“In the absence of advanced business intelligence and rich customer data, banks develop and deliver digital banking transformation initiatives in the dark.”
This was a moment of truth statement made by the CIO of a retail bank during our discussion on business transformation in the banking sector. Yet, many line of business managers shudder at the very thought of data warehousing. Blame it on unfinished, monolithic technology programmes that failed to keep up with business change and delivered poor value. These were issues faced way before the onset of the digital age with its even greater demand for responsiveness and agility.
So, how does an incumbent bank or financial services organisation burdened with the baggage of decades old legacy systems catch up with the digital disrupters of today? We offer five tips that you can apply to gain lost ground and make digital transformation a reality.
1) Develop a Mature BI Platform
A common thread noticeable amongst digital disrupters, across industries is their ability to capitalise on the wealth of information at their disposal. We are familiar with the likes of Amazon and challenger banks serving up relevant, real-time suggestions based on our current and past actions and purchases. While these suggestions are personalised, helpful and engage customers, they are also likely to trigger add-on purchases and gain a larger share of wallet.
Disrupters achieve this by deploying mature BI platforms that run on well-integrated information management architectures. Such platforms capture every possible data point on each customer, prospect or casual browser. They provide a single logical view that enables analysts to ‘joins the dots’ using sophisticated BI tools and techniques.
2) Master your Data
Arguably, a mature BI platform is much easier for disruptive new entrants to implement than it is for incumbents. Digital disrupters don’t have the baggage that typical incumbents possess – 30-year old plus core legacy systems written in now defunct programming languages such as COBOL and running on mainframes. Any change to these systems is potentially risky and expensive, as not everyone is confident of rewriting the code. Additionally, customer data is spread across multiple system silos, which pose significant challenges to the enablement of customer insight analysis.
Yet, you cannot forfeit the rich customer history residing in your aging systems, which offer even greater insights compared to what challenger banks get today. Advanced analytics rely on multiple data points; so the more history available, the greater will be the accuracy of the predictive models. In order to ensure that the data is well-defined, consistent, secure and compliant, incumbents must rely on a Master Data Management (MDM) strategy, potentially supported by a MDM system. Inevitably, each data item should have an identified owner and steward. However, implementation requires an approach that delivers speed-to-value whilst addressing compliance issues around data.
3) Achieve BI Agility
Today’s information architecture is a far cry from the slowly-evolving data warehouse monoliths of yore. New technologies and improved performance for the data gathering process necessitates that we define a logical model. This model delivers on all the valid reasons behind the data warehouse approach without being constrained into a rigid, physical component.
Good information architectures also reflect the need for two different speeds of a front-end BI solution – a quick turnaround for discovery and innovation, followed by a process to convert it into a long term business-as-usual solution. By doing this, you can deliver information governance and business agility, and enable the experts to increasingly focus on customer insight and less on data processing.
4) Create a Discovery Lab
Expert analysts at incumbent banks already innovate and support new customer-led product development, loyalty initiatives and the like. However, in the absence of an agile information architecture they lack the bandwidth or tools to gain the desired value. Customers tell us that up to 90 per cent of the analyst’s time is wasted in wrangling disparate data into shape with a mere 10 per cent left to gain customer insight. Not to mention, considering the added risks of data governance and regulatory compliance across an estate of unmanaged end-user solutions. By improving the efficiency of the data gathering process even by a mere 10 per cent, you can double the value of the productive analysis time and improve business insight.
Analysts should establish a two-speed information architecture with a formalised Discovery Lab to get ready access to structured and unstructured data. The Lab should be equipped with the latest data virtualisation tools that will reduce the hassle of mashing up the data in Excel or Access. Data virtualisation also enables a seamless transition of innovations into the production environment, thereby liberating at least 10 per cent of the wasted time.
5) Adopt an Agile Delivery Approach
An agile BI platform is just part of the solution, as it won’t get you far without an agile approach. You have to ensure that the platform continuously evolves to stay on par with innovation. However, it will need to stay true to an architecture blueprint, in order to remain manageable.
Mastek is experienced in building successful and enduring BI Solutions by adopting a ‘think big, build small’ mentality. Our advice in this area is that you ‘DO design the big-picture architecture, but DON’T try to build everything at once, as the priorities will always change.’ The method should be iterative, delivering a sequence of short cycles with rapid time to value. Businesses that adopt true Agile methodologies as part of an organisation-wide culture can really optimise the benefit of this approach, by driving towards continuous delivery (DevOps). This does not always suit the commercial models and risk aversions of big system integrators – call me a cynic, but could that be one of the real underlying reasons why so many data warehouse projects fail?
I joined Mastek as BI Solutions Director earlier this year, having spent over 30 years in the BI industry. What appealed to me about Mastek was that:
- It embraces Agile (for all projects, not just BI) and is one of the few organisations capable of executing Enterprise Agile complexity at scale
- Its project success rate measured at 93 per cent contrasts dramatically with the failure rates of 70 to 80 per cent often quoted by industry analysts for DW projects
I firmly believe that there is a strong link between the two having seen the results for myself.
Want to get an edge over today’s digital banking disrupters by capitalising on Financial Services BI? Get in touch with Mick Bull – BI Solutions Director at email@example.com to know more.