How Bad Data Can Damage Your Business

The practical application of the Iceberg Model

“Where there is data smoke, there is business fire.”

-Thomas Redman (The Data Doc)

Data is an essential tool for a business, helping it to become more competitive, better informed and super-efficient. However, simply having data is not enough - the accuracy of the data is critical. Basing your strategy on inaccurate data is like steering your business straight into an iceberg. It can skew decision-making, waste resources, damage your reputation and create missed opportunities. It can also cause you to lose a deal or a client. Talking of icebergs, we can learn many lessons from the mental model – The Iceberg Model. What you see happening above the surface is just a small fraction of the whole picture…

The Iceberg Model

Losing a deal or a client base might seem like a straightforward, surface-level event but the Iceberg Model reveals that it’s often the tip of a much larger problem. By considering the deeper factors at play, you can understand why you may lose clients or deals and how to prevent that sinking feeling.

The Iceberg Model is a concept that highlights that real problems are hidden from plain sight. Only about 10% of the iceberg's mass is above the water, with the majority (90%) below the ocean.

The Iceberg Model consists of four levels:

1. Events
2. Patterns and Trends
3. Underlying Structures
4. Mental Models

Events will tell you what is happening right now. If you look below, you can see trends or patterns over time. Diving further down, structures and Mental Models tell you why it is happening.

In this blog, we will apply the Iceberg Model to our client profiles and deconstruct the patterns we see repeatedly, whilst trying to pinpoint the causes of business damage.

1. Events – what's above the surface

The tip of the iceberg (events) is the portion visible above the waterline. This represents the current situation and visible dynamics and events. This might include declining revenue, fewer transactions, client complaints, higher local market competition and marketing campaigns failing to resonate with target audiences, leading to low engagement and poor ROI. Declining sales and loss of client base don’t provide much insight into the deeper causes. Businesses may resort to quick fixes to stop this happening without actually addressing the root problem. Surface-level events are just a dip in the ocean. When businesses only focus on surface-level events, they may interpret this without a broader context.

2. Patterns and trends – what lies just beneath the surface

Beneath the surface, you can start to observe patterns and trends to reveal why the client left, or the deal was lost. The company may notice over time that clients leave for the same reasons or that there’s a reoccurring issue causing deals to fall through. Perhaps clients leave for competitors as they see them as more trusted and credible or that rivals will deliver stronger results or better value. Whilst working with estate agents, we have observed that overpricing properties relative to current market realities lead to slow sales or withdrawals. Poor sales progression leads to lost opportunities, with some agents losing up to 21 exchanges annually due to inefficiencies. In the case of retail businesses, the inability to integrate data across marketing channels results in incomplete customer profiles, making personalisation difficult. This further leads to consumers disengaging and switching brands offering more personal relevance. These trends indicate that many companies are not effectively adapting their strategies to changing market conditions.

3. Underlying structures – going deeper

At this level, we dive into the underlying structures that influence the patterns and events we observe. These are the systems, processes or practices that contribute to the company’s inability to retain clients or close deals. This is where weak market data becomes a critical factor. Perhaps the company is conducting insufficient market research and failing to position itself effectively against competitors. Alternatively, the company may be relying on partial, outdated or inaccurate data which misguides their decision-making.

For property businesses, structural issues consist of:

• Poor use of data: Many agents fail to leverage accurate and timely property market data to inform pricing strategies or identify motivated sellers and buyers.
• Inconsistent market insights: Agents often lack localised knowledge or fail to use comparative data effectively, leading to mis-pricing properties or missing key opportunities.
• Operational inefficiencies: Inefficient processes for analysing and applying market data can result in slow sales progression and missed instructions.

For example, agents who price properties based on outdated or incomplete data may alienate potential clients who expect realistic valuations.

For retail brands, heavily driven by their marketing operations, weak market data emerge as a critical factor influencing the observed patterns:

• Inaccurate demand forecasting: Retailers often fail to use robust data analytics for predicting consumer preferences or seasonal trends, leading to mismatched supply and demand.
• Limited consumer insights: Many retailers lack granular data on customer demographics or purchasing behaviour, making it difficult to tailor product offerings or marketing strategies effectively.
• Operational inefficiencies: Poor integration of digital tools for inventory management or e-commerce platforms hampers agility in responding to market changes.
• Dependence on housing market trends: Homeware sales are closely tied to housing market activity. A lack of up-to-date insights into residential property and homemover data may mean missing untapped revenue opportunities.

Without addressing these structural issues, retailers remain vulnerable to external shocks and competitive pressures.

4. The deepest level of beliefs and assumptions

At the bottom of the iceberg, we find mental models. These are the underlying beliefs, assumptions and attitudes that guide how you run your business. In both property and retail, these mental models often shape how you approach data collection and decision-making. A lack of emphasis on leveraging advanced analytics in retail perpetuates reactive rather than proactive business practices. If an estate agent cannot demonstrate strong local market knowledge backed by reliable data, potential clients may choose competitors who appear more informed and proactive. You may think that your data is ‘good enough’ or that more detailed up-to-date data is too expensive or difficult to manage, so you just continue to rely on the same processes. With this mindset, you can miss opportunities, create poorly targeted marketing campaigns and ultimately lose clients or deals.

The implications of inaccurate data

Firstly, let’s define what we refer to as good and bad data.

As a data analytics business, we have been working for over 16 years to ensure that the data we provide is of the highest accuracy and genuinely useful to the companies and brands we work with.

Good data is high-quality, reliable, and fit for its intended purpose. It reflects real-world conditions without errors (e.g., correct address details or market indicators). Good data is complete, remains uniform across systems and periods, and is up-to-date and relevant to current decisions or analysis needs. You know you have good data if it aligns with the specific goals or questions being addressed.

Conversely, bad data fails to meet quality standards and often leads to inefficiencies, errors, and poor decision-making. It misses the critical information, which reduces the dataset's usefulness. Bad data conflicts values across systems or datasets, creating confusion, it also often requires a significant effort for cleaning and processing before it can be used effectively. In simple terms, if you have to think twice about using it, it is not any good.

Now, let’s look at some real-life scenarios that could impact you as a business if the data you rely on is inaccurate:

Wasted spend

Picture this: a retailer sends out thousands of direct mail leaflets, but their data is outdated and many of their addresses are incorrect. The retailer will waste its budget on printing and postage and lose out on reaching customers who may be interested in their product or service.

Poor location choice

A national retailer may decide to open a new store in a location based on inaccurate data. They may believe this location is home to their target customer or is an area that is rapidly growing. However, if the demographic data is incorrect, the new store may suffer from low footfall and poor sales - leaving the business lost at sea.

Lack of the full picture

Marketers who don’t understand the full dynamics of the market, fail to identify motivated sellers and buyers during economic downturns. Estate agents lose client trust when listings stagnate because they are not aligned with current market conditions.

Ineffective product launch

A company will hit rough waters if it launches a new product based on misleading consumer demand data. This would result in slow sales and marketing efforts aimed at the wrong audience.

Here’s how bad data can sink your success like the Titanic:

• Misaligned business goals: When your strategy is built on incorrect data, you risk focusing on the wrong priorities.

• A distorted view: If your data only covers 70% of the market, that 30% blind spot could be large enough to throw off your entire business strategy.

• Reputational damage: If your clients notice that the data you’re using is inaccurate it could damage your reputation. For example, if you send out poor recommendations for products.

• Missed opportunities: With incorrect data, you’ll overlook key trends and misjudge demand. Imagine a campaign that only reaches a third of your target market - you’re leaving money on the table.
• Lost revenue: Bad data impacts your stock levels, underpricing high-demand products, targeting the wrong audience and investing in the wrong locations. Your business could end up treading water rather than making real progress.
  

Now that we’ve broken down the Iceberg Model, you can see that losing a client or deal due to bad data is a multilayered issue. The problem starts with surface-level events, but the deeper cause lies in patterns and trends supporting your decisions and the mental models that shape your approach to data.

Here’s what to do to steer your business in the right direction:

1. Surface-level events: Acknowledge the loss and understand the immediate reasons as to why.
   
   
2. Patterns or trends: Identify reoccurring issues such as missed opportunities for personalisation. For example, TwentyCi provides UK residential property data. You can easily anticipate customer needs with this data and personalise your marketing to the homemover.
   
   
3. Underlying structures: Invest in accurate data and tools that can help you better understand customers and the market you operate. This is where our property and homemover data can be invaluable. By utilising high-quality, up-to-date data about homemovers, retailers can pinpoint individuals who are in the transactional phase and likely to need specific products or services, allowing for more targeted and effective marketing strategies.
   
   
4. Mental models: Shift your mindset towards valuing accurate and comprehensive data as an essential resource for making decisions. Recognise the cost of not using accurate data.

Conclusion

By understanding and addressing these causes of poor data quality, retailers and property professionals can start making smarter data-driven decisions that lead to better customer outcomes, improved profitability and clients, and increased resilience in volatile market conditions. Accurate and complete data should be the cornerstone of every business strategy to blow competitors out of the water.

Talk to us about UK residential property data and how you can achieve an impressive £21:1 return on investment by targeting the homemover in your marketing. Get in touch today!