Aoife D'Arcy: When in doubt, let data be your guide

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IMI_Talking_Leadership · Mapping the Future of Work with Bruce Daisley
How realistic is a four-day working week in our lifetime? Working from home has recalibrated our expectations, but it has also surfaced new challenges for all of us.   

To go deeper, this week I am joined by Bruce Daisley, a best-selling author and one of the most respected thought leaders on the subject of workplace culture and the future of work. 

Bruce was welcomed recently by the IMI with his Future of Work Series session on Workplace Culture. 

This conversation was recorded on April 26th, 2021.
Subscribe: Spotify, iTunes, TuneIn, Soundcloud, Acast, Stitcher – or search ‘IMI Talking Leadership’ in your podcast provider of choice.
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            [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. _____________________________________
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Aoife D'Arcy

14th Jun 2022

Episode 42 | Mapping the Future of Work with Bruce Daisley

Do you treat your data as an Asset?

Aoife D’Arcy: When in doubt, let data be your guide

The right metrics can be a decisive differentiator for your business, writes Aoife D’Arcy.

We have all heard phrases like “Data is the new oil”, “Use analytics to get ahead of the competition” or, my favourite, “In God we trust; all others bring data”, bandied about in the media, at conferences and elsewhere.

While these make for nice headlines, it can sometimes be difficult to understand how they translate to your organisation or, more importantly, how your organisation can embrace this new way of working. It’s time to demystify some of the rhetoric.

Let’s start with what it really means to have a data-driven organisation. In my experience, there are five common features that organisations who embrace a culture of data-driven innovation possess:

There is a business strategy that incorporates, and is often guided by, data-driven insights
Everyone, from C-level executives to HR, Finance and beyond, is tuned in to the strategy and are data literate
Data-driven insights are knitted into decision-making processes across the organisation
There is a culture of data-driven innovation throughout the organisation
The decision processes within the organisation move from being reactionary to being proactive

To embed a culture of data-driven decision-making, organisations need to go beyond just having good data and the systems to process and analyse that data. It is about really understanding how decisions are made within your organisation and using data to improve these decisions.

Decisions are made by a mixture of people, systems, processes, and data – what I call The Decision Space. In an organisation that embraces a culture of data-driven decision-making, understanding and improving decision processes, and the data that drives them, is critical.

Current business outcomes are examined using data, these outcomes are improved by tweaking the right part of the process, and you’re given the option of making decisions using the combined power of people, experience, and evidence. To get there involves assessing and understanding the mix of data, systems, people, and processes currently used to make decisions, followed by constant examination, questioning and improvement for a better business outcome.

Becoming an organisation that has data-driven decision-making at its core is not easy and takes time, so what are the tangible benefits?

A Culture of Measurement – Once you embrace a data-driven culture, you cannot avoid measuring things. This could be as simple as measuring the number of sales that resulted from a particular campaign and how that compared to the last campaign. Once you start measuring in a consistent way and presenting the resulting insight, you start to understand how particular decisions impact business performance. You can then measure the effectiveness of decisions and evaluate the impact of those decisions on the organisation.
A Culture of Continuous Improvement – Once you start measuring things consistently, the next step is to use these measurements to improve things. For instance, a Customer Retention dashboard that captures key KPIs for the retention process and displays how these values are changing over time is a great basic measurement tool. The next step is to look at ways that you can use this information to increase retention, for example by giving extra incentives for customers to stay. The important thing is that once you have basic measurements in place, you can then start tracking the effectiveness of decisions made to improve the outcome of the retention process. At this point you might also introduce some machine learning or AI into the mix.
A Culture of Innovation – The key to embracing a culture of innovation is to accept that to be innovative, you need to try new things – and sometimes you will fail. But in our new world of constant measurement, the key is that it is measured failure. Data-driven decisions are measured, and their results are fed back so that the process can be improved. While it is acceptable to take a decision that results in a negative outcome, that measured failure should always result in rethinking the decision to ensure an improved outcome the next time. It is this journey of gathering insight and understanding through testing and trying new things that leads to a successful outcome and embodies with it a culture of innovation.
A True Understanding of the Decision Space – Once you start layering up the various elements of becoming a data-driven organisation, the understanding of how decisions are made increases. These elements may include an improved dashboard for a particular area, a predictive model, improved data quality processes, or a new system – and everything in between. As you start adding new layers and elements, your understanding of the decision space increases. So, you are continually improving your understanding of how people, processes, systems, and data combine to improve business performance.

This article was first published in the Irish Examiner on 9th June, 2022. Download the PDF here.

■ Aoife D’Arcy is an IMI Associate faculty member and CEO of Krisolis. An expert in analytics strategy, customer insight, fraud, and risk analytics, she has worked in Ireland and abroad with some of the biggest names in banking, finance, insurance, gaming and manufacturing.