This is a brief note to describe some of the challenges that insurers face with reference to data and provides some useful insight as to actions taken by companies that yielded successful outcomes.

Typical Data Challenges

Here is some description of the data challenges which I am sure will resonate with many of you:

    • siloed business systems with incompatible data sets (mainly focused on structured data) in a business that is inherently complex. there is no single version of the truth and data is difficult to access and thus it is not fully exploited.
    • There are lot of manual processes and spreadsheet work. There is a plethora of adapted and modified extracts from core systems or legacy data marts that are managed or controlled.
    • There are real challenges with data inaccuracy and quality issues. These are slow to reconcile but also provide incomplete insight.
    • Producing MI/BI reports is a cottage industry, it is highly manual and takes a long time with disproportionately large teams
    • There is a lack of effective data governance and a clear understanding of how to implement within the organisation.
    • IT is often seen as the problem – “all I need is a data warehouse”. Businesses tend to specify their requirements in terms of a technology solution.
    • No overarching clarity on a Data vision, And how to transform from an existing suboptimal state to become data and insight driven (effectively no data strategy)
    • Lack of synchronisation of interests between stakeholders, finance and underwriters understand the outcomes they need but do not necessarily know how to get there. IT build shiny capabilities but without necessarily closing the loop on exploitation. Operations want their dashboards and operational efficiencies etc. There is need for congruence of objectives which is often lacking in many organisations.
    • Data projects are conducted everywhere within the organisation but without any co-ordination on investment, capabilities being built, reusability etc. leading to duplication and higher costs.
    • Basics for data are not there – many data foundational capabilities are simply missing. Various departments have focused on piecemeal solution delivery resulting in sub-optimal outcomes.
    • Data and reporting are often deferred or deprioritised when Change projects and programmes are delivered, especially when budgets are tight and contingency had been spent. It is highly unlikely that future initiatives are initiated to fix this data deficiency thus resulting in data debt.

I think I will stop here but I am sure there are many more points that you can add to this list. Fixing the data question is not easy and needs focus and a long term plan – it is a marathon not a sprint. Data is not an IT problem and data programmes should be not technology driven.

It is important to step back and consider an exercise to define your data strategy. Make this an inclusive exercise across all areas of the organisation. This exercise needs to be driven from the top.

Commitment from the CEO and the Board is essential. But that commitment must be manifested by more than occasional high-level pronouncements; there must be an ongoing, informed conversation with top decision makers and those who lead initiatives throughout the organisation.

McKinsey Quarterly Report – Sep 2018

Here are the main steps that a Data Strategy can help you address:

    • Why data is important – consider all business-driven consumption use cases and their respective value.
    • What is the organisation’s Data Maturity (a maturity assessment can be done using a data capability reference model e.g. DCAM or Gartner. This will identify what foundational capabilities are present but not effective and which ones are missing. Define target maturity and the level of challenge and ambition.
    • Define a Data Target Architecture and TOM.
    • Define foundation capabilities needed and build a a Roadmap. Secure investment.
    • Careful considerations should be made on whether legacy ‘data’ is remediated or migrated versus alternative approaches.
    • Build DataOps practices and offer Data as a Service
    • Focus on Data culture (data is an asset)
    • Agree a data TOM that would work within your organisation

We have many examples where we have helped numerous organisations in building and executing their data strategies.

Author: Jacob AbboudChief Information Officer | Digital, Data & Agile Transformation | IT Cost Optimisation | M&A | Innovation | NED – FS (2019)