How utilising embedded analytics tools can unlock the power of data-driven decision making

Data-driven decision making is imperative to any organisation looking to gain a competitive advantage.

However, many financial institutions face a myriad of obstacles when trying to extract value from their data.

“Most financial services firms are overwhelmed with data in both the number of sources and the complexity in unifying the data into formats that are usable by the business,” says Carmen Logue, product manager at InterSystems.

“Overly complex data infrastructures that rely on a disjointed set of technologies for data management, semantic layers, data integration, and analytics leave businesses struggling to obtain data fast enough, and in a way that is easy to interpret and share to drive their organisation forward.”

Bridging the gap between data silos for a consistent enterprise overview

For the majority of organisations, significant data silos across the business can lead to problems if the data is delayed and inconsistent – resulting in decisions that are neither timely nor accurate.

According to a recent study from Enterprise Strategy Group Research, a staggering 93 percent of organisations revealed that the majority of their employees don’t have access to data analytics tools.

So, what’s the solution? Bridging the gap between data silos and embedded analytics is one. Data fabric with both embedded analytics and self-service business intelligence (BI) can be an extremely powerful tool for businesses to derive the most value and to impact the most change.

The use of strategic embedded analytics and self-service BI in combination with a data fabric can allow organisations to visualise and explore data more freely and empower employees and customers with accurate information.

Adopting a data-driven architectural approach

Another vital step to benefit from a data and analytics platform is to start prioritising data within an organisation to obtain the full value of actionable analytics.

“Using embedded analytics to gain a more current, comprehensive, and detailed understanding of their business is essential for financial institutions,” says Logue.

She adds: “Ultimately, adopting embedded analytics capabilities enables line of business users  to ask sophisticated questions and obtain the answers they need to make actionable business decisions using current and trusted data in the moment, without relying on IT.

”Implementing a smart data fabric  will also help to remove silos and help organisations to gain a common semantic view of the data, even if that data remains distributed.

While they are likely to be faced with a large number of silos, prioritising key metrics and iteratively connecting data sources will allow companies to reduce redundant data and provide a common language across data sources.

Utilising embedded analytics can also open the door to additional revenue streams, enabling frontline employees to have the right data at their fingertips when approving loans, making decisions about deployment of assets in relation to sudden changes in interest rates or in capital and commodity markets.

Figure out what needs to be measured using automation

Understanding what needs to be measured is a great starting point and will allow businesses to work backwards, as well as permit their IT teams, who will be undertaking the implementation process, to understand what data and insights they need to provide answers to the questions those leading the business require.

Once organisations have started to take incremental steps to unify their data, the next step is to understand where the crucial business problems lie and the next questions that need to be answered.

Utilising the benefits of artificial intelligence and machine learning is another vital step for businesses to overcome the difficulties faced by legacy infrastructure that may hinder any potential progress to map out the true potential of data.

“While there continues to be a lot of interest and hype around AI, many business leaders don’t know to which business challenges they should apply it to get the most business value, says Logue.

“There are, however, many potential applications – including customer personalisation, risk, and wealth management – but deciding where to focus is often the pressing challenge. Our advice would be to identify a particular business need that would benefit from the use of AI or ML, and start there, hone processes, and quantify the business value gained. This will allow businesses to understand the immediate impact of these initiatives, learn and iterate, before tackling additional use cases.”

Reporting and risk management processes will also become faster and more efficient. Employees can gain quick access to profit and loss data they need on an as-needed basis, or to the customer activity they rely on for customer support, cross-selling, and upselling opportunities.

Collaboration key to success

While changes may likely be driven by IT teams, implementing analytics platforms isn’t just an IT initiative. Instead, it requires collaboration from individuals across the organisation.

To guarantee success, it’s imperative for different teams to work together iteratively and constantly assess the contributions being made by the introduction of analytics platforms and continue to refine the use cases and required metrics to understand whether they are providing value and what changes might be needed to measure progress.

“Taking this approach will help to iron out any issues as they occur and ensure that all users are extracting real value from the platform,” adds Logue.

Simplifying the complex

For most businesses, obtaining a single source of truth from which they can gain insights can be extremely complex.

Not only do organisations tend to have a large number of data silos, but they already have a range of different technologies in place, from data warehouses, data lakes and data marts, to integration platforms and BI tools.

As such, the majority are ideally looking to simplify their technology infrastructure, but without having to rip and replace.

“When self-service analytics capabilities are embedded into operations, it creates significant value for businesses by making it much easier to access the data to understand what’s happening in the business and to predict what may be likely to occur,” says Logue.

“Smart data fabrics make this possible, helping businesses to unlock the true potential of their data by speeding up and simplifying access to data assets across the entire business. This is all while allowing existing legacy applications and data to remain in place, to enable organisations to maximize the value from their previous technology investments.”

Taking on new challenges

As we look ahead, the accelerated move to online and self-service brought on by the pandemic is another trend that we will continue to see play out in different ways.

“We will see even more digitisation among the more traditional financial institutions, and perhaps greater collaboration between traditional financial institutions and fintechs as they try to adapt the ways in which they operate to reflect the changing needs of both consumers and businesses,” says Logue.

With access to more comprehensive, accurate, and timely information, employees across businesses will also be better placed to make informed decisions and measure the success of new initiatives needed to drive their organisation forward.

Organisations can, for example, embed high-performance, dynamic, interactive dashboards in transactional applications to give users real-time insights at the point of action.

“This will hand greater power and productivity to businesses and help create a data-first mindset in a much wider range of roles within financial services organisations, giving them the tools needed to make informed, intelligent business decisions easing the reliance on IT teams,” says Logue.

Read the full survey report ‘Embracing embedded analytics and a comprehensive data analytics platform’

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• Automated Underwriting Systems