Making Your Organisation Data-Driven [MYOD]

Kshira Saagar

Kshira Saagar (the K is silent as in a Knight) is currently the Group Director of Data Science for Global Fashion Group (parent company of The Iconic) and has spent 19.7 per cent of his lifetime helping key decision makers and CxOs of Fortune 100 companies make smarter decisions using data. He strongly believes that every organisation can become truly data-driven, irrespective of their size or domain or systems.

“Data! Data! Data! You cannot make bricks without clay”, said the most famous detective to grace a paperback, Sherlock Holmes. This was stated in 1892, a mere 128 years ago.  

While Sherlock realised the power of data to make smarter decisions long ago, pretty much every business that needs to make ‘tough’ decisions today as COVID-19 sweeps the globe is finally realising the undeniable and unquestionable power of data.  

Of course, almost all organisations in Australia have wanted to be truly data-driven, and it’s called out in annual shareholder statements through to startup pitch documents. But being data-driven is no longer a fad, just like being a digitally transformed company is no longer an option but a must for all organisations today.  

With this as the background, let us look at what makes an organisation truly data-driven and how every enterprise, regardless of size, can aim to become one big data team.  

Framework for Success

As someone who has built nearly half a dozen successful data teams from scratch, here is the winning framework that has been always used, with minor contextual tweaks, which definitely bears the same results: Mindset, Skillset and Dataset (MSD).  

This framework serves a couple of functions. Firstly, it structures the thought process into smaller addressable sub-components. It also provides the right questions one needs to ask of their data team set-ups. Be it a fully-fledged, state-of-the-art data setup or a fledgling new data team, this framework applies to all equally.  


Dataset is the easiest and most practical part of the framework aimed at addressing the rancorous disputes and analysis paralysis caused by lack of ‘reconcilable’ data. Have you ever been in an executive meeting or a key strategy discussion and wondered, what is actually our actual sales, or new customer counts or number of unique products sold?  

The issue is not the lack of data to answer these questions, but the availability of too many different sources of data to answer such a question. The transaction system has one sales figure, the accounting systems another and your online analytics platforms have a third interesting number. Which one is correct?  

This question of which data to ‘trust’ is the biggest source of friction in using data and insights, and often the biggest detractor of data team credibility. A good way to solve this is to build a Data Availability Map(s). The DAM is an awareness and education document, on a simple Excel or Google Sheet, that serves to shine a light on the following aspects:  

  • What all systems track and store data that is being used for decision-making
  • In these systems, what all kinds of information and metrics are stored
  • Across all these metrics, how the most ‘reliable’ ones are identified
  • How to create a new ‘taxonomy’ for these metrics and assigning the most reliable
  • Identify gaps/opportunities for which metrics or information is still missing.

With a DAM like this, building feature or metric libraries on top becomes a much easier job. An example is a customer metric library that has all possible information about a customer across every single system - mapped, validated and stored in one place. The next time someone has any kind of question about a customer or a customer cohort, this metric library will speed up information availability and also provide credible results.  

Think of this exercise as building a mini-Google search engine for all your data.  


Once the fundamental issue of disparate data sources is resolved, the next big challenge that acts as a barrier is the skillset to use this data.  

Data science and analytics is already considered tough and mystical. Add to this the fact someone has to know two programming languages, three new data tools and four data-karate moves, and you make it a no-go area for most of the business. It is also worth remembering knowledge has been power in all of human history, and some data setups encourage this ‘safeguarding’ of knowledge consciously or subconsciously.  

The best way to break this deadlock is by providing tools that can help self-serve decision making for everyone who makes a decision in the business. Based on the size of the business and the deep pockets available, a singular good and effective commercial data tool can be purchased for the whole organisation. The main focus should be on providing the whole organisation with just one common tool, so that everyone can talk the same language and point to the same numbers, without the different tools becoming another excuse or challenge to overcome.  

But at scale and with mounting costs, this becomes a challenge. Here, open source data tools can come to the rescue. There are more than a few dozen extremely good open source visualisation platforms like Superset, Redash, RShiny, enabling every single employee in the organisation access to real-time insights all the time for no cost.  

The choice of open source versus commercial tools, depending on the organisational needs, is a debate for a different time. Nonetheless, it is important every single decision maker at your organisation has access to this common data tool. Think of it as the PPE we need to provide every single frontline health worker. Providing one tool to rule over all the various metric libraries and data sources is akin to having one easy-to-use ring to find, bring, bind and rule them all.  

3. Mindset  

The last and toughest part of the framework is changing the mindset that data science is voodoo or difficult dark magic.  

All of us make decisions at every moment of our lives - be it whom we choose to marry, what course to study, which job to pursue or the mundane ones of what to eat for lunch or wear to the office. Each of these actions is a mini-algorithm in our brains making random forest style decisions. If we can simulate so many algorithms subconsciously in our mind, why not extend that to work data?  

To pivot to a healthy mindset towards data and have a more animated curiosity about what is possible, organisations need data Tools, Education and Access (TEA). The right tools will become available after solving the skillset challenge above, which leaves us with education and access.  

A big part of data education starts with simple awareness of what data exists, what questions can be answered and how to search for the right information. Sounds familiar? The DAM (Data Availability Map) from before does it all.  

Next, as part of the education pillar, having a structured uni-style syllabus to upskill your existing data team members and also providing an intensive/extensive SQL, data basics, data inference and dashboarding courses (both live and recorded) for wider organisational members will massively help with bringing about a healthy curiosity with data in your organisation.  

The final challenge of the right access to the right data can be underpinned by a strong and robust data governance program, which can serve to ensure that every member has the most appropriate access to the most suitable metrics as and when they join. Detailed examples of access controls and education are a topic for a future date.  


Using the Mindset, Skillset, Dataset framework, starting with the focus on dataset by asking all the tough questions above, will help all organisations get on a self-actualising data-driven journey.  

The aim of this series is cover specific examples within each aspect of the framework with a practical how-to guide. What challenges do you face in these three areas and how do you overcome them? Please leave your questions and feedback below, or email us here.

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Tags: data analytics, data-driven marketing

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