Traditional automation projects have largely been siloed in nature, because processes themselves were siloed – for instance, by asset class or in either the front, middle, or back office. Firms typically had to resort to a specialised point solution for each particular automation use case.
Data automation, by comparison, provides a solution for the end-to-end, front-to-back automation of all enterprise data (regardless of format or structure).
It enables you to conquer the ‘last mile’ to full automation, replacing a piecemeal approach with one scalable platform.
This empowers you to tackle some of your biggest challenges by standardising and consolidating your data and technology: rising costs, opaque data controls, high headcount in Operations, risk-prone manual work and a lack of business agility.
Data automation unlocks enterprise wide transparency and analytics, for greater intelligence and compliance, while delivering $millions in savings annually and powering a modern, scalable and adaptable way of doing business.
WHO IS DATA AUTOMATION FOR?
Data automation is the solution for functions that process high volumes of complex, mission-critical data.
Finance and Operations departments in capital markets, in particular, stand to benefit hugely from data automation. They rely on a significant amount of costly and slow manual work to deal with the scale and importance of their data.
WHY DO I NEED DATA AUTOMATION?
Data automation is the long-awaited solution to many familiar and sticky challenges around data in financial services
Organisational complexity and poor data quality have limited automation across capital markets. Most data processing happens outside of core systems, with people (the ‘Human APIs’) filling the gaps between systems.
On average there’s a 60/40 split between core processing and ‘off platform’ activities. All this leads to the need for 5,000-10,000 full-time employees (FTEs) across the middle and back office in large enterprises.
The data automation technology and approach empowers you to break free from this cycle of bad data and outdated systems. It enables a new, futureproof operating model built on a foundation of trusted data – one where intensive manual work is a thing of the past.
You can use data automation to cut costs and risk, increase efficiency, improve transparency and compliance, and accelerate time to market.
Financial firms have needed data automation for decades. Now, the technology is ready to make it a reality.
What are the business benefits
of data automation?
Data automation can rapidly deliver value on the level of an individual use case, or unlock huge global efficiencies across an enterprise.
Operational efficiency
A data automation platform solves the core issue slowing Operations functions down: a lack of trust in data. It does this by bringing all mission-critical data together in one place so you can standardise and consolidate processes across the business.
Cost savings
Data automation enables you to remove the legacy technology cost burdening your operations. As a result, those huge Operations teams are freed from low-value manual work and able to make a real impact.
Why is data automation transformative?
Data automation enables firms to move away from traditional operating models focussed on exceptions management.
These models don’t give organisations the flexibility and speed they need to keep pace with the demands of the modern world.
As a result, the technology, people and process demands of your organisation change. You can decommission costly legacy systems, redistribute responsibility amongst Operations, Finance and IT, remove manual work and risk, and increase business agility.
Firms can use data automation to create a data-centric operating model, where the focus is on having clean data that enables processes, rather than on repeatedly cleaning up the mess made by poor data. What this means in practice is that you take a proactive approach to data quality instead of waiting for exceptions to arise downstream and then addressing those.
By working this way, you create an organisation that has much more trust in its data. The result is far less time spent checking it – which currently often happens multiple times across different teams and functions – and the cost and inefficiencies associated with that disappear.
EXAMPLE: A DATA-CENTRIC APPROACH
Data automation enables firms to proactively manage data quality, instead of waiting for something to break and then fixing the symptoms of the issue.
A good example of this could be having weekly controls to check the accuracy of standard settlement instructions (SSIs) you hold for all your counterparties.
This happens even when you’re not currently trading with them. Ensuring they are accurate removes a major cause of downstream errors when you do trade.
Find out more about the transformative power of data automation:
Oliver Wyman: Harness data automation and AI to tackle cost
GreySpark: Automating operations on mission-critical data
HOW DOES DATA AUTOMATION IMPACT MY TEAMS?
Data automation helps to make your teams more productive and add value for the business, while at the same time improving the employee experience. Currently, capital markets firms rely on thousands, or tens of thousands, of ‘Human APIs’. They are a slow and expensive alternative to system integration, and spend their days manually extracting and entering data between systems.
Data automation removes the need for this repetitive (and boring…) manual work. Freed from manually extracting, checking and entering data every day, your teams have time to focus on more valuable tasks, such as analytics or improving the customer experience. And, because data automation helps redefine the relationship between operations and IT, staff have more autonomy.
Operations workers have the opportunity to make a difference, leveraging the latest technology to play a vital role in the efficiency of the business. Let’s face it: no one wants to spend their career manually checking or entering data.
Click below to find out more about how data automation impacts various teams and parts of your business.
Data automation enables Operations teams to solve many of the longstanding challenges that have created cost, manual work and ultimately inefficiency.
The no-code aspect of a data automation platform is very important. It puts control over the platform in the hands of the people who use it. This means Operations teams are able to perform the tasks they need around data using best practice technology, rather than trying to circumvent blockers through risky and inefficient spreadsheets or end-user developed applications (EUDAs).
When users can operate the technology they’re given, it creates an entirely different way of working. No-code removes the need for deep technical expertise to build processes. Instead it puts the power into the hands of the people with deep expertise of the business needs. They can start working on any new controls they need – or changes to existing controls, which otherwise also go through the sluggish change request process – immediately.
Something that used to take months now takes days or weeks. Operations can now use their own technology, instead of waiting on other teams to do it for them. What could be more empowering than that?
Today’s talent wants to be working with the latest technology and do work that has an impact on their organisation. Highly repetitive tasks like manual data entry or reconciling data on a spreadsheet are anything but. Data automation removes the need for these laborious, low-value tasks, enabling your teams to focus on more fulfilling work.
With data automation fully embedded in your organisation, you are able to reduce rates of employee turnover in departments that traditionally deal with a lot of data manually. You’re also able to offer more attractive opportunities for new hires to help you attract the best talent – and retain them.
REAL WORLD EXAMPLE:
VALUE-ADDING TASKS
Former data entry staff could instead work on data analytics, providing valuable business intelligence that senior management can act upon.
One Duco customer does just this – they have over a hundred offices globally and, now that they can trust their data, they are able to flag underperformance and apply insights from the top performing locations across the business.
Read more about it here.
IT teams in most financial firms spend a huge amount of their time supporting other teams. In the case of data management, this often means maintaining the hardware and systems that teams in Operations and Finance use to extract, transform, validate and publish data. These systems are often hard-coded, meaning that changes – such as a new control process or reconciliation – require a developer to build them.
This adds more to an already backlogged development queue, stretching IT resources thinner. Data automation is built on a foundation of no-code, cloud-based technology. There is no physical hardware to maintain and the business users can build processes themselves.
There are still plenty of governance and control features built in, so there’s little risk associated with putting automation in the hands of end-users. In fact, it’s a more secure and compliant way of working – teams that have to wait weeks or months in the IT queue will often resort to risky and opaque manual workarounds.
All this means the IT team is able to focus on bigger, more strategic priorities, like protecting the organisation from cyber security threats.
Can data automation work with legacy systems?
Data automation can work with legacy systems – up to a point. You can’t achieve data automation using legacy systems alone: you will need a dedicated platform designed for just such a purpose. One that uses the core technologies that enable it to succeed where legacy technology has failed. These include cloud computing, artificial intelligence and no-code functionality.
But you can begin your data automation journey by integrating a dedicated platform into your existing legacy architecture. A data automation platform, by its very nature, must be flexible. This means it is able to take data from, or feed it to, your existing systems.
Many firms start their journey this way, by using a data automation platform to plug the gaps in between other legacy systems. They automate processes typically carried out manually, such as data extraction, normalisation or reconciliation.
Once these gaps are plugged, you have a fully automated technology stack. However, reliance on multiple point solutions alongside one advanced automation platform will deny you the full benefits of end-to-end automation.
For example, true data automation relies on proactive controls to prevent bad data from breaking processes in the first place. Legacy systems can’t support this kind of operating model. Many rely on overnight batch processing, for instance, meaning you won’t discover data errors until the following working day.
Eventually, you’ll want to demise your legacy technology and consolidate most of your processes onto flexible data automation platforms. Doing so will enable you to unlock full end-to-end visibility of your processes and data, creating a streamlined, scalable and auditable control framework.
How do you measure data automation success?
Data automation promises many benefits – therefore there are many ways that you can measure its success. It all depends on where you start and what your initial aims are.
Let’s take as an example the automation of unstructured data extraction or a particular use case, like broker statements. You can first measure how much time it takes a human to manually process each document. Then, you can compare how much time they spend intervening to check a decision the AI model has made.
If you are automating a reconciliation process, you can measure success in terms of match rate improvements, or time to resolution for your exceptions.
Because of the broad nature of data automation, there are many facets you can measure:
- Annualised cost savings on the legacy on-premise systems you have demised after automating your data on a flexible platform.
- Time to close audit points or meet your SLAs with the front office.
- Speed of onboarding a new business.
- How much your volume has grown without the need for additional Operations headcount.
In other words, you can measure data automation based on your particular goals. Once you have a strategy in place, you’ll be able to set the relevant KPIs to track your success.
How do you integrate structured and unstructured data with automation tools?
The complexity of dealing with financial data is the prime driver of complexity in your operating model and technology stack.
Even structured data can cause problems. Just because the schema defines where the ‘date’ information is doesn’t mean that every firm is using the same conventions for writing dates. These small differences confound legacy technology, creating false exceptions (in other words, breaks in the data that aren’t due to a genuine error).
It’s no surprise, then, that unstructured data poses even more of a challenge. Unstructured data is any data that doesn’t have a defined schema specifying what data is included and where it should be located.
Examples include PDF broker statements which, while they may contain the same information from firm to firm, will vary in terms of how that information is presented. Another example is email instructions from a client.
How could legacy technology, that struggles with even small changes in the formats it is designed for, ever process data like this?
The key to handling various structured and unstructured data formats is to combine the right technology. Data automation platforms are schema-free by design. The system isn’t built to accept data in a specific format; it’s built to accept data and then work out what format it is in. This means you can automate the ingestion of CSVs, text files, SWIFT messages, PDFs and more, ready to transform and feed to downstream processes.
Traditional solutions for extracting unstructured data have had the same problem. Optical character recognition (OCR) requires ‘pointing’ at the right data; if the format changed so that the data moved, these tools can’t adapt.
Even the first Intelligent Document Processing (IDP) tools, which harnessed artificial intelligence, had a similar problem. Their models only worked on a specific document type so, while they were advanced technology, they were still only point solutions.
But by using an adaptive IDP (AIDP) solution, you can overcome these challenges. Adaptive models are able to generalise their knowledge and react to changes in formats or business requirements.
By combining the elements outlined above, a data automation platform handles all kinds of structured and unstructured data. This makes it possible to automate your processes end-to-end.
IS DATA AUTOMATION THE SAME AS HYPER AUTOMATION?
Data automation can be thought of as a component of hyper automation. Hyper automation involves using multiple automation platforms to remove all manual work from across the business.
A data automation platform does this for Operations and Finance departments, so when combined with other enterprise-grade solutions across the business would enable a hyper automation strategy.
IS DATA AUTOMATION THE SAME AS BUSINESS PROCESS AUTOMATION?
Data automation and business process automation (BPA) aren’t the same thing, but data automation could be considered a type of BPA.
BPA is more broad reaching and, while definitions vary, it often focusses on automating all processes that aren’t run by IT that keep the business running (everything from KYC to employee onboarding). Data automation focusses on tackling the complexity and operational inefficiency holding capital markets firms back in their Operations and Finance departments, regardless of whether the processes in question are owned by IT.
Like hyper automation, business process automation relies on multiple automation platforms to achieve its aim. Therefore, a data automation platform would be a valuable part of a BPA strategy.