Bringing Data Modelling into the Boardroom

Array ( [0] => WP_Post Object ( [ID] => 11405 [post_author] => 57 [post_date] => 2015-08-21 15:55:57 [post_date_gmt] => 2015-08-21 15:55:57 [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.

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 3^rd 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] => 15829 [post_author] => 15 [post_date] => 2016-09-21 13:10:23 [post_date_gmt] => 2016-09-21 13:10:23 [post_content] =>

When I ask the question how data driven is your organisation, I invariably get back a positive answer because of course everybody uses data in their work. However, if I ask the question do you treat data as an asset, the answers are much more subdued and vague. Observing the difference in the two answers is very interesting (if not concerning) as every organisation appreciates the importance of data and its impact on all aspects of the business. Yet, very few really appreciate data as an asset, even though this appreciation is at the core of being data driven as an organisation. Given this difficulty, I have outlined a few simple questions that will help you examine your (and your organisation’s) conceptualisation of data as an asset, which also point to a number of simple changes you can make.

What financial measures do you use for organisations data? The key characteristic of any asset is that it produces financial value. Applying the same logic to data, it is essential that financial measures are utilised for data. For some, the idea of putting a value or financial measure on something as intangible as data sounds weird, but if you don’t have any financial measures in place, how can you even know if your data has value or not. Simple measures such as the cost of data should be recorded by either (a) calculating the cost of acquiring, integrating, analysing and delivering a particular data set, or (b) calculating the cost of replacing a data set if you suffered a full non-recoverable data loss. Other measures such as: market value, depreciation, appreciation, and revenue generation should also be included.
What type of language do you use to have data conversations? A good indicator of whether an organisation treats its data as an asset is the language they use to describe and discuss data. If an organisation has a mature apprehension of data as an asset they will have a very concise language that is aligned to that of an asset. For instance, many of the terms described above (e.g. appreciate, depreciation, value, cost, investment) would be very closely tied to conversations around data. Other terms and concepts such as: life-cycles, renewal, governance and ownership will also be common within data conversations.
Have you clearly separated IT assets from data assets? The tangibility and commercial nature of IT have made it easy for people to view IT as an asset. However, due to the fact that IT is the container and infrastructure on which data flows, organisations have a lot of difficulty in separating the two types of assets. This leads to a number of issues, including: the mistake of making technology the central focus of projects to the detriment of any data priorities. For instance, emphasis gets placed more on the type of technology to be utilised rather than the data requirements for projects.
How do you maintain the condition of your data? The perquisite of answering this, is first knowing the condition of your data. The majority of organisations will admit that their data is not all of good quality but fail to actually quantity how good or bad it is. Key to understanding good quality data is noting that while data is not depletable (the amount of data does not diminish with use), more data is not necessarily better and it is also perishable if not kept up-to-date and accurate. Without practices in place to ensure your data is of good quality you will never get the most out of data as an asset.

Finally, it is worth remembering that behind every good asset are strong data savvy managers that continually ask the questions above, fully understand the value of data, and instinctively know how to facilitate a data driven organisation.

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. _____________________________________ [post_title] => Do you treat your data as an Asset? [post_excerpt] => [post_status] => publish [comment_status] => open [ping_status] => open [post_password] => [post_name] => treat-data-asset [to_ping] => [pinged] => [post_modified] => 2020-05-11 19:53:24 [post_modified_gmt] => 2020-05-11 19:53:24 [post_content_filtered] => [post_parent] => 0 [guid] => https://www.imi.ie/?p=15829 [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 : Follow @IMI_Ireland 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.

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] => https://www.imi.ie/?p=8958 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) )

Tadhg Nagle & David Sammon

5th May 2017

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

Are you a Data Structure Scientist?

Do you treat your data as an Asset?

'Off the Wall' tips for early business success

Bringing Data Modelling into the Boardroom

It is key to understand the importance of being visual when you are trying to convey to someone an understanding of complex problems.

Throughout the past four years of Data Business, our participants have increasingly understood the importance of being visual when they are trying to convey their understanding of complex problems. While on this journey, participants have also realised that the capability to translate verbal/textual data into a visual artefact is not well developed in most people, most notable themselves (the business executive). This represents a significant challenge because breaking down complex problems most often requires an ability to “see” the problem and seeing the problem requires us to visualise its components so we can share our understanding with others. On the Data Business programme, understanding this translation of verbal/textual data into visual artefacts is extremely important for unlocking business value from business data, and we achieve this through relational data modelling using our canvas

Bringing data modelling into the boardroom (Photo source)

In business today there are many data black boxes. By data black boxes we mean business applications with associated data repositories that no one truly understands. By truly understands we mean no individual or group has a full appreciation of the data models that dictate the structures of the data in these repositories. By data models, we mean the relationships between the business things of interest. By business things of interest, we mean the master data of the business. However, our applied research activities on the Data Business programme inform us that when executed effectively data modelling can impact positively on the business – from the shop floor to the boardroom. Soundbites from business executives on the use of data modelling have been extremely positive, for example:

“I didn’t realise data modelling is as much an art as it is a science.”
“This is a new way for me to look at the business.”
“I think we have overcomplicated the relationships between the business things – simplicity is the key.”
“Doing data modelling is exposing the cost of data silos in my business.”
“I wonder who performs this modelling role in my business today.”

So the fact that a business is supported by many different business applications (aka data black boxes) can suggest that the data picture (aka data model) may not be very well understood by the business, or indeed it may not be as simple as it possibly could be. This data complexity is further compounded by the semantic differences that exist across business units when defining the same business thing of interest (e.g. a customer or a product). Our Data Business experiences tell us that by taking part in data modelling workshops, business executives can experience the value of thinking differently about their data and essentially draw a more appropriate data picture.

However, in the Big Data era, relational data modelling has been somewhat disregarded as to its importance, but this is simply because it has all too often been misunderstood by business executives as to its business value. A point often not appreciated is that relational data modelling does not simply mean relational databases; they are two very separate things! To be clear, relational data modelling is more concerned with the fact that the business understands their critical business data through the lens of a relational data structure (the relationships between the business things of interest). Within Data Business we champion a mindset that promotes the business value of relational data modelling as being a low tech, low cost, collaborative and people-centric activity. The data models that are designed are canonical (independent of any existing systems) and low fidelity. To provide an example, three business executives (completing the MSc Data Business) have calculated the business value of their respective A3 sized, paper-based, business-oriented, relational data models, designed using our canvas. These valuations have come in at €1m, €10m and €16m. So data modelling has an important role to play in unlocking quantifiable business value from business data.

To conclude, our applied research experiences (both inside and outside the Data Business classroom) have provided ample evidence that the design of a data model (data modelling) by business executives is an effective way of visualising complex problems through the translation of verbal/textual data into visual artefacts. Ultimately, a data model designed by business executives targets the complexity of business data and remember a picture is worth a thousand words!

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.