By: Lauren Kline, Vice President of Content and Technology Solutions, FactSet
With the rapid adoption of alternative data, the costs to asset managers are not just for the data itself, but also for the cost of implementing the data and hiring the talent to capitalise on it.
Financial firms have always relied on a vast array of data and technological processes to get the intelligence they need to make decisions. However, with the transition to today’s advanced technology environments occurring almost overnight, many organisations are relying on data architectures, software, and workflows that aren’t optimised to fit their needs. As a result, inefficiencies and overspending abound, while untangling the network of legacy systems and processes becomes ever more difficult as new technologies and data are added.
Alternative data adoption is transforming investment management and exacerbating the challenges of inefficient workflows. The positive impact of alternative data integration is that it can provide new insights to help evaluate securities, industries and economies. In addition, systematic and quantitative investors need to look beyond structured data into the world of unstructured data for a competitive edge. It isn’t a question of whether or not to use alternative data, but how to get the most out of it.
As firms consider which new content datasets to acquire, they must also understand how data is used across the business and how to effectively integrate it and share it across the enterprise in order to drive data aggregation and governance. Questions to consider include: which teams have access to data that can be shared with other teams; where is the optimal spot in the firm for data integration so that the data can flow to all relevant parts of the organisation; and which new alternative datasets are complimentary to existing core datasets?
Solving for these questions in advance of acquiring new datasets may reduce the operational costs associated with licensing data as well as the cost of maintaining the data.
The first task in optimising data management and integration is cataloguing all data vendors, technologies and data types, and determining how the components interact. The exchange of data between systems is the fundamental building block of any workflow.
After mapping and cataloguing existing data, look at the business objectives the data is associated with. Without aligning datasets to their objectives, firms risk introducing duplicate datasets or unused datasets. If duplicate or unused datasets are discovered, eliminating these can create cost savings that can be used towards purchasing new data or hiring new talent.
The final step is linking data silos and integrating the data. Most investment managers are taking in data – both structured and unstructured – from a variety of sources and storing it in a variety of formats and databases. Firms have both internal content assets as well as third-party data. Linking the symbology between disparate and diverse datasets is critical in order to truly realise their benefits. Firms need a content model to connect disparate sources of information by mapping disparate sources to a single entity identifier.
The people behind the data
Hiring data engineers and data scientists is a new but necessary cost for integrating alternative data. Data engineers are critical in today’s world for bringing newly purchased alternative data to a format and process that value can be derived from. Data engineers can organise, transform and link any data. Data scientists can then more easily take all of the datasets and discover alpha generating signals. They have the analytical and technical skills to add intelligence to the data.
These roles may result in new headcount spend for the organisation, but they are a critical part of alternative data adoption. There are time and resource costs for integrating new datasets, but the impacts the data can have on investment management are incredibly valuable.