By: Douglas Greenwell, Global Head of Sales
When it comes to growing a business (especially during a pandemic), most leaders will be asking the same questions. How are our profit margins looking? What is our revenue forecast? Are we targeting the right kinds of customers? How do we improve our products and services? And how do we grow faster than the competition?
Data is clearly the key to being able to answer these questions effectively. In today’s world, as we enter the “information economy, data has become the lifeblood of any business — arguably even more important than physical assets to securing sustainable long-term growth.
Why? Because data helps you to put together an effective business strategy and make informed decisions. It enables you to plan yet be agile. To make long-term decisions and decisions in the moment with confidence.
The pandemic has only accelerated the information economy trend, with business leaders being forced to embrace home working and remote collaboration. And while many are getting on well with adjusting to home offices and remote meetings, data management is suffering because of the way businesses currently store and use their data.
The issue at hand with current data management strategies
One of the major problems with current data management is that too many organisations do too much of it manually.
Let’s look at the financial services industry for example where — because of intense regulation and the nature of the data that exists — data management is vital. One of the most important parts of financial services data management is the idea of data reconciliation — the verification of data when it moves from one system to another, identifying errors and gaps while ensuring overall data quality.
But recent research by Duco found that nearly one in five (17%) financial services organisations rely entirely on spreadsheets for data reconciliation. Nearly one in three (30%) use automated systems for specific types of reconciliations yet still admit they have manual processes in place. Just one third (31%) say they automate all their data reconciliation.
This reliance on manual processes is riddling financial services organisations with data quality and compliance complications — not to mention the amount of time and money it costs as well. Indeed, 42% struggle with poor data quality and data integrity within their organisation and 41% go as far as to say that they find data reconciliation is stressful and something they lose sleep over.
The will and struggle to change
The good news is that organisations in this sector recognise that they’re struggling.
46% of organisations want to improve their data reconciliation efforts to get ahead of the competition and reduce the risk of non-compliance with regulation and associated fines. 45% also recognise that better reconciliation would lead to better operational agility, while 40% see a significant benefit in reducing the risk of fraud.
The challenge, however, is that organisations are struggling to change.
Nearly half (44%) think that executing reconciliation without manual processes would be too difficult because of the different types and sources of data they need to handle as a company. And more than two fifths (42%) say that the benefits of data automation are not worth the risk of whatever business disruption any changes will cause.
So where do organisations go from here? How do they get out of being stuck between a rock and a hard place?
Machine learning and intelligent data automation
Automation isn’t new within financial services organisations (92% are already benefitting from machine learning in data reconciliation to a degree), so its benefits are widely known. But only 13% use machine learning for all their data.
Clearly there needs to be more than just a technical change to managing data to boost that 13% figure. One strategy that organisations are exploring is the idea of intelligent data automation (IDA). This approach accepts that data needs to live in and traverse different systems, and so a complete overhaul of data management technology isn’t necessary, reducing the risk of disruption.
What is IDA? Effectively, it’s an ecosystem of no-code, cloud-based tools that automate and control all financial, operational and commercial data across an organisation. It uses fully customisable, low-cost machine learning-based technologies that can sit alongside or on top of legacy systems, which is the key to not only successfully managing data, but to unlocking the full benefits of that data for the business.
By employing this over-arching, self-optimised level of automation, the IDA method enables businesses to get a detailed view of data across the enterprise. With this level of insight, they can better understand the performance of their operations, uncover and address weaknesses and identify new opportunities, all of which drives greater efficiency and agility across the organisation.
Nearly half (49%) of financial services organisations agree that intelligent data automation is the future, and 42% say they will investigate the use of machine learning in 2021 for the purposes of intelligent data automation.
One of the additional benefits of IDA is how it can build on existing architectural ideas such as data fabric. Data fabric is an ecosystem approach that works by enabling data to traverse disparate systems, no matter what those systems are, where they live or what kind of data they process and store. With this kind of approach, organisations do not need to replace the current systems they have invested in with a “single source of truth” for data, and instead can benefit from systems sharing information seamlessly. IDA complements data fabric by enabling non-technical users to create their own systems thanks to no-code, and benefit from the data living in separate places.
It remains to be seen whether the pandemic we are currently in will be the catalyst for change within the financial services industry, but as the internal and external pressures become greater, organisations will become compelled to make the changes necessary to ensure business can prosper.