The idea that we’re living in the age of big data is not new, but how big is big anyway? Every day approximately 6 billion Google searches are sent and 65 billion Whatsapp messages are exchanged around the globe. In 2020 alone, 60 zettabytes – or 60 billion terabytes (!) – of data was created. Though that volume is about to get dwarfed by the 175 ZB data tsunami predicted to hit us by 2025. If you were to load that data across 5GB DVDs and string them together, you would be able to cover the entire equator!
What is Data Thinking? A modern approach to designing a data strategy
First let’s start with the question: Why do you need to consider a strategy for your data? An abundance of data and computing sources has unlocked tremendous potential.
Due to limited resources, businesses have to choose which opportunities to pursue. So, what to do with an abundance of data and computing power for your business? The natural human response would be to behave like a kid in the candy shop (‘I want it all!’), until the realisation kick-ins that bellies are only as big as they are (did someone mention diabetes..?). Today, companies face the same challenges with data. Which data do we capture, process, and use. And how? And most importantly, why? Inevitably, companies have to make decisions regarding data due to financial and resource constraints. No doubt making these decisions could cause some sleepless nights, Blockbusters, Kodak, Xerox surely had a few.
Today data is an enabler for every business strategy yet few manage to understand what that means. Despite the rapid explosion in data roles such as chief data officers, data engineers, data scientists and data stewards to name a few, most companies fail to fully grasp the value and opportunities from their data. Because along with the access to zettabytes of data, comes a mountain of opportunities to use that data. It makes the decision about which ones to prioritise and how to pursue them a challenging task. Typical areas for opportunity are enhanced customer experience, personalised marketing, process optimisation, R&D, and evidence-based decision making. But how do these areas relate to each other, what data to use and where to start? And despite nearly every company having identified ‘data’ as an enabler for pretty much their entire corporate strategy, few have managed to translate this into a data strategy to help see the trees through the forest.
How do you develop a data strategy that supports your business effectively?
A data strategy should be user-centric, flexible yet directive, and well-aligned with the business.
In order to maximise the value of their data, companies are scrambling to formulate and implement strategies to jump on the opportunities of today and position for the twists and turns of tomorrow. But not all data strategies are created equal, its success (or failure) can be determined by the method used to get there. So what do well-defined data strategies generally have in common?
First and foremost, they are strongly aligned and well-integrated with the company-wide strategy, focusing on realising strategic business objectives. It is the only way data will be utilised as an enabler for the entire organisation. And it certainly helps when data teams understand how their daily activities help in achieving a firm’s goals. This is obviously easier said than done, but taking a user-centric approach to designing a data strategy will strongly contribute to this objective.
In addition, a data strategy seeks to strike the balance between flexibility and guided direction. Flexibility is required to allow for responding rapidly to dynamic market conditions, while forward thinking and planning is required to realise long-term goals. Prior to the age of agile companies would typically over engineer strategies with a rigid approach. Nowadays, some fail to set a general direction at all, leading to opportunistic behaviour and misalignment across an organisation. Seen the increasing number of job descriptions that come with the capability requirement to ‘manage ambiguity’ at the very top? Striking the right balance between flexibility and direction along the data strategy journey requires finesse.
Data Thinking makes your data strategy user-centric by using Design Thinking
To design this type of data strategy – business aligned, user-centric, guiding and flexible – companies need to apply appropriate methods and tools. Luckily, there is an answer readily available! Because alongside the adoption of agile methodologies, Design Thinking has gained enormous momentum over the past decade. This concept was originally born out of psychological studies on creativity, before an accelerated adoption into the corporate world spearheaded by design firm IDEO. It aims to practice human-centered design through the use of empathy, optimism, iteration, creative confidence, experimentation, and embracing ambiguity and failure.
So what exactly is Data Thinking and why can a gastronomic analogy be used to explain it?
By combining concepts of Design Thinking with typical strategic tools to structured problem solving, a modern approach to designing strategy has emerged: Data Thinking. It takes a user-centric approach for setting priorities, focusses on strong alignment across the organisation, and allows for an adaptive yet guiding strategic roadmap. In the spirit of the ‘Master Chef’ analogy from above, this modern approach can be explained as follows:
In deciding what kitchen (data ecosystem) a restaurant (the company) should build, it should firstly answer the question what kind of meals it wants to cook (data use cases). Finding the answer to what meals to prepare, it should ask its customers (users) what they would like to eat. Or even better, it should try to find out the reasons WHY customers like certain meals, so they could come up with even more delicious and nutritious meals! Perhaps they could also ask the waiters (marketing & sales) how best to serve up a dish? Once they know what customers like best, the restaurant can ask their kitchen staff (data teams) which combination of meals they would be able to prepare. In a combined effort (management, kitchen staff and waiters) they might even be able to craft a menu that would appeal to customers, be best placed for the kitchen to prepare, waiters know how to recommend and management knows how to operate profitably. They decided to pick an Italian theme so unfortunately, the curries didn’t make it to the cut, but the antipasti, pizzas and pastas did! Only once the kitchen staff knows what to prepare, they can start building the kitchen! They’ll need multiple firewood ovens next to the regular bench space and stove top burners. Better order some large plates and pizza knives too! Starting to see how different the kitchen of an Indian restaurant would have looked like..?
Designing a kitchen that is fit for purpose
The analogy might make this look easy and straightforward. Yet many companies still fail to achieve this integrated approach resulting in misaligned and misdirected data strategies. Ensuring a data strategy that is fit for purpose and effectively drives business outcomes is by no means an easy job.
Besides strategic alignment, this approach enables rapid development and testing of the data use cases, speeding up time to value and providing specific users with the relevant insights to make decisions.. Being able to showcase results fast will drive adoption and buy-in from the rest of the organisation. Also, bear in mind that the restaurant remains open and there will be staff initially being sceptical on the decision to pick the Italian menu. Like they say the proof is in the pizza …pudding.
How to ensure taking small steps leads to a desired long-term outcome
By applying Data Thinking, companies can quickly identify use cases for their data strategy. Increasingly, use cases in data science, machine learning and artificial intelligence are tantalising go-to’s for most companies. Though, some of these use cases are beyond the immediate horizon as underlying problems with data access and quality typically hamstring trust and adoption across the organisation. In addition, companies struggle to develop and bring complex data science and machine learning solutions effectively into production. By matching business opportunities with technical and operational capabilities, Data Thinking helps identify a logical sequence of use cases and step-by-step upgrade of company-wide foundational data capabilities in parallel. This ensures that each use case brings a company one step closer to achieving its long-term goals. It is the most effective way to start serving customers pizzas and pastas on day one, while allowing for continuous improvement and expansion of the kitchen.
As mentioned before, the benefit of a sound data strategy is not only to manage limited budget and scarce resources, but also to achieve alignment across the organisation and ultimately equip it to navigate an unpredictable future while adding incremental value along the way. Identifying and prioritising data use cases is critical for mapping out this path into the future. The support from other parts of the business can truly be the difference between ideas on paper and data models in production. What then separates the adequate from the exceptional is a data strategy that is amenable to iteration, allowing a business to meet any unforeseen challenges or seize new opportunities effectively.
We firmly believe that design (advisory) and build (delivery) go hand in hand. And what better than to have both under the same roof? Having both the experts to design and implement a data strategy ensures we don’t design strategies out of thin air, or build the right solutions for the wrong problems. Instead, we focus on building solutions for problems that are high priority to the business. On top, we can leverage the expertise of other Mantel Group brands such as Pretzel Lab, Digio, CMD, Kasna, Cuusoo and Azenix. With ample hands-on experience, we feel confident to help any Australian or New Zealander business drive their data-driven ambitions, from data strategy & ideation, data governance, data platforms and foundations, data storytelling and data science.We look at the big picture but identify immediate actions to drive fast results.
We typically start with an assessment of the current state and description of the target state covering tech (architecture, tech stack, tooling) and people (capabilities, culture, process and governance). By identifying and evaluating user-oriented data use cases, we piece the puzzle together for a data roadmap and action plan. The development of a data roadmap marries together big picture long-term ambitions with thin-slicing into immediate actions based on thoroughly vetted use cases. We believe that alongside a use case driven approach that enables fast time to value, a combination of data capability uplift, change management, and a phased migration to new platforms with accompanying tooling realises long-term goals. Combined, these structural changes and identified use cases, will drive the strategic roadmap forward. Finally, we will tie everything together in a compelling business case to appropriately budget resources and evaluate progress.
Want to discover more?
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Data Thinking FAQ’s
What is Data Strategy?
A Data Strategy sets out the goals, priorities and resources required to optimise business value from data in line with the corporate strategy, taking into consideration the value add and required investment.
What is Design Thinking?
The term ‘design thinking’ refers to an iterative, divergent and convergent process to drive innovation. It takes a human-centered approach to understanding the needs of a user and more broadly the business, surfacing solutions that achieve alignment and buy-in.
What is Data Thinking?
By combining Design Thinking concepts with traditional strategic tools, a modern approach to designing strategy has emerged: Data Thinking. Priorities are established based on user needs, alignment across the organization is emphasized, and a roadmap is developed that is adaptable but still guiding.
What is a Use Case?
A use case describes how a person who actually uses a process or system will accomplish a specific goal.