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Data automation technology and principles can be deployed gradually and expanded in a realistic manner. The combination of software-as-a-service, cloud-based and no-code technology makes it possible to target a particular use case and quickly realise value.

That’s not to say you can’t have big, transformational goals, but simply that data automation can take a realistic and phased approach, de-risking the change.

Getting started

Data automation can be transformational, but one of the advantages of the new technology is that it’s not an all or nothing project. Indeed, many firms using data automation on an enterprise level started with one specific goal or use case in mind.


The first thing you need to do is turn those big promises into phases and deliverables. This will allow you to prove value and expand the project in increments, while all the time remaining measurable and accountable.


Communication is key here. Unlocking full data automation will require the buy in and continued support of numerous stakeholders across functions. You need to clearly explain what you are going to do and when –and then make sure to keep everyone updated.


Build on your successes. Once the initial deployment is successful and has delivered value for the business, you can use this to drive excitement and budget for the next phase. It is by doing this that you keep everyone engaged and bought into what you are doing.

In terms of where to start, firms usually begin with one
of two approaches: standardisation or consolidation.



Standardisation involves automating all complex, non-standard data that flows through your organisation. It’s data like derivatives data that causes the biggest problems for traditional systems, and creates the need for a spreadsheet or other end-user workaround. As we’ve said, this can be done on a use case by use case basis.

For instance, you may want to use a data automation platform to automate all of your spreadsheet-based derivatives reconciliations. A data automation platform works differently to traditional hard-coded legacy systems, point solutions, or managed services. You’re able to easily extract, transform and load your derivatives data onto the platform and then build automated reconciliations for it.



The average financial services firm has a complex technology landscape, with different systems handling different data tasks. Over time this becomes ever-more complex, with more systems – and therefore cost – added.

Even when data is standardised, firms still often struggle to manage it, especially in high volumes, like cash trades data. Here, different teams often need to use the same data and so have their own separate systems for transformation and validation. This disparate network of technology is costly, opaque and difficult to govern.

Consolidating systems used to be impossible in an on-premise, legacy world because of issues like access for global teams, or the fact that systems weren’t flexible enough to handle the needs of multiple teams. Data automation technology is enabling firms to bring disparate processes together on one platform. This enhances governance, reduces duplicate work and simplifies their technology landscape.

Every bank is different, and your data automation starting point and journey will be unique. Talk to an expert to discover the cost savings and efficiency gains of data automation for your firm.

Who’s responsible for delivering data automation?

In order to unlock the full benefits of data automation, firms need to think cross-functionally. Unlike a reconciliation process owned by Operations, data automation impacts teams across the organisation. This affects everyone from end-users to budget holders.

So, while it is possible to start in a single function on a single use case, it’s not possible to unlock the enterprise level benefits of data automation (i.e. where the real gains, efficiencies and savings lie) without a full transformation.

For that, the responsibility has to sit with senior stakeholders, who can assemble a data automation task force that has the vision and the authority to bridge those gulfs between departments. Indeed, according to one study, data transformation projects don’t succeed (i.e. deliver value within 18 months) if the burden of delivery is placed on IT alone, with no engagement from the wider business. Business engagement, it found, had a binary correlation with the success of the programme.

In other words, if you want it to be successful, the answer to the question of who owns data automation should be: everyone.

Watch: Santander COO discusses data-centric transformation

Santander’s Chief Operating Officer José Muñoz joined Duco founder Christian Nentwich to discuss the importance of focussing on data rather than technology, the need to prioritise transformation and how to build a truly global operating model.

Scaling across the enterprise

You’ve implemented data automation focussed on one problem in one team or function and delivered a return on your investment. But what now? Scaling across your enterprise brings the biggest returns and unlocks the greatest efficiencies, but how do you realise this data automation vision?

68% of data transformation projects fail to realise value. How do you ensure that yours is one of the success stories?


Let the business be your guide

Make sure that you continue to target areas for automation where the results align with the goals of the wider business. For instance, is the business focussed on cost reduction, risk reduction, eliminating manual work, or improving your employee experience? Data automation can address all of these, but the priority for your organisation will help to guide where you should focus your automation efforts next.


Champion your successes

Scaling data automation requires you to champion it. Highlight the value that you have already delivered for the business; it’s often the case that other teams, upon hearing what’s been possible, will want to explore the potential themselves. But in order to know about it, you need to tell them in the first place.


Educate and evangelise

Also, don’t forget the importance of expertise – as automation grows across your business, you’ll need more people who understand the platform and how to get the most from it. Ensure that your initial users are experts on the solution (Duco, for instance, offers learning resources including the Academy and Certification) so that they can then evangelise to other teams about the software.


Revisit existing automation

Remember that automation is not a ‘set it and forget it’ exercise. Automation should be iterated on and optimised. This is particularly true where AI and ML are involved, as your models can continuously improve to deliver increasingly accurate results. Scaling doesn’t mean forgetting about the automation work you’ve already done. There are always ways to advance your automation, for example by strengthening the governance around it or establishing best practices.

Transforming the business

Some of the world’s leading financial institutions are harnessing data automation as part of their leading modernisation and cost-savings programmes.

Data automation and transformation go hand-in-hand: the full value of data automation is unlocked by firms that want to elevate their operations for maximum efficiency and agility. It also invites you to think differently by challenging long-standing assumptions, created by decades of working with legacy technology, about how data should be managed.

Data automation enables you to think data-centrically, understanding the way data drives processes and ensuring that the data you need to operate is readily available, up-to-date and trustworthy. This is a significant shift away from how many financial institutions operate now, which involves planning and resourcing for the consequences of bad data.

While transformation projects are clearly about the future state, it’s vital for your success that you invest the time necessary to understand your current state first.

This not only means mapping out your current processes, but also the resources and cost associated with each of them. Then it’s time to identify your Target Operating Model, based upon what you have learned about your business in the discovery stages and your strategic priorities.

The next step is key to ensuring that your transformation project is successfully delivered. It’s time to connect the current state to the future state, creating a path of measurable, deliverable objectives that will lead you from the former to the latter. Breaking this journey down into concrete phases makes it much easier to deliver and track progress.

Watch: Securrency and Mizuho on delivering successful transformation

Replacing legacy technology to achieve data automation

Data automation enables firms to do in one platform what they haven’t even been able to achieve with multiple legacy platforms. You can consolidate and standardise your processes, unlock greater transparency and share best practices across teams and geographies.

While the agility of data automation platforms enables them to integrate into your existing tech stack, the ultimate goal for any organisation wanting to cut costs and risk, ditch manual work and increase operational efficiency is to remove legacy systems entirely.

The pitfalls of on-premise

You need to ditch the legacy technology in order to fully transform your business and realise the true potential of data automation. These systems are costly, inflexible and generate enormous operational inefficiency and people cost. It’s time to change your legacy.

The Human API

Your operations are full of people plugging the ‘automation gaps’ between legacy point solutions. We call them ‘Human APIs’, because they exist to create connections where technology has failed – usually through manual work.

Data automation vs Excel

Excel is a powerful business tool, but it was never designed to cope with the kind of tasks many capital markets Operations and Finance teams use it for. Spreadsheet-based processes often start life as a ‘quick fix’, but end up staying for the long haul.

Data automation vs EUDAs

Operations and Finance teams will often come up with their own solutions when strict change control policies block them from completing vital work. End user-developed applications (EUDAs) abound in most firms and, while they may keep the lights on, they come at a significant cost.

Rethinking change management

Teams usually have to go through a lengthy and complex process to change existing processes or get new ones developed. It all starts with the business requirements document (BRD) and it’s supposed to ensure governance and control. However, the reality is often the opposite.