Scaling Your Data: The Walk Stage
In today’s data-driven world, businesses are increasingly realising the importance of leveraging analytics to gain insights and make informed decisions. Analytics is a journey and we’ve separated out that journey into three stages: Walk, Run and Fly. The Walk stage is all about getting started with data, building a data stack and getting your first key insights. In this article, we will explore key steps to get started with data and highlight important considerations at each stage.
Agreeing on Business Priorities and Questions
Before diving into the vast ocean of data, it is crucial to identify your business priorities and questions. What are the key challenges or opportunities your organisation is facing? What questions do you need answering? Don’t just focus on the problems though, consider the levers and decisions that you can make to affect those problems. It’s no use getting a lot of data on competitor pricing if you are ultimately not willing to change your own. While it can be difficult to narrow your focus, we always recommend applying data to the business function that will see the biggest ROI, and then expanding is better than trying to do too much at once.
Top-Level Daily Reports: A Good Starting Point
When starting with data, it is advisable to think about the top-level daily reports you will need. These reports provide a snapshot of your business’s performance and can serve as a foundation for further analysis. Define your Key Performance Indicators (KPIs) and establish clear definitions for the customer journey and other important business metrics. This initial focus on high-level reporting will help you gain valuable insights and set the stage for more advanced analyses in the future.
Choosing the Right Tools and Infrastructure
Choosing the right tools and infrastructure is a critical decision that can significantly impact your data journey. While off-the-shelf tools may seem tempting due to their quick implementation, we strongly advised you to build your own data stack. Off-the-shelf tools often come with limitations and may incur rising costs as your data volume and usage increase. By building your own data stack, you have more flexibility and control over your data infrastructure, allowing for scalability and customisation. While it may require more initial investment, the long-term benefits outweigh the costs.
Building the Right Team
To kickstart your data journey, you will typically need at least one data engineer and one data analyst. These roles are essential for building and maintaining the necessary infrastructure and extracting insights from your data. However, assembling a data team and building out your infrastructure can take time, often ranging from six to twelve months. It is crucial to consider the opportunity cost during this period and plan accordingly. While waiting for your team to be fully operational, you can explore interim solutions or seek external expertise to bridge the gap.
Do Not Skip Data Modelling: Establishing a Strong Foundation
Data modelling is a critical step that should not be rushed or skipped. It is during this stage that many projects go wrong, as insufficient quality assurance and validation lead to shaky foundations for future analyses. Take the time to ensure your data is properly structured, cleaned, and validated before proceeding. Invest in comprehensive data modelling processes, including documentation and data lineage, to maintain data integrity and ensure accuracy throughout your data journey.
Automate Extraction for Efficiency
As you progress in your data journey, automation becomes increasingly important. Automating data extraction and integration processes saves time and minimises manual errors. Identify relevant data sources and explore automation tools and techniques to streamline data collection. By automating repetitive tasks, you free up valuable time for analysis and decision-making, ultimately improving efficiency and productivity.
Think Beyond Reporting
While reporting is of course a valuable aspect of data analysis, it is essential to think beyond this. Consider how data can be leveraged to optimise various areas of your business as part of a longer-term plan. Map out your customer journey and touchpoints and look for areas of key intervention where a data-driven approach might make a difference.
Getting to Value Quickly: Start Small and Iterate
One common mistake organisations make is trying to tackle too much data modelling at once. Rather than creating an overwhelming laundry list of data sources to model, it is more effective to start with your most relevant data source and iterate outwards. Identify the data that is most critical to answering your initial business questions and focus on extracting value from it. This iterative approach enables you to gain insights and deliver value more quickly, setting the stage for further data exploration and modelling.
In conclusion, embarking on a data journey can be both exciting and daunting. By following these tips, you can set a solid foundation for success. Remember to prioritise your business objectives, choose the right tools and infrastructure, and build a skilled team. Don’t underestimate the value of proper data modelling and strive for automation where relevant. Or if you need an extra hand, why not get in touch with the friendly team at 173tech?