From the early days of my career at Inchcape, around 1993, I had the privilege of working closely with senior managers, assisting them in preparing reports and presentation packs for board meetings. Back then, tools such as PowerPoint didn’t exist. We used Harvard Graphics to create individual slides, which were printed onto transparencies for projectors. After finalising the presentation, it was my duty to ensure that all slides were neatly printed, bound, and in perfect sequence. I often accompanied our Managing Director, carrying the briefcase to ensure everything was set for his keynote during important meetings.
Looking back, it’s clear that Inchcape was ahead of its time in being a data-driven organisation. The technology may have been basic by today’s standards, but the accuracy and efficiency of our operational framework were exceptional. Sales data was captured at the source, aggregated by a reliable MIS system every night, and routinely used by managers for analysis and decision-making. A common ERP system was deployed. Front-office and back-office systems were integrated. Today, when I see businesses struggle to achieve even a fraction of that level of data maturity, I often wonder why!
Modern businesses generate vast amounts of data daily, have robust IT systems, yet many still fall short when it comes to using that data effectively for decision-making. In my experience, the challenges are rarely about technology alone; it’s about culture, processes, and mindset. Inchcape got that right in 90s where modern businesses still struggle with better technology and access.
Based on my experience with Inchcape, here’s why I believe organisations often fail to fully leverage their data:
1. Lack of trust in data and fact-based decision-making
One of the main issues I come across is that many business managers don’t fully trust their data, which leads to resistance in using it for decision-making. It’s not that they don’t believe in data-driven strategies per se; it’s often due to past experiences with inaccurate, incomplete, or inconsistent data. When the data hasn’t been reliable in the past, managers understandably prefer to rely on their intuition or experience. But without building trust in the data, even the most sophisticated systems won’t make much of a difference.
2. Siloed systems make data integration difficult
Many businesses still have multiple systems operating in isolation – finance, marketing, sales, and operations all storing their data in silos. The issue here is that data remains trapped within these systems, making it incredibly difficult to extract, correlate, and get a holistic view. Without proper automation and integration, pulling data from different sources becomes a manual, resource-heavy task, which means opportunities are often missed, and insights are delayed.
Often, this silo is encouraged by stakeholders because it gives them a feeling of control and ability to restrict the lateral flow of reporting data to other stakeholders, simply because their poor OLAs that impact customer SLAs will be used against them.
3. Lack of focus on data stewardship
Another key challenge is that many organisations overlook the need for proper data stewardship. Data isn’t just something that should be collected; it needs to be managed, cleaned, aggregated, and modelled to meet the needs of various stakeholders across the business. Without a solid data stewardship framework in place, data becomes fragmented and inconsistent, which only reinforces the mistrust I mentioned earlier. Ensuring that data is ready for analysis is a foundational step that too often gets neglected.
If you’ve ever attended a demo of a data analytics and visualisation platform, you’ve likely noticed clean, polished graphs and well-organised visuals designed to impress. However, achieving that level of clarity within your own enterprise is often far more challenging. The primary reason lies in your master data. In most business systems, master data is neither well-structured nor clean, and this directly impacts the quality of analytics, resulting in cluttered and often unpresentable visualisations.
We are in 2024, and still, we have various functions in the organisations where data is not even captured at source (e.g. HR, Recruitment, Pre-Sales etc.). The data stewardship need to ensure that all key functions are involved in the organisations data strategy.
4. Adhoc management reporting processes
I often see organisations where business reporting and dashboards are not up to the task. The systems in place might be capable of basic reporting, but they fall short when it comes to providing deep insights or enabling automated decision-making. As a result, management reporting tends to be manual, requiring a lot of tinkering to get the right figures. This manual process is not only inefficient but also prone to errors.
This aspect, I found was very well taken care during my stint with Inchcape. It took me some time initially, but, as I got deeply involved with the preparation of management reporting pack and reviews, I could see that the reporting process was very well thought and implemented. It captured information that was obtained from various systems but also provided opportunity to capture and anayse human inputs on company performance, people and areas of improvements.
To move forward, businesses must invest in defining the metrices and reporting tools that can automate the process and deliver accurate, real-time insights. Performance reporting shouldn’t be a burden on the people.
5. Technical debt and underfunding of data projects
Finally, many organisations face the issue of technical debt. Simply put, data projects don’t get the budget or attention they need. They are seen as a reporting overheads. Often, data engineering tasks are approached in a piecemeal fashion, which creates short-term fixes but long-term headaches. Executives might support the idea of becoming a data-driven organisation in principle, but without a clear strategy or appropriate funding, initiatives stall. The result is a backlog of projects and delayed progress, which can become overwhelming to manage. Lack of funding also applies to employees training on modern data analytics tools.
Finally, the need for a robust action plan
If organisations want to overcome these barriers, they need more than just better tools. They need a clear, actionable plan. This includes:
- Cultural shift: Building a culture of trust in data, where leaders understand its value and embrace it for decision-making.
- Systems & tools: Investing in tools that allow seamless data integration, so silos are broken down and data flows across the business.
- Data stewardship: Implementing a strong data governance framework to ensure data is accurate, clean, and accessible to all who need it.
- Advanced reporting framework: Upgrading to modern reporting systems that eliminate manual processes and provide automated, real-time insights.
- Long-term investment: Allocating proper budgets for data engineering projects and committing to a long-term strategy rather than piecemeal efforts.
In my view, tackling these areas head-on will enable businesses to unlock the full potential of their data. With the right approach, organisations can move from data chaos to data clarity; making better, faster decisions that drive success.
There are a number of business, who have achieved this through persistence, continuous investments in systems, employee education and encouragement to be at the forefront in their league. You can be the one too!
