This week marks the beginning of limited numbers of fans being allowed back into sports grounds to watch their teams. Whether it’s rugby, football or cricket, though, these sports are a lot like data strategy; everyone needs to know the rules before you have an enjoyable game on your hands. These rules, or what we call the ‘method’, are one of the four pillars of an effective data strategy. But what does a good organisational method for data look like, and how can data leaders develop one?
At its most basic, the method by which data is extracted and used by a business should be determined by a clear set of best practices within an organised structure. That isn’t to say that it needs to be a rigid structure that puts people into predefined boxes, but rather one where roles and responsibilities are clear and agreed. To go back to that team sports analogy, you don’t just need to know the rules to be a great sports team; just like football and rugby, data is a team game which requires different talents and roles to click together for a great end result.
For an organisation just starting on its data journey, a centralised system of data governance, where data strategies are decided by one leader or leading team, is ideal to kickstart a data transformation. Once an organisation becomes better at using its data, it can then move toward a hybrid system where data decision-making can be spread across multiple departments for quicker and more bespoke results, within a general organisational framework.
Data strategy: creating an organisational framework
But what exactly should that organisational framework look like? My motto when it comes to data regulations has always been that less is most definitely more. Businesses that want a data strategy to be successful and rewarding need to make sure data guidelines are simple, clear and, most importantly, short!
Why is shorter better? Firstly, the shorter a document, the easier it is to update on a rolling basis to ensure it reflects the reality of the business’ situation and its evolving needs.
Perhaps more importantly, though, these documents must be accessible and understandable to as many people in the organisation as possible. Data strategies shouldn’t only be readable for data scientists; if an organisation’s ultimate aim is for data to permeate all aspects of its business (here’s a tip: it should be!), then it’s critical that anyone, at any level and in any department, can understand the rules around data.
Data should form an integral part of an overarching business strategy, rather than simply being implemented as a wholly separate department.
Ensuring an organisations’ data strategy is easy to understand is, of course, only part of the journey toward a data-enabled business. When it comes to implementing a data strategy, I actually think the most important thing to remember is that a data operation shouldn’t just be bolted onto the business. Data should form an integral part of an overarching business strategy, rather than simply being implemented as a wholly separate department. If data is to be successful in ultimately transforming the business, it needs to fit into, and be based on, the organisation’s existing business strategy and framework. Just like the wider business operating model, an organisations’ data operating model needs to be dynamic, constantly updated to fit the wider company goals and flexible enough to react to new business priorities.
Data governance and policy frameworks can definitely sound restrictive on paper, and there’s an important balance to strike between regulation and freedom to be creative. That being said, though, the reality is that a good policy document can actually foster the creativity needed for an organisation to flourish. Playing by the rules of data shouldn’t mean being constricted.
Good data methodology and frameworks are crucial to creating a data-enabled environment that brings every part of the organisation along for the data-driven business transformation journey.
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