Data Demystified- Exploring the Three Levels of Data Maturity

Business leaders increasingly recognise the importance of leveraging data to drive growth and innovation. However, navigating the complexities of a data journey can be daunting, especially for those who are more accustomed to defining winning business strategies than crunching numbers. Our recent webinar, 'Data Demystified: A Roadmap to Data Maturity', hosted by Lorraine Deane, CSO of Hyperclear Tech, featured a dynamic discussion with industry experts Ian van Niekerk and Thinus van Rooyen. As Head Data Architect and Principal Data Architect at Cyberlogic DataGeek, Ian and Thinus drew on their extensive experience to share insights into the core principles of a successful data journey.   

This article summarises the key takeouts from the webinar, giving you a high-level roadmap to get the most out of your organisation's data no matter where you are on your data journey. 

We Have Not Started our Data Journey. Where Do We Begin?   

At the initial stage of the data maturity journey, organisations often grapple with scattered data sources and rudimentary data practices. Many businesses start by using Excel spreadsheets, disparate data extracts from CRM systems, and other isolated data sources. This stage of the data journey is characterised by the need to create a unified approach to data management. 

The first step of any data journey is to start with a clear business need or question to be answered, such as improving revenue, optimising stock levels, or understanding customer behaviour.  

Understanding the business need is critical in defining the data journey. Once you have a clear view of what you need the data to tell you, the next step is to build prototypes to ensure your data provides the answers you need. These proof-of-value projects can be simple manual processes built using tools like Excel or Power BI to create initial dashboards that provide insights that answer the key business questions identified. 

This stage requires an iterative approach. As prototypes evolve, it is essential to incorporate more data to continuously enhance insights and value. The goal is to demonstrate the potential of data-driven decision-making to stakeholders, which can usually be achieved within weeks rather than months if approached correctly. This rapid value delivery into the business quickly justifies further investment in data.

We’ve Implemented Some Data Reports. Where to Now?  

Once an organisation has implemented some form of Business Intelligence (BI) and is reaping the initial benefits, it can face new challenges. As more users access data, the rate of new requests and the need for operational monitoring increase, which can slow the delivery of new reports and create bottlenecks. 

At this stage, organisations encounter the challenge of managing the rate of delivery. Operational monitoring becomes increasingly time-consuming as the data infrastructure scales, requiring more effort to ensure the data is accurate and refreshed appropriately. 

To mitigate this, organisations must develop frameworks that create repeatable and efficient processes.  

This can involve using data-driven pipelines and configuration tables to speed up the addition of new data tables. Proactive monitoring is crucial. Implementing proactive alerts to identify and address issues before they impact users builds trust and ensures data reliability. Additionally, more frequent data refreshes become necessary as reliance on data grows. This increased frequency requires the implementation of incremental loads to handle updates efficiently. 

We Have a Data Warehouse and Are Running a Full Suite of Reports. How Do We Get to the Next Level? 

Organisations at the advanced stage of data maturity typically have a data warehouse and run a full suite of reports. At this stage, the focus often shifts to data governance, ensuring data quality, transparency, and security. While organisations in the earlier phases of maturity should build with data governance best practices in mind, it is often only at this point that it becomes a burning issue. Data governance is critical for managing the data lifecycle and making data assets more transparent and reliable. 

As we know, ensuring high-quality data is paramount to enabling better decision-making. Transparency in data sources and calculations is essential to building user trust, clarifying how data is derived, and ensuring consistency in reporting. Security becomes a critical concern, as protecting sensitive information is crucial for maintaining trust and compliance with regulations. 

Having a robust data governance framework in place involves defining processes for data management, implementing data quality controls, and ensuring compliance with regulations. The benefits are substantial. Improved data quality leads to better business decisions, enhanced transparency increases trust in data, and stronger security measures protect the organisation’s data assets. 

A Look into the Unitrans Data Maturity Journey 

Unitrans, a leading supply chain and logistics business, operates in a highly competitive environment where merely transporting goods from point A to B is no longer sufficient. To stay ahead, they have focused on enhancing operational efficiency and providing additional value to their clients. Unitrans collects extensive data from vehicle telemetry, driver monitoring systems, trip management, warehouse systems, and even drones monitoring agricultural sites. This meant data availability was not an issue, but the usability of this data certainly was. Key issues included centralising data from diverse systems, ensuring data consistency and quality, and selecting the right skills and infrastructure to manage their data effectively. 

Overcoming Challenges Through Collaboration 

To address these challenges, Unitrans embarked on a journey to enhance its data capabilities and maturity. They focused on creating a centralised data platform with consistent quality controls and a robust data governance framework. This allowed them to turn vast amounts of raw data into actionable insights. Importantly, they emphasised the role of communication and transparency in engaging business users with the data, ensuring they understood and trusted the information provided. Alex Black, Data Solutions Engineer at Unitrans, noted the importance of making data business-readable and accessible, stating, "It's no point us building very sophisticated tools that the business can't make sense of." Executives play a critical role in shaping BI strategies to accelerate their business growth. 

The Value of Partnership with Cyberlogic DataGeek 

The partnership with Cyberlogic DataGeek played a crucial role in Unitrans' data journey. By collaborating with Cyberlogic, Unitrans could leverage industry expertise and advanced technologies without diverting focus from its core operations. This partnership enabled Unitrans to concentrate on strategic goals like fostering a data-driven culture and maintaining scalable, high-quality data assets.  

This collaborative approach ensured Unitrans could achieve significant advancements in their data maturity while maintaining operational efficiency and driving business value. 

Taking the Next Step in Your Business’s Data Journey  

Understanding and navigating the three levels of data maturity is crucial for any organisation aiming to leverage data effectively. By taking iterative steps based on a clear business need, organisations can transform their data practices, drive better decision-making, and ultimately achieve greater success in a data-driven world.  

At Cyberlogic DataGeek, our team of experienced data experts are ready to help you make the most out of your business's data assets. Whether you're looking to streamline operations, optimise decision-making processes, or uncover hidden opportunities, our data architecture specialists have the expertise to drive your business forward. To schedule an exploratory call, contact us at hello@cyberlogic.co.za and let our data experts help you unlock the value in your business data.