How to Overcome the Barriers to Best Practice BI
Business Intelligence (BI) refers to the practice of using applications, infrastructure and tools to convert raw data into actionable insights. Best practice BI solutions present data in a straightforward, user-friendly way so that it can be used effectively to inform strategic business decisions.
As financial services (FS) organisations collect an ever-increasing volume of information, they are realising the need to think more carefully about how they can make the most of their data: only with the best insights can they make the decisions required to operate the most efficiently, understand their customers the best, and outperform their competitors.
Although many organisations have been slow to embrace BI practices, there has been a notable increase in the desire to improve their data literacy. As a study published by market intelligence firm IDC observed, “By 2022, a third of Global 2000 companies will have formal data literacy improvement initiatives in place to drive insights at scale, create sustainable trusted relationships, and counter misinformation”. Data literacy is one of the key factors in both understanding and being able to effectively implement, use and manage BI solutions. Without a basic level of data literacy, the benefits of BI solutions at the very least become more difficult to realise.
While an increased focus on improving data literacy and an influx of information opens up the opportunity to gain deeper and more valuable insights, banks and financial institutions will need to implement effective BI practices in order to ensure that they stay ahead of the curve – and keep up with the competition. To do so, they will need to overcome several key barriers.
Data quality can be a difficult barrier to overcome. Unfortunately, a BI solution is only as strong as its source data: if the quality of the data is poor, so too will be the output. In order to maximise the effectiveness of BI outputs, it is therefore important to ensure that effective data management strategies have been embedded. As a starting point, organisations should consider carrying out a gap analysis and a maturity assessment of their existing infrastructure to evaluate the quality of their source data.
BI should be one of the easiest IT expenses to justify: although there may be an initial outlay, the opportunity for business growth and the promise of optimised internal processes once BI technologies have been effectively deployed yield a potential return on investment that shouldn’t be ignored. Organisations should therefore make efforts to understand the current state of their BI infrastructure in order to effectively prioritise initiatives and identify workstreams where BI would maximise returns. Increasing the BI solution’s value-add should, in turn, remove barriers to future investment and encourage senior stakeholder buy-in.
Well-defined and organisation-specific BI requirements are essential to ensuring that the implementation of a BI solution doesn’t become overly complicated or tricky to execute. Costs and timelines will naturally differ depending on the size of an organisation and the infrastructure that underpins it, but when methods are kept simple and execution is carefully thought through, firms can avoid spiralling costs and protracted timelines. BI solutions can vary from advanced AI and Machine Learning through to simpler Management Information and data visualisation improvement; as such, it is also important to outline success criteria in the early stages of a BI implementation in order to be able to effectively quantify success.
Even if an organisation successfully delivers a working model, it may still fail if its intended users do not adopt it, or if it is not well integrated into existing technical and business processes. Whilst technology integration commonly poses problems, user adoption is a much greater cause of failure for BI projects. Differing standards across an organisation or a reluctance to move away from old ways of working can lead to disjointed BI practices. A well-defined business readiness plan and implementation strategy, incorporating training alongside more informal support networks, can help to mitigate against the strain of adopting a new way of working. Addressing these challenges, combined with the right overarching BI strategy, will drive user adoption — as well as improve general data literacy.
As the volume of data that FS organisations have at their disposal increases, so too is the demand for valuable BI insights. Although there are undoubtedly barriers to adopting best practice BI, each of these can be overcome. This is good news for FS organisations, who face the pressure of balancing implementation costs with an effective BI solution.
The case for effective BI, however, is clear: the more data that organisations attempt to process, and the more they persist with legacy systems and maladapted operational processes to do so, the more difficult and costly BI initiatives will become. FS organisations that do not respond to this challenge risk being left with an inefficient operating model that cannot adapt to change and is ultimately outpaced by their competitors. Those that do embrace best practice BI infrastructure, however, will reap the rewards that dynamic and actionable insights will bring to their short- and long-term performance and business growth.