Data analytics has truly gone mainstream. Organisations across the world, in nearly every industry, are embracing the practice. Despite this, however, the execution of data analytics remains varied – and not all data analytics approaches are made equal.
For most organisations, the most advanced data analytics team is the centralised Business Intelligence (BI) team. This isn’t necessarily inferior to having a specialist data science team in place. However, the world’s most successful BI teams do embrace data science principles. Comparatively, this isn’t something that all ‘classic BI teams’ nail.
With more and more mature organisations benefiting from best practice data analytics – competitors that haven’t adapted risk getting left in the dust. The charter and organisation of typical BI need to be set up correctly for data analytics to address increasingly complicated challenges and drive transformational change across the business in a holistic manner.
Where is classic BI lacking?
BI’s primary focus is descriptive analytics. This means summarising what has happened and providing visualisation of data through dashboards and reports to establish trends and patterns. Visualisation is foundational in data analytics. The problem lies in how this visualisation is being carried out by BI teams. It’s often the case that BI teams are following an IT project model. They churn out specific reports like a factory production line based on requirements set by another part of the business. Too often, the goal is to deliver outputs quickly in a visually appealing way. However, this approach has several key deficiencies.
Firstly, it’s reactive rather than proactive. It is rooted in delivering reports or visualisations that answer predefined questions framed by the business. This is opposed to exploring data to uncover new insights or solve open-ended problems. This limits the potential of analytics to drive new innovative solutions.
Secondly, when BI teams follow an IT project model, they typically report to central IT teams rather than business leads. They lack the authority to influence broader business strategy or transformation. Therefore, their work remains siloed and disconnected from the core strategic objectives of the organisation. For too many companies, BI has remained a tool for looking backwards, rather than a driver of forward-thinking, data-driven decision-making. The IT model of collecting requirements and building to specification is not the transformational process used by world-class data science teams. Instead, understanding the business and driving change is a central theme seen within the world’s leading analytic organisations.
The case for centralisation
To unlock the full potential of data analytics, organisations must centralise their data functions. They need a simple chain of command that feeds directly into the C-Suite. Doing so aligns data science with the business’s strategic direction. Doing so successfully creates several advantages that set companies with world-class data analytics practices apart from their peers.
Solving multi-domain problems with analytics
A compelling argument for centralising data science is the cross-functional nature of many analytical challenges. For example, an organisation might be trying to understand why its product is experiencing quality issues. The solution might involve exploring climatic conditions causing product failure, identifying plant processes or considering customer demographic data. These are not isolated problems confined to a single department. The solution therefore spans multiple domains, from manufacturing to product development to customer service.
A centralised data science function is ideally positioned to tackle such complex problems. It can draw insights from various domains as an integrated team to create holistic solutions without different parts of the organisation working at odds with each other. In contrast, where data scientists report to individual departments (centralisation isn’t happening) there’s a big risk of duplicating efforts and developing siloed solutions that miss the bigger picture.
Creating career pathways and developing talent
It should be obvious to state – data scientists need career paths too. The most important asset of any data science domain is the people. But despite this, where teams are decentralised, data scientists tend to work in small, isolated teams within specific departments. This limits their exposure to a broader range of problems and stifling career advancement opportunities.
For example, a data scientist in a three-person marketing analytics team has fewer opportunities and less interaction with the overall business than a member of a 50-person corporate data science team reporting to the C-suite.
Centralising the data science team within a single organisational structure enables a more robust career path and fosters a culture of continuous learning and professional development.
Data scientists can collaborate across domains, learn from each other and build a diverse skill set that enhances their ability to tackle complex problems. Moreover, it’s easier to provide consistent training, mentorship and development opportunities where data science is centralised, ensuring that teams are fully equipped with the latest tools and techniques.
Linking analytics across the business
A centralised data science function acts as a valuable bridge across different parts of the business. Let’s take an example. Two departments approach the data science team with seemingly conflicting requests.
The supply chain team wants to minimise shipment costs and asks for an analytic that will identify opportunities to find new suppliers near existing manufacturing facilities.
The purchasing team, separately, approaches the data science team to reduce the cost of each part. To do this, they want to identify where they have multiple suppliers, and move to a model with a single global supplier that has much larger volumes and will reduce costs. These competing philosophies will each optimise a piece of the business, but in reality, what should happen is a single optimised approach for the business.
Instead of developing competing solutions, a centralised data science team can balance competing objectives and deliver an optimal solution that’s aligned with overall strategy. Cast in this role, data science is the strategic partner contributing to the delivery of the best outcomes for the organisation.
Leveraging analytics methods across domains
The best breakthroughs in analytics come not from new algorithms, but from applying existing methods to innovate use cases.
A centralised data science team, with its broad view of the organisation’s challenges, is more likely to recognise these opportunities and adapt solutions from one domain to another. For example, an algorithm that proves successful in optimising marketing campaigns could be adapted to improve inventory management or streamline production processes.
Driving organisational change and analytics maturity
Finally, a centralised data science function is best positioned to drive the overall analytic maturity of the organisation.
This function can standardise governance, as well as best practices. In doing so, it can drive the change management processes, ensuring that data-driven decision-making becomes ingrained in company culture.
The way forward
The shift from classic BI to a centralised data science function is not just a structural change; it is a crucial strategy for companies looking to stay ahead in a competitive, data-driven landscape. By centralising data science and enforcing a charter for BI to solve key problems of the organisation rather than be dictated to, companies can solve complex, cross-functional problems more effectively, foster talent development, create inter-departmental synergies and drive a culture of continuous improvement and innovation.
This evolution is what sets world-class companies apart from the rest. It might just be the transformation your company needs to unlock its full potential.
- Data & AI
