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The Analytics Ecosystem: Capturing the Value of Customer Data and Analytics

image showing Consumer Consent in the Insurance Industry

Data and analytics capabilities have made a giant leap forward in the last few years. The volume of available data has increased, algorithms have become more sophisticated, and computational power and storage have steadily improved. The convergence of these trends is fuelling rapid technology advances and business disruptions.

Yet, as a recent Aite Group study commissioned by TransUnion shows, most financial institutions are capturing only a fraction of the potential value from their data and analytics. We look at some of the barriers to building an effective analytics ecosystem and how these can be overcome.

The analytics advantage

Data and analytics are changing the basis of competition, enabling companies to both improve their core operations and launch entirely new business models.

Many financial institutions are assessing their current solutions with a view to building an analytics ecosystem that will be a competitive differentiator.

If they’re able to integrate new data and new analytics techniques, such as artificial intelligence and machine learning, into their analytics process, they’ll likely be able to react more quickly to market changes.

Potential unrealized

The Aite study looked at analytics in the financial services industries in multiple countries, focusing in on three aspects of orchestrating the analytics process across the enterprise:

  • Existing systems and processes
  • Data
  • Operational effectiveness

What the study found was that disparate systems, the lack of a single platform and talent were the biggest barriers to extracting value from data and analytics.

Here are some of the high-level findings.

  • Existing systems and processes

    60% of financial institutions surveyed use a hybrid approach of building and buying their solutions
    Analytics systems, models and processes tend to be driven by role, so it’s nearly impossible to get a single view of a consumer and incorporate data-driven insights into day-to-day business processes. And in many organizations, so much time is spent on data cleansing and preparation, there’s less opportunity to focus on value-added activities such as model development.

  • Data consolidation and integration

    Top three data sources used to understand significant life events:
    • Transactional/bank account data (45%)
    • Traditional credit scores (41%)
    • Property data (37%)

    Financial companies hold vast amounts of their own consumer data, but it’s often organized by product rather than consumer, so it’s not easy to get an integrated view of a consumer.

    Access to third-party data can provide additional insight into consumer needs but many struggle to both consolidate internal data and integrate third-party data. As the number of data sources and types keeps growing, it becomes more difficult to find insights quickly.

  • Operational effectiveness

    Just over half the respondents strongly or mostly agreed they would like to extract insights to make business decisions quickly.

    Many organizations want to add new data sources and analytics models, and implement emerging technologies such as AI to optimize the analytics process — but few have the common enterprise platform to do so.

The net result is that many financial institutions have limited insight into how their customers use their products and services, and a narrow understanding of the financial impact of other activities consumers engage in.

Narrowing the gaps

Only 5% of respondents said it’s not challenging to find qualified data scientists.

Extracting powerful insights from big data requires the right talent as well as the right approach.

There’s a great demand for data scientists and business translators who can combine data savvy with industry and functional expertise — but attracting and retaining this talent is easier said than done.

This is where partnering with data and analytics experts can be a powerful differentiator.

Data and analytics experts can help with transformation in some key areas. Firstly, by providing additional, up-to-date data that will give you a more holistic view of consumers.

And secondly, by helping to integrate that with your internal data sources, enabling you to draw meaningful insights that can inform your risk and marketing strategies. Finally, their investment in some of the latest analytical tools and technologies can help you extract those insights more quickly.

You already have the data — now’s the time to put it to work for your organization and your customers.

TransUnion’s online and offline data assets and industry expertise enables us to help organizations narrow some of their gaps in data, analytics models and skills and help create more effective analytics ecosystems.

1All references throughout this article come from the following source: Aite Group Study commissioned by TransUnion, LLC: Current State Assessment: Global Analytics Ecosystem (October 2019)

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