The Need for Data Visualization (and How to Get There)
Many organizations are data rich, but today, merely having data is not enough. A recent report by Qlik has found that 76% of key decision makers aren’t confident in their ability to read, work with, analyze or argue with data. A recent study by Gartner on business intelligence and analytics maturity also highlighted the issue, with low levels of maturity applying to 87% of surveyed organizations. As a result, it is hardly surprising that organisations struggle to use data in meaningful and insightful ways, despite being in an era where many perceive data to be ‘the new oil’.
In the highly competitive payments industry, having key data at your fingertips enables better, faster decision making and is often proven to be a crucial differentiator. Common complaints from fuel card account managers involve not knowing profitability levels, the impact of fees on sales, or a customer’s share of wallet.
Reporting is often requested ad hoc, with this highly-reactive approach often proving too slow and incapable of providing sound footing for critical decision-making. While underlying data quality needs to be validated for accuracy; strong data visualization becomes central to making data digestible for key decision makers. The latest, best of breed, data visualization tools help to address this issue by providing real time, up to the minute insight.
Unfortunately, it’s not as simple as plug and play
However, what most members of C-suites don’t realize is that it’s not as simple as purchasing a business intelligence tool, and ‘plugging it in’. While vendors often have several off-the-shelf configurations, there is a responsibility on the end user to clearly define what they want to see.
Reporting requirements are different on a client by client basis, so we need to start asking ourselves: ‘What business decisions do I need to make?’ and ‘what questions do I want to answer?’ Taking this approach assists in building up the list of requirements, so that any bespoke configurations can be implemented quickly. Any tweaks required within the back-end database architecture can also be made at an early stage.
Asking the right questions
Asking the right questions directs focus towards where the required data resides. Data is typically stored in multiple locations due to legacy environments. So, by understanding what the answer needs to be, finding the data becomes a much easier task.
Going a step further than using existing database architectures would be to integrate data sources into a ‘data warehouse’ that is optimised for reporting. Although this transformative approach demands more time and effort, it creates an easily accessible environment for users to drive the key insights they need to get ahead of the competition.
Business leaders need to stop neglecting data visualization tools as a ‘nice-to-have’ and realise they are a necessity based on the competitive advantages they offer. One reason business intelligence tooling may be perceived this way could be because it is difficult to build a clear business case around their integration. Although it is now widely accepted that improved business intelligence can lead to faster and better decisions, it is hard to measure this as a quantifiable benefit.
One way to overcome this challenge is to build a business case from an alternative perspective: ‘how much is being lost by not having business intelligence?’ Decisions that are made too late and decisions with negative outcomes typically produce lessons learnt and a cost of lost opportunity. This could be lost revenue from a client leaving a managed service relationship, or the failure to secure a sale, partly, or wholly down to having a sub-standard analytics offering.
If the value of these lost opportunities outweighs the cost of implementation, then this should provide a strong case for investing time and effort into ensuring an enterprise ready, business intelligence solution. Here are my tips for maximising your data insight through visualization
- Clearly identify what questions your business needs to answer.
- Get management buy-in – while ensuring there is an understanding that an enterprise solution is not a short-term project.
- Establish a Data Warehouse optimised for reporting purposes.
- Validate data accuracy. This can be achieved through the creation of a data catalogue, where users can tag data and report any anomalies.
- Select the ‘right’ Business Intelligence tool to maximise the data benefit, ensuring your business can become truly data-driven.
Taking the B2B fuel card sector as a use case, taking the time to plan these steps and get it right could prove the difference between a card being ‘front of wallet’, or customers reverting to competitor fuel cards. If a story can be seen developing within the business intelligence tool, such as a customer not using their fuel card as much as expected; focused marketing approaches can be pursued in order to win back the customer. Without visualization, fuel card account managers will just continue to be stuck in the headlights.
Author: Tom Saunders, Customer Data Insights Analyst at The ai Corporation (ai)