The financial industry has always been information centric. We collect volumes of data every day and have been told for years that “big data is the new oil.” But, few firms really know how to manage their data, let alone how to use it to increase their firm’s profitability. The vision of information flowing through an organisation, seamlessly feeding downstream systems and being harnessed for business intelligence and automation still seems a distant future to them.
But, it is not. This vision is a reality today. Unfortunately, many firms have heard the siren call of implementing data lakes or data marts only to realise they were the wrong approach. The good news is there is still time to turn away from the dangerous and costly shoals they are heading towards.
Beginning with data lakes — why are they costly and wrong? The main problem with them is that data in a data lake has no structure. For example, imagine you are building a client portal and an application to measure internal performance analytics. These require roughly the same information: holdings, transactions, accounts, security master and so on.
Financial data: an untapped resource
A data lake doesn’t give you the information to power this in an easy way. It is meaningless unless specific programs are written for each and every downstream application wishing to consume it, bit by bit. Now, let’s add CRM, reporting, goals-based financial planning, mobility, billing – in a data lake, information will not seamlessly flow to downstream systems. This data also cannot be harnessed for predictive analytics and behavioural science algorithms without costly custom programming for each and every change.
As an alternative to data lakes, firms wishing to achieve operational efficiencies have looked to implementing data marts as a solution. The original data warehouses used “data marts” which would place information in separate containers. The benefit was improved reporting over the data lake, but as everything has been siloed away with this approach, navigation between different data sets is difficult. This makes interpretation difficult.
In response, data warehouses evolved — improving navigation and business intelligence by putting data sets into smaller segments. But the user didn’t organise the data — the developer did. This resulted in data filed in ways that were not intuitive, or useful, to the end user.
Today – we’re at a new point in our data evolution. Welcome to the era of the digital warehouse.
A digital warehouse, especially in the asset management industry, is more than a database with extract, transform and load processes. It considers data coming in from different sources, in different formats, and at different times — handling it all seamlessly. The digital warehouse takes the best of what came before and adds openness and non-traditional data points such as media, documents, text, video held away and PE assets. All the information you need is all in one place.
This creates a single version of the truth – the ability to derive all information from one source. We all know that better information powers better decision-making — but this can only be achieved when you get the full picture.
The future of finance will rely on data science. The search for talent is on
The greatest achievement of the digital warehouse is that it transforms the financial industry into a data model. A difficult task to begin with – it was previously not possible for the older, less sophisticated data warehouses. This allows for the automation of the entire client engagement life cycle: from digital on-boarding where the collection of client data, including know-your-customer and anti-money laundering takes place, to all the information gathered through goals-based financial planning apps. It is here where any downstream application or predictive algorithm can seamlessly plug in. This is where financial firms reap the most gains in automation, allowing you to service more clients effectively.
This also effectively means that financial firms can finally ‘monetise’ and derive value from their data.
The decision to move from silo-based data management due to disparate processing engines, to a centralised digital warehouse approach is more than eliminating data redundancies, it’s about reducing costs and allowing your staff to do more with less. It reflects the awareness of the tremendous value in data and the synergies of combining and comparing data sets from different sources.
Data is the engine behind the modern financial firm. But it means nothing if you cannot derive information from it. And that means your data management system needs to provide insight, not just storage.
