but it can get you started on your data-driven journey in a simple, yet effective, way! It’s easier than you think, just start with appreciating the concept of the data asset!

Tadhg Nagle & David Sammon are joint Programme Director of the IMI MSc in Data Business.

Tadgh is 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.

David is a Professor (Information Systems) at Cork University Business School (CUBS), University College Cork (UCC). David’s research interests focus on the areas of conceptual/logical data modelling, agility, master data design, theory and theory-building, and redesigning organisational routines through mindfulness.