The future of data analytics: Why organisations need more than numbers

Miles Harris, Director, Tech Talent Solutions & Customer Success, Peregrine

As organisations respond to rapidly emerging technologies, the roles data analysts and data scientists play are also changing. Now, data analysis sits in the centre of organisations where leaders make decisions, not at the periphery in sole service to a product or solution. But this relocation doesn't come without challenges. How can organisations define what they need to know to make the data-driven decisions that lead to success, growth, and progress???

Aligning with leadership needs?

The modern data analyst and data scientist works closely with leadership to align their efforts to overarching business objectives. This alignment is critical for several reasons:?

1. Strategic focus: By understanding the organisation's goals and challenges, data professionals can tailor their analyses to address key business questions and support strategic decision-making.?
2. Resource allocation: Closer collaboration with leadership helps prioritise data projects, ensuring that time and resources are invested in areas with the greatest potential impact.?
3. Communication bridge: Data experts increasingly serve as translators between technical insights and business strategy, helping leaders understand complex data in actionable terms.?
4. Proactive problem-solving: With a deeper understanding of business needs, data professionals can anticipate future challenges and proactively develop analytical solutions.?

To achieve this alignment, many UK organisations have implemented structural changes. Some have created chief data officer (CDO) positions to represent data interests at the executive level. Others have integrated data teams directly into business units, fostering closer collaboration between analysts and decision-makers.?

Deciding what to measure and analyse?

• Stakeholder consultation: Regular meetings with department heads and executives to understand their key performance indicators (KPIs) and information needs.?
• Business objective mapping: Aligning data collection and analysis efforts with specific business goals, ensuring that every metric serves a purpose.?
• Data audits: Conducting comprehensive reviews of available data sources to identify gaps and opportunities for new insights.?
• Iterative feedback loops: Establishing systems for continuous feedback from end-users of data insights, allowing for refinement of analytical approaches.?
• Cross-functional teams: Forming multidisciplinary groups that bring together data experts, subject matter specialists, and business strategists to identify key areas for analysis.?

The decision-making process often follows a structured approach:?

1. Problem definition: Clearly articulating the business question or challenge that needs to be addressed.?
2. Data assessment: Evaluating available data sources and determining if additional data collection is necessary.?
3. Methodology selection: Choosing appropriate analytical techniques based on the nature of the problem and available data.?
4. Pilot analysis: Conducting initial studies to test hypotheses and refine analytical approaches.?
5. Scale and implementation: Expanding successful analytical models across the organisation.?
6. Continuous monitoring: Regularly reviewing the relevance and accuracy of metrics and analyses, adjusting as needed.?

Challenges and opportunities?

The evolving role of data professionals balances benefits and presents challenges. One of the most important challenge is understanding data quality as the scope of what we can collect increases. Ethical considerations also play a role as navigating privacy and the appropriate use of data become important. Data professionals also need to develop their skills against a landscape of continuing technological change and become influencers in organisations that haven’t embraced data-driven decision-making. Helping to manage organisational change should?become part of the data leaders job description.??

The challenges also present opportunities for data professionals to drive innovation and create value:?

• Advanced analytics: Implementing machine learning and AI techniques to uncover deeper insights from complex datasets.?

• Predictive modelling: Developing forecasting tools to help organisations anticipate market changes and customer behaviour.?

• Data democratisation: Creating user-friendly dashboards and self-service analytics tools to empower non-technical staff with data insights.?

• External data integration: Combining internal data with external sources to provide a more comprehensive view of the business environment.?

What's next??

The role of data analysts and data scientists has transformed from largely technical positions to strategic partners in decision-making. By aligning closely with leadership needs and employing collaborative approaches to reach priorities, people with skills in data analysis are driving a data-centric revolution in business operations.?

As organisations continue to recognise the competitive advantage that data-driven insights provide, the importance of these roles will increase. The most successful data professionals will be those who can balance technical expertise with business acumen, effectively communicating complex findings to diverse stakeholders and driving business outcomes.?

The future of data analysis and science in UK organisations is one of increased integration, strategic importance, and transformative potential. As these roles continue to evolve, they will play a crucial part in shaping the future of business in an increasingly data-driven world.?

Get in touch to discuss the future of data analytics and meet the people with the knowledge and skills to help your organisation.