The financial services industry needs to do a better job of harnessing business intelligence and technology, writes Cloudera’s Steve Totman.
Traditionally, financial services firms are inward-focused, struggling with multiple data silos and outdated legacy IT infrastructures.
With the availability of large volumes of data, often called big data, and the transition from in-person to online banking service models, today’s financial services institutions need to use business intelligence and technology to drive change.
Amid this era of digital disruption, financial services firms are turning towards data and analytics to gain an overall competitive advantage.
Data is now used to derive deeper consumer insights, detect fraud and drive operational efficiency, while minimising risk and maintaining compliance.
Using modern data management and analytics tools, firms can turn cost centres into profit centres, bring all their data together and provide employees with a complete, secure view of data to realise its value.
Personalisation is certainly a key challenge in the financial services sector, where customer service remains a key differentiator.
A chief information officer in financial services shared his vision with me – there was a desire to take banking back to the '70s, when the bank manager knew your name, your job, your family and why you needed a loan.
This is the level of personalisation and interaction that data and analytics can enable.
What is holding companies back from adopting big data business intelligence solutions?
Many organisations still view the adoption of big data solutions as a major systemic disruption to their business, costly and an inevitable change only to be implemented when the timing is right. This reactive approach poses risks.
Organisations, instead, should plan ahead and proactively take steps towards embracing a data-driven culture, rather than letting existing workflows dictate and drive the adoption of big data solutions.
This is a fundamental mindset shift for a lot of companies, and it can be a daunting task.
It is crucial to ensure big data analytics are set-up to provide useful and productive insights.
Businesses first need to embrace a data-driven culture, get alignment from all business leaders and stakeholders before getting started and enforce a proper data governance plan.
Big data is no longer just a competitive differentiator or cost saving approach, it is now required for survival.
Many firms that have yet to begin their big data journey will want to jump the gun and get started right away.
Our golden rule is to pick three rapid-use cases to start with, including one around cost reduction and one new data source – this can be an existing source that your organisation has yet to take advantage of due to data volumes, velocity or variety – or ideally something completely new like social media.
Once you have this new structured or unstructured source, blend it together with others (a process often called ‘data wrangling’) and you will be well on your way to leveraging big data as a big asset.
How to engage business intelligence program in a financial corporation
To build a data-driven culture, businesses must first enable data to drive success in every area of the organisation.
It is essential to get alignment from all business leaders and stakeholders before getting started and an executive sponsor is critical for such projects.
Organisations must then develop the right team, with the right set of skills, such as a team of data scientists and data engineers, that understands data and how to drive its use across the organisation.
Lastly, enforcing a proper data governance plan is key. An appropriate data governance plan provides the freedom for teams to play with data in a secure environment, but does not constrict them too much with too many workloads.
Some data sets are more important to a business intelligence plan than others, but it depends on the company’s main business objectives and the specific problems they are trying to address.
For the finance industry, it is safe to say that regulatory compliance is a very big factor, and for that to be improved, data needs to be taken out of storage silos and moved into a dynamic, highly available solution that allows SQL, search and a rich processing framework including machine learning that, in turn, enables analysts to see a broad cross-section of the company’s information.
Preventing customer churn and focusing on the customer journey as part of a digital transformation is of high importance for all financial services institutions as well, so gaining deeper insights into the full customer journey is imperative.
This can give companies a great advantage and help keep customers loyal to their brand as well.
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