In the context of business intelligence (BI) and digital banking solutions, there are numerous ways to characterize the process of translating raw data into actionable insights via the use of business intelligence (BI) applications, infrastructure, and tools. The most optimal method of going about things An effective business intelligence system delivers data in an accessible and user-friendly way, allowing for the formulation of strategic business decisions.
Organizations in the financial services (FS) industry must conduct a more comprehensive analysis of their digital banking platform in order to make the best decisions possible. Only with the most precise information can they manage their businesses more efficiently, better understand their customers, and outperform their competitors.
Some firms have been hesitant to implement business intelligence approaches, such as Oracle digital banking experience (OBDX), despite the fact that the demand for data literacy has increased in the recent years. “A third of Global 2000 firms would have formal data literacy improvement initiatives in place by 2022 to provide insights at scale, build sustainable trusted relationships, and combat misinformation,” according to a poll conducted by IDC (paraphrasing). Data literacy is required for the implementation, use, and management of business intelligence (BI) systems, as well as for the awareness of such systems. In order to get the most out of business intelligence solutions, at least in the near term, basic data literacy is essential.
Banking and financial institutions will need to implement effective business intelligence procedures in order to stay on top of the competition. These processes will help them get deeper and more meaningful insights into their customers’ behaviors and transactions. A number of critical barriers will need to be overcome before they can achieve their goal.
Data quality
The lack of high-quality data might be a significant roadblock. Unfortunately, the strength of a business intelligence solution is only as powerful as the quality of its source data: if the quality of the data is bad, the output will be poor as well. It is also critical to ensure that efficient data management practices have been implemented in order to maximize the efficacy of BI outputs. As a first step, organizations should consider doing a gap analysis and a maturity evaluation of their current infrastructure in order to determine the quality of their source data.
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Implementation costs
If appropriately implemented, business intelligence (BI) technologies can provide a significant return on investment, despite the fact that there will be an initial outlay. The potential for business growth, as well as the promise of improved internal processes, are two factors that should not be overlooked when determining whether to invest in BI technologies. As a result, organizations should make an effort to assess the current health of their business intelligence infrastructure in order to properly prioritize projects and identify workstreams where BI would provide the most return on investment. As a result, increasing the value-add of the BI solution should help to eliminate obstacles to future investment and gain buy-in from top stakeholders.
Execution complexity
It is critical to have well-defined and organization-specific business intelligence needs in order to avoid the deployment of a BI solution becoming unduly complex or challenging to implement. Costs and duration will inevitably vary depending on the size of the organization and the infrastructure that supports it. Still, businesses may avoid spiraling costs and extended delays by keeping procedures basic and carefully planning their implementation. In order to be able to evaluate success in a BI implementation properly, it is vital to identify success criteria in the early phases of a BI implementation, ranging from complex artificial intelligence and machine learning to basic management information and data visualization enhancement.
Adoption
Even if an organization successfully produces a functional model, it may still fail if the model is not adopted by its intended users or if it is not correctly integrated into current technological and business processes and procedures. While technical integration issues are often encountered, user acceptance is a considerably more significant cause of project failure in business intelligence (BI). Disparate standards within an organization, as well as a reluctance to abandon old methods of working, may result in disconnected business intelligence processes. A well-defined business preparation plan and implementation strategy, which includes training as well as more informal support networks, may assist in alleviating the stress associated with implementing a new way of working. In conjunction with an appropriate overall business intelligence strategy, addressing these issues will increase user adoption while also improving general data literacy.
As the amount of data that financial services organizations have at their disposal grows, so does the need for helpful business intelligence insights. Despite the fact that there are unquestionably obstacles to implementing best practice BI, each of them can be addressed. This is encouraging news for financial services organizations, which are under pressure to strike a balance between deployment costs and the effectiveness of their business intelligence solution.
