The built environment is evolving fast, and data has emerged as a pivotal force, capable of driving innovation, enhancing efficiency and even driving brand new revenue streams.
However, the true potential of this data can only be realised through a meticulously crafted discovery methodology.
Our methodology consists of 6 steps that are the foundations for everything you’re about to build, including any potential future needs.
Why a Discovery Methodology is Vital to Become Data Driven?
A discovery methodology is the systematic process of identifying & gathering information from the myriad different stakeholders within any given business. PoV’s need to be taken from strategic leaders, middle managers and operational employees.
In the context of the built environment, this methodology forms the backbone of any successful data analytics strategy. It ensures that every piece of infrastructure built, every software decision, and every piece of data collected serves a purpose and contributes to over-all data analytics strategy and informed decision-making.
6 Steps for an Effective Data Analytics Strategy
Step 1: Define Clear Objectives for Your Data Analytics Strategy
The starting point of a great discovery methodology is to establish clear and achievable objectives (from different levels of seniority within the business).
- What specific outcomes are you aiming to achieve with your data analytics strategy?
- What are the business’s overarching strategies?
- What are the c-suite's personal drivers?
- What are the management layer missing to be able to do their job in the most effective way?
- How could operational staff benefit from having better access to data?
Here at Datore we gather this information through a series of workshops with groups from all areas of the business, to give us a complete picture.
Step 2: Identify Data Sources Relevant to Your Data Analytics Strategy
It’s only with that clear picture of what the business wants/needs/would love to achieve – that we can begin the technical process.
The built environment generates a vast array of data from various sources, including IoT devices, building management systems, and historical project data – as well as the typical sales, HR, Marketing, Operational data found in all businesses.
A critical step in the discovery process is to identify which data sources are relevant to the objectives identified in step 1. This targeted approach prevents data overload and ensures that the data collected is pertinent and valuable.
Step 3: Data Collection and Data Integration
Gathering data from multiple sources is only part of the challenge; integrating this data into a cohesive system is equally crucial.
Building an environment where these data sources can be brought together in a logical way via APIs, Data dumps, emails, spreadsheets etc – that can then be sliced, diced and accessed by different parts of the business for their particular needs.
Step 4: Ensuring Data Quality for Success of Your Data Analytics Strategy
Great-quality data is the foundation of any successful analytics strategy. Implementing stringent data governance practices ensures that your data is accurate, consistent, and reliable is imperative.
Regular data cleaning and validation processes are essential to maintain the integrity of your data, which in turn, enhances the reliability of your data insights.
Step 5: Stakeholder Engagement in Data-Driven Business
From here, it’s key to bring your stakeholders on the journey from data to actionable insights with you. By building out your analytics environments in partnership with your stakeholders they feel part of the process and keep you honest as to what will and won’t be valuable.
Step 6 (the extra step): Continuous Improvement of Your Discovery Methodology
The discovery process is not a one-and-done effort but an ongoing cycle of evaluation and refinement. By continuously monitoring your analytics projects and gathering feedback, organisations can adapt and improve the impact of their data insights over and over. This iterative approach ensures that the data analytics strategy remains aligned with evolving goals and challenges.
A well-executed discovery methodology does more than just generate data insights.
It creates a foundation for a data-driven culture within the organisation.
It empowers built environment professionals to make informed, data-driven decisions that drive efficiency, innovation, sustainability and revenue.
Whether it’s optimising energy usage, predicting maintenance needs, or enhancing the occupant experience, the foundations built through a robust discovery methodology can lead to total business transformations in many cases.
Conclusion - Initial Steps to Become Data-Driven
In the built environment, the stakes are high, and the opportunities for leveraging data are absolutely immense. Ensuring that your data analytics strategy is built on solid foundations is EVERYTHING and without a tried and tested methodology, this can be hard to achieve.
At Datore, our Analytics as a Service discovery and delivery methodologies have been developed off the back of 100s of successful analytics implementations and are continuously iterated to keep up with best practices. If you’re interested in what we do and how we do it, give me a shout David.leslie@datore.co – I love talking about this stuff 😊
