Why data is the most underappreciated asset in your company

In today's dynamic, technology-driven business landscape, companies are constantly seeking that elusive edge over their competitors. They invest heavily in cutting-edge tools, hire top talent, and meticulously strategize to gain a competitive advantage. Yet, there is one asset that is often taken for granted, even though it could be the key to unlocking unparalleled success: data.

In the relentless pursuit of innovation and growth, many organizations fail to recognize the true potential of their data – an oversight that may be costing them dearly.

Data has silently transformed into the lifeblood of modern businesses. It flows through every department, transaction and decision. However, despite its omnipresence, it remains an underappreciated and underutilized resource in the corporate world. In this article, we will delve into the reasons why data is often undervalued and explore the profound impact it can have when harnessed effectively.

We live in the age of information overload, where data is abundant and it is important to recognize that not all data is created equal. Just as a diamond in the rough requires careful cutting and polishing to reveal its brilliance, data requires strategic handling to reveal its true value. This point is especially relevant given the recent advent of artificial intelligence (AI).

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What is artificial intelligence (AI)?

According to the Oxford Learner’s Dictionaries, AI is the study and development of computer systems that can copy intelligent human behavior. And looking at its applications, according to the latest Bain & Co. definition, AI is a range of analytical techniques that allows a computer to detect relationships, predict outcomes, and act based on patterns in data without being explicitly programmed to do so.

Intelligent systems are made possible by Machine Learning (ML), a subfield of AI, which consists of algorithms that learn and improve as they go. Together, AI and ML can be powerful tools, enabling companies to automate manual processes, optimize customer recommendations and develop innovative products.

The ultimate point to remember about AI and its subfield, ML, is that despite the technological power, these systems cannot function without data.

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for customer experience

The current state of data management

A suboptimal data quality strategy can have severe consequences for CX analysis and decision-making. This problem is exacerbated given the advent of and reliance on AI.

As outlined below, a poor data quality strategy has several consequences.

Operational and financial consequences

Lost revenue: Misguided decisions stemming from bad data can lead to missed revenue opportunities. For instance, if a retail AI system fails to accurately forecast demand, it can result in understocked or overstocked inventory, impacting sales and profitability.

Wasted resources: Relying on AI systems trained on low-quality data can waste valuable resources. Companies may invest in AI initiatives, hardware, and software, only to find that the results are unreliable or unusable due to data issues.

Misallocation of resources: AI-driven decisions based on low-quality data may prioritize the wrong areas or customer segments. This can hinder the company's ability to focus on what truly matters to customers.

Customer experience and reputation impact

Customer dissatisfaction: If, for example, a recommendation engine suggests irrelevant products or services to the customer due to bad data, it can lead to frustration and decreased trust in the company.

Reputation damage: Repeated poor recommendations or experiences can harm the brand's image and lead to customer churn.

Compliance risks: If AI systems make discriminatory decisions due to biased training data, it can result in legal action and regulatory fines.

AI system performance and data management

Inaccurate insights: AI models rely on data to make decisions, so if the data is flawed, the decisions and recommendations generated by the AI system will also be flawed.

Difficulty in problem diagnosis: When AI systems produce unexpected or incorrect results, it can be challenging to diagnose the root cause. Without a data quality strategy in place, it may take longer to identify and rectify data-related issues.

Underutilization of AI: Companies may become skeptical of AI's effectiveness if they consistently experience subpar results. This can lead to underutilization of AI technology and missed opportunities for innovation and efficiency.

“If your data isn’t ready for generative AI, your business isn’t ready for generative AI,” according to McKinsey & Co.

The challenges that exist in data managment

Omdia has identified five key challenges that organizations face in unlocking the true value of data:

• They lack an integrated view of customer data.
• Their data isn’t transparent and accessible.
• They can’t integrate data from disparate sources.
• They find it too time-consuming to query omnichannel data.
• They lack the talent and knowledge to execute.

Any one of these challenges can keep an organization from fully utilizing its data, and for organizations that face two or more of these challenges, the prospect can seem daunting. But they cannot afford to continue to live with the consequences of their current state.

On an optimistic note, Forrester indicates that enterprises are investing heavily in data and data structure.

The consultancy says 48 percent of CX leaders mentioned an increase in their data and research budget in 2024. That is progress, but still more is needed.

The hidden cost of bad data                                                

There are hidden costs associated with minding data. Personalization is often referred to as the new CX battleground by pundits, however, underestimating the power of data can have costly consequences.

Bad data can lead companies to completely miss opportunities to better understand and connect with customers on a deeper level. 

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Misguided marketing personalization

Imagine you are a frequent traveler who recently booked a luxurious beach vacation in Mexico through an online travel agency. As an adventure enthusiast, you enjoy exploring off-the-beaten-path destinations and have a preference for eco-friendly accommodations.

After returning from your trip, you start receiving personalized travel recommendations from the same travel agency. Excited to discover new destinations, you eagerly open the email, only to find recommendations for luxury ski resorts in Switzerland and high-end spa retreats in Bali.

Confused and disappointed, you realize that the travel agency has completely missed the mark with their personalization efforts. Rather than understanding your preferences for unique destinations and eco-friendly accommodations, they have made assumptions based solely on your recent booking.

To make matters worse, the recommendations they are sending couldn't be less aligned with what you enjoy. It feels like a generic marketing campaign, and the travel agency has failed to deliver the personal touch that was promised.

While personalization can enhance the customer experience when done right, it can also have adverse effects when misused. Companies must exercise caution and consider the potential pitfalls of personalization efforts that are built using inadequate data.

Increased customer churn / decreased account activity

Inaccurate, incomplete or outdated data can have a detrimental effect on customer retention. It could be a result of data entry errors, lack of data validation processes, or the failure to maintain data hygiene practices.

Let’s build on our example above, which discusses a customer’s loss of trust and confidence in a brand after receiving bad marketing communications. If irrelevant communications persist, this customer will begin to feel neglected and disengaged with the brand. Despite being a loyal and potentially high-value customer, they may start to drift away and seek better experiences and deals elsewhere. The travel agency may be unaware of the problem brewing behind the scenes and may attribute the decline in sales to external factors such as market competition.

By the time the travel agency realizes the issue lies with their data and adjusts their marketing efforts, it will likely be too late. Many of the mislabeled customers had already switched to a competitor, resulting in increased customer churn. The cost of acquiring new customers to replace those lost was substantial, far exceeding the expense of ensuring data accuracy and quality in the first place.

Operational inefficiencies

Failure to maintain data accuracy and reliability can lead to misinformed decisions, but what does that actually look like? 

Inaccurate and unreliable data can be fragmented, scattered, or inconsistent information collected from various touch points throughout the customer journey. This can include incomplete or outdated contact details, inaccurate purchase histories, or disjointed records across different systems. When left unaddressed, these issues can lead to a multitude of problems for businesses.

Operating with unreliable or incomplete data can result in significant operational inefficiencies. Customer service representatives may waste valuable time searching for missing information or attempting to reconcile conflicting records. These unnecessary efforts not only hamper productivity, but also increase costs associated with prolonged handling times and potential errors.

To illustrate this point further, let's consider an example: company X operates an e-commerce platform that sells various products online. Due to poor data management practices over time, their customer service database becomes fragmented with duplicate entries and inconsistent records. As a result, when customers reach out for assistance regarding their orders or returns, representatives struggle to access accurate information promptly.

This lack of centralized and reliable data leads to prolonged response times as representatives scramble between multiple systems trying to piece together relevant details about each case. Consequently, frustrated customers may experience delayed resolutions, resulting in diminished CX and potential loss of loyalty.

Building your data strategy

No two organizations are alike and, as such, each must have their own unique data strategy; some organizations are more advanced than others, but there are key components to any successful strategy, regardless of where a company is starting from.

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Data-driven culture

Leadership must foster a mindset and approach that values data and emphasizes the use of data and analytics to inform decision-making and drive business processes that result in organizational success.

This involves bringing data into every aspect of an organization's operations, from strategy development to day-to-day tasks, with the goal of improving efficiency, effectiveness, and overall performance. The quality of data is defined by the quality of an organization's culture and its business operating model. Building such a culture requires commitment, investment, and a willingness to embrace data as a strategic asset. 

Data governance

It is critical to establish a clear set of practices, policies, processes and rules that the entire organization uses to effectively manage data assets. This involves defining and implementing data management standards and guidelines to ensure data is accurate, secure, accessible and used in a way that aligns with the organization's objectives and regulations. 

Trust 

As part of their data governance, organizations earn the right to conduct business with customers, partners, employees and shareholders through data trustworthiness.

Three elements that drive data trust are:

Data security and privacy: Establish policies, standards and monitoring to ensure the data is protected and personal data is secured.

Ethical data use: Establish ethical data usage guidelines to uphold individual privacy, avoid bias and comply with legal regulations such as GDPR or CCPA. 

Transparency: Establish communication, clear standards, and plain language on how data is collected, processed and utilized, as well as the purpose and methods of data processing.

Integrated a data ecosystem

An integrated data ecosystem is a comprehensive and interconnected system that allows organizations to efficiently collect, store, manage, and analyze various types of data from diverse sources. It acts as a cohesive platform that brings together different data types, such as customer information, sales data, market research and operational metrics.

Data taxonomy plays a crucial role in this ecosystem by determining what data needs to be collected, where it will come from, and how it will be integrated. It involves collecting data from various sources, both internal and external, and organizing it into a coherent data architecture.

In an integrated data ecosystem, data flows seamlessly across departments and systems, eliminating silos and promoting collaboration. This integration enables organizations to gain a holistic view of their data, uncover valuable insights and make informed decisions.

Key components of this ecosystem often include:

• A robust data warehouse or data lake.
• Data integration tools.
• Data governance processes.
• Analytics and reporting capabilities.
• Advanced technologies like AI and ML.
   

By implementing an integrated data ecosystem, organizations can:

• Streamline data management processes.
• Improve data quality and accuracy.
• Enhance operational efficiency.
• Drive innovation.
   

Eventually, it empowers businesses to leverage their data assets effectively, enabling them to stay competitive in today's data-driven landscape.

Conclusion 

Ignoring data's potential is no longer a viable option.

Embracing a data-driven approach requires building a robust strategy that effectively manages the entire data ecosystem. This strategy should not only leverage insights for impactful business decisions but also foster a culture of data governance, transparency and trust.

By taking a methodical approach grounded in business and customer needs, organizations can harness data's power while remaining flexible in today's dynamic environment.

Leveraging advanced technologies like AI further amplifies this potential. Ultimately, unlocking the power of data allows companies to create enduring value for customers, shareholders, and employees.