Insights from: Greg Glass (Head of Revenue @ Aiviq)
What is Advanced Analytics?
Aiviq defines Advanced Analytics as an enterprise capability to analyse large volumes of data, uncover hidden patterns, and provide actionable insights.
The most recent advances in advanced analytics have been the application of Artificial Intelligence (AI) and machine learning (ML) to process larger volumes of data and create more sophisticated outputs including sentiment analysis, more accurate predictive analytics and entirely new consent using generative AI.
However, despite the powerful technologies that have emerged, many asset managers still find it difficult to realise value from investments in building advanced analytics capabilities.
Barriers in Adopting Advanced Analytics in Asset Management
Three major challenges need to be solved alongside the implementation of any 'next gen' advanced analytics technology:
1. Poor Data Quality
GIGO (Garbage in, garbage out) is an iron law of any analytics endeavour but it is surprising how often it is overlooked or assumed that the technology on its own will magically solve bad data. Establishing data quality is like cleaning up a murky swimming pool - it requires a big initial intervention to get the system on the right path followed by a strict regime to maintain quality.
Beyond GIGO there is a new risk in the world of AI-based analytics: plausible hallucinations in the outputs of AI enhanced analytics. These hallucinations are multiplied by bad data quality but are much harder to identify and therefore more dangerous.
2. Business Change
... or more accurately, a lack of business change, to inform, educate and motivate data consumers and systems users to accept of new data, new ways of working, even if they perceive that adopting it may encroach on their job security.
Data runs through an organisation like a river, ignoring functional boundaries. So any business change has to be Enterprise wide - which is the hardest of any change to effect.
3. Lack of Sponsorship
Data transformations - including changes in the analytic tools and outputs used to surface insight and analysis to users- are massive endeavours. What looks like shiny, powerful tools are often just the iceberg above the waterline.
The real work below the waterline is the communication and organisational support to provide users with confidence and conviction in the new data, new tools and new ways of working. This starts with personal sponsorship from the leader in the firm.
Considering the Soft Factors
The tools and technologies to power advanced analytics are available today. However, soft factors like business change, data quality and executive sponsorship are essential to creating a valuable advanced analytics capability.
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