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Five Pillars of a Successful Data Strategy

A robust data strategy is crucial for any businesses or organisation aiming to leverage data for growth and efficiency. For social landlords, understanding and implementing the core elements of a successful data strategy can make a significant difference when it comes to the cost-efficient delivery of services to tenants. Here, we’ll explore the five fundamental components necessary for crafting a winning data strategy, ensuring alignment with business objectives, identifying gaps, quantifying benefits, developing a roadmap, and managing the unexpected.

Alignment with Business Objectives

The cornerstone of any successful data strategy is its alignment with business objectives. Without this alignment, the strategy loses its purpose and direction. Business objectives, whether broad such as increasing revenue or specific like reducing tenant complaints, provide a clear pathway for the data strategy.

Why Alignment Matters

Business objectives should drive the data strategy, not the other way around. For instance, if an organisation aims to enhance tenant engagement, the data strategy should prioritise data sources related to customer comms, behaviour, and contact resolution performance. This targeted approach ensures that the data efforts are streamlined and focused on achieving the defined goals, excluding irrelevant data sources.

Practical Example

Consider a social landlord with a goal to decrease repeat repairs jobs. The data strategy would need to focus on repair job data (what work was carried out, how long the job took, components used etc), customer interactions, and repair operative performance data. This focus helps exclude unnecessary data and keeps the strategy aligned with the business objective, ensuring a more efficient and impactful approach.

Gap Analysis

A thorough gap analysis is essential for identifying any deficiencies that may hinder the achievement of business objectives. This process involves evaluating existing data sources, pinpointing missing data, and ensuring the accuracy of analytics.

Identifying Gaps

Gap analysis should not only consider current data but also identify what data is missing. For example, a council might miss costs related to third party contractor labour or server hosting in their financial calculations, leading to inaccurate conclusions. Identifying these gaps early helps in refining the strategy and ensuring all relevant data is considered.

Ensuring Data Accuracy

Moreover, the accuracy of the analytics performed on the existing data is crucial. Having the right data but using flawed analytics can lead to incorrect outcomes. Therefore, part of the gap analysis should involve reviewing and improving the analytics methods to ensure they align with the business’s quantitative objectives.

Robust Roadmaps

An actionable roadmap is necessary for translating the data strategy into reality. This roadmap should detail the sequence of sub-projects, their dependencies, and timelines.

Creating Dependencies

The roadmap should consider resource availability, critical deadlines, and alignment with other business initiatives. It’s important to include existing projects to ensure a cohesive approach. Formal checkpoints, such as quarterly reviews, should be incorporated to assess progress and make necessary adjustments.

Dynamic and Adaptable

The roadmap should be flexible to adapt to changing circumstances. Regular updates ensure that the data strategy remains relevant and aligned with evolving business objectives.

Quantifying Benefits

Quantifying the benefits of a data strategy helps in justifying the investment and understanding the potential returns. This involves breaking down the data strategy into smaller projects, each with associated costs and expected returns.

Building a Business Case

For each sub-project, a business case should be developed. This helps in understanding the impact of each initiative and deciding which projects to prioritise based on their potential return on investment (ROI). For instance, a sub-project focused on data cleansing might not have immediate returns but is essential for the success of subsequent projects that do generate revenue.

Example Sub-Project

An example could be a data cleansing project that prepares the groundwork for more advanced analytics projects. While the cleansing process itself might not directly increase revenue, it ensures the accuracy and reliability of data, which is critical for making informed business decisions later on.

Provision for the Unexpected

A successful data strategy must be adaptable to unforeseen changes and challenges. This involves modelling potential ‘what if’ scenarios and preparing contingency plans.

Preparing for Change

Unexpected situations, such as the introduction of a new service or an acquisition, can significantly impact the data strategy. Identifying top ‘what if’ scenarios and planning accordingly ensures that the strategy remains resilient and effective.

Scenario Planning

For example, if a new service provision is unexpectedly successful, the data strategy should be able to accommodate increased data volumes and new types of data. Regular involvement of the original strategy team can help navigate these changes smoothly.

The Bottom Line

A successful data strategy is anchored in aligning with business objectives, identifying and addressing gaps, quantifying benefits, developing a clear roadmap, and managing unexpected challenges. By focusing on these core elements, social housing providers can leverage data to drive growth, efficiency, and innovation.

Starting with a data visualisation project can be a great first step in tracking and managing business metrics throughout the implementation of the data strategy. Remember, the technology used to implement the strategy should be chosen after defining what the business needs from its data, not before. This approach ensures that the data strategy remains focused on achieving the business objectives rather than being driven by technological constraints.

For more information around data strategy in a social housing context, take a look at our Data Strategy: What It Is & Why You Need One and Unlocking the Power of Data Science in Social Housing articles. If you’d like to discuss data strategy further with one of our experts, get in touch via our Contact Us page.

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