Why in-house data solutions miss the mark: Eric Giroux on the importance of analytics expertise

Traditional insurers, MGAs and intermediaries may be drawn to the idea of developing their own in-house insurance analytics platforms, thinking they will gain greater control over data and benefit from tailored solutions and cost savings. However, in reality, a DIY approach to insurance analytics appears to be far more complex and costly than many businesses anticipate.

Insurers and MGAs may believe that DIY data solutions are a silver bullet that can quell their data struggles, offering an important capability and a competitive advantage. However, due to the numerous unexpected challenges associated with building an effective data model, the hidden costs can quickly add up, often negating any initial savings.?

Advanced analytics and data science can bring transformational insights to an MGA business. However, when investing in analytics, research has shown that the real competitive advantage appears to be in the application of analytical insights to make better underwriting or commercial decisions, and not in building and maintaining the underlying physical data architecture required to produce those insights.?

Having the right strategy

Many insurers and intermediaries lack a standardised approach to data governance, resulting in inconsistent practices for data use and storage.

Without a clear strategy to manage this influx of information, insurers risk missing out on valuable insights that could enhance their operations and decision-making processes.

The truth is the process of capturing, organising and analysing data requires far more expertise than businesses may realise. “When you allocate a lot of resources, even your IT staff, to granulating data – putting together messy data, cleaning it up, and organising it into a usable format – this requires a substantial amount of time and resources. While the analysis of that data adds high value to the business, the effort required to get it into good shape can be overwhelming,” said Giroux.“?Companies often underestimate how much work is involved and how much knowledge is required.”???? ?

Enter Giroux

Giroux.ai is a London-based company that is revolutionising the insurance sector by making data and reporting more accessible and manageable. Its innovative platform collects and manages data from disparate sources and consolidates it within an OLAP (Online Analytical Processing Layer); a semantic layer designed to help users develop multidimensional data models for business intelligence and analytics.?

This technology structures vast amounts of data into multidimensional formats, enabling efficient data analysis and insight generation.

Through this technology, Giroux.ai can consolidate and organise large volumes of data from various sources into a structured, multidimensional format.?

This frees up employees’ time to easily use valuable insights from the data to make better underwriting and commercial decisions, instead of spending time making sense of the data itself.

By using an OLAP layer, Giroux.ai can "granulate" the data – meaning it refines and prepares it for analysis – so that employees can efficiently interrogate the data, ensuring accurate, reproducible, and actionable analytics.?

This approach allows clients to gain deep analytical insights without needing to heavily invest in their own data management capabilities – and deplete time and resources in the process.

Exposing inefficiencies ?

Accenture found that data analysts in large insurers spend as much as 80% of their time searching for, cleaning and preparing data, leaving only 20% for actual analysis. This inefficiency is impractical and costly.

Giroux explains that: "The real power lies in employees being able to interrogate the data and produce reproducible, high-quality analytic results. Our strength is in our ability to provide top-tier talent such as specialists in IT, architects, economists, and insurance experts to support internal teams of underwriters, actuaries and insurance personnel with the specialist skills they need alongside the platform we provide. We brought everything together to empower employees to do more."

For many MGAs, doing analytics internally may prevent them from having the budget to then spend on a good underwriter to utilise the insights. This comes with significant business risks. “If I were in charge of an MGA or insurer, I would focus on recruiting insurance-related talent such as excellent underwriters instead of recruiting an IT person to write SQL queries or PowerBI reports.” states Giroux. "MGA’s may have two or three people in the background, and if those people leave the organisation, they take knowledge with them as well.”

This kind of staff turnover can severely disrupt data operations, leaving companies exposed to risks and inefficiencies. Such turnover can also be exacerbated by the underwriters’ frustration with having to manually maintain, for example, their triangulations in Excel or PowerBI instead of accessing an advanced analytics platform, such as Giroux.ai, which already provides these sorts of analyses right out of the box.

Without continuity in key data roles, insurers may find themselves struggling to maintain consistent data practices, which can lead to gaps in analysis, reporting and overall decision making.

Giroux.ai solves this problem by offering a professional consulting service alongside its platform that handles complex data tasks at a fraction of the cost of employing a full analytics team.

"For the cost of a single analyst, we provide a highly sophisticated platform that delivers actionable insights quickly and efficiently," Giroux said.

This allows MGAs to focus their valuable resources on core business activities, rather than grappling with the labour-intensive task of data management.

The hidden costs of a DIY solution

There are also significant time savings associated with partnering with Giroux.ai.

"In terms of timeline, most clients who come to us have typically spent at least 18 months, if not two years, attempting to make their DIY approach work, only to find it unsatisfactory. Once they start engaging with us, they begin seeing tangible results within about three months. This isn’t the end of the project; it’s merely the start of a best-practice approach to analytics,” Giroux underlined.

By partnering with Giroux.ai, companies will receive personalised and receptive support. ??????

"Our clients often feel as though our team is part of their own; we operate as true partners. Whenever they have questions, we address them quickly, and we handle everything for them – leaving them with high-quality analytics that are easy to access," stated Giroux.

“This enables them to be stress-free and ultimately allows them to push their resources into other more valuable areas of insurance,” added Giroux.

Research from Crisp reveals that setting up automated ingestion for a new data source requires two engineers and a project manager, taking an average of 18 weeks and costing around US$100,000 per source. This expense covers only a single data stream, and the complexity multiplies as more sources are added.

Maintaining these pipelines adds to the burden, with companies spending approximately $500,000 annually to keep them functional.?

Frequent changes to data structures pose an ongoing challenge, leading to data discrepancies, broken reports, and operational delays. Even a minor update, like adding a new data field, can disrupt the entire ingestion and reporting process.

Addressing these issues is time consuming. Teams must first identify changes, understand their impact on reports, and then determine how to integrate new data points.?

This reactive approach can result in constant firefighting, with maintenance costs spiralling out of control. Changes in data formats or updates is a common reality in insurance, requiring continuous and costly pipeline upkeep.

The true silver bullet

While DIY solutions seem on the surface to be tempting, the reality is that they can leave an organisation red-faced and out-of-pocket. For example, Microsoft is doing a great job at promoting PowerBI as a silver bullet but most of the challenge is the creation and maintenance of the transform layer. As such, it is far more cost-effective to partner with a specialist company.

The company’s innovation and expertise has seen Giroux recognised for its impact, securing a place on the prestigious InsurTech100 list.

This achievement highlights the company’s status as a leading player in the space and reinforces the view that partnering with Giroux.ai is not just a wise choice, it’s a strategic imperative for businesses looking to maximise their market position.

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