Introduction
Data has become one of the most valuable assets for business success, driving everything from marketing campaigns to sales strategies and shaping the decision-making processes that enable sustainable growth. As businesses increasingly embrace digital transformation, leveraging automated data-driven systems and advanced analytics has become a top priority. Senior leaders are now focused on uncovering hidden opportunities by moneyballing everything from product roadmaps to marketing strategies. In this context, maintaining high-quality data is more critical than ever.
Yet, as data volumes continue to rise and information remains fragmented across various points, organisations without automated data systems frequently face challenges in accessing reliable information when it's most needed. Issues like data drift, inconsistencies, human error, duplications, and siloed data across departments make it difficult to derive accurate insights—ultimately undermining decision-making and stifling growth. This becomes especially critical when making real-time credit decisions, where inaccurate data can lead to costly mistakes or missed opportunities.
This "data fog" isn't just a temporary issue; it can lead to delays, bottlenecks, and inefficiencies that ripple through the organisation, negatively impacting the bottom line. To keep the business on a growth trajectory, organisations must ensure that the data driving key decisions is accurate, timely, reliable, and accessible. This requires a proactive approach to data management, ensuring its integrity and quality before it reaches decision-makers.
In this article, we explore the importance of maintaining rich data quality and how organisations can leverage automation to enhance their credit management process with a zero-change approach in Salesforce.
Understanding Salesforce and credit decisions
Digitising and automating credit and risk workflows has long been a strategic priority for credit teams across industries. In today's landscape, marked by economic challenges and fierce competition from tech-savvy rivals, reducing operational costs, accelerating credit decisions, and maintaining precise pricing for customers and counterparties is more crucial than ever.
Yet, credit checks are usually performed outside of Salesforce, requiring manual data transfers to separate systems. This approach introduces friction, delays decision-making, and heightens business risk. A recent survey highlights this issue: 46.7% of respondents identified the manual collection of business-critical information as their main challenge when initiating the loan or credit provisioning process. Additionally, Dreamforce research shows that nearly 49% of Salesforce data loss and corruption incidents are caused by human error.
Outdated data in Salesforce exacerbates these issues, resulting in inaccurate credit risk assessments, missed opportunities, and increased risk. Correcting this data manually also intensifies the problem, driving up operational costs and obstructing timely, informed decision-making. Studies reveal that data teams spend up to 80% of their time locating, cleaning, and organising data, leaving only 20% for analysis! This delays decision-making, creating internal friction and ultimately hindering business growth and efficiency.
Data quality is directly linked to the quality of decision-making. Good quality data provides better leads, better understanding of customers and better customer relationships. Data quality is a competitive advantage that D&A leaders need to improve upon continuously.
Melody Chien
Senior Director Analyst, Gartner.
The challenges of data
Knowledge is power, and businesses today have more information than ever. Adopting a data-first approach is crucial for efficiency, growth, customer experience, and risk management. However, this approach presents several key challenges.
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Data integration
Businesses rely on multiple tools and platforms, which can result in data silos and fragmented information. Integrating data from diverse sources while ensuring compatibility and consistency across systems is a significant challenge that can slow down decision-making and hinder efficiency. Without seamless integration, businesses face delays in accessing the full picture needed for informed credit decisions. Incorporating data into an existing tech stack allows data to be exchanged between systems, creating a "zero-change" approach that can drive real-time risk analytics and decision-making.
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Data quality
Having large volumes of data is not enough; the data must be accurate, complete, and relevant. Poor data quality results in flawed analysis, leading to misguided decisions and heightened risk. Inaccurate or duplicate data, missing fields, and inconsistent formatting are common issues that reduce the reliability of insights. Businesses must establish strong data governance practices and implement regular data cleansing processes. By improving data accuracy and consistency, companies can remove barriers to automation and digital transformation - ensuring that the data remains trustworthy, actionable and accessible.
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Increasing volume
The sheer volume of data generated daily can overwhelm businesses. Managing and processing massive amounts of information requires significant resources, robust infrastructure, and advanced tools. Without proper handling, organisations struggle to extract valuable insights from the data, leading to inefficiencies and missed opportunities. As data continues to grow exponentially, businesses must adopt scalable solutions that allow them to efficiently process, analyse, and act on data to maintain a competitive edge and drive informed decision-making.
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Data understanding
With the increasing volume and complexity of data, many businesses struggle to fully comprehend and interpret the information they have. Without a clear understanding, it becomes difficult to extract actionable insights and make informed decisions. Organisations must implement zero-change approach tools that simplify data interpretation and make it more accessible and usable for decision-makers across the enterprise. By ensuring data is accessible, useable and actionable, businesses can unlock its full potential and drive more effective decision-making, driving heightened efficiency and strategic outcomes.
The challenges of data management are significant, but so are the benefits of overcoming them. Businesses that have adopted automated credit-decisioning models have gained three key advantages:
Increased revenue: Automation has led to a 5–15% increase in revenue, thanks to higher acceptance rates, lower acquisition costs, and enhanced customer experience.
Reduced credit losses: More accurate models determining a customers’ likelihood to default have reduced credit losses by 20–40%.
Efficiency gains: Businesses report 20–40% improvements in operational efficiency through precise data extraction, case prioritisation and model development.
Embracing decision automation
As senior leaders assess AI initiatives to address tangible business challenges and drive efficiency, decision intelligence is emerging as a vital strategic asset. This technology not only solves complex problems but also enhances decision-making processes by integrating intelligent automation with existing solutions and data sources.
The current reality is that decision-makers frequently lack the time, capacity, visibility, and technology required to make optimal decisions in a timely manner. A recent IDC study highlighted that 25% of decisions that should be made are not being made due to operational hurdles related to data, analytics, and AI. This gap in decision-making capability leads to missed opportunities and inefficiencies that can undermine business performance.
Decision intelligence addresses these challenges by digitising and automating decision-making processes. By embedding intelligent engines into existing workflows, it accelerates and refines decision-making, enabling faster, more accurate decisions at critical moments. The technology can assess complex variables, provide recommendations based on specific business rules, and facilitate impactful decisions that drive value.
With continually more dynamics and complexity in modern-day business — especially digital business — our capabilities must improve to make the best possible decision in the shortest possible time, in a scalable, risk-conscious, consistent, adaptive and personalised fashion.
Pieter den Hamer
Sr. Director, Analyst, Gartner.
Turning data into revenue in Salesforce
In today’s digital era, data is as vital to businesses as air and water are to living organisms. Every business action generates new data, and harnessing this information is crucial for informed decision-making. However, transforming data into a revenue-generating asset requires more than just collection; it must be reliable, accurate, and actionable.
Clive Humby’s famous quote, “Data is the new oil. It’s valuable, but if unrefined, it cannot really be used,” perfectly captures the importance of effective data management and quality. Inaccurate or outdated data within Salesforce can disrupt credit decision-making by delaying approvals, increasing risks, and hindering commercial outcomes. Manually correcting data only adds to these delays, creating internal friction. Given the significant investment a CRM represents, maintaining high-quality data is essential for maximising its value and boosting team performance.
The Salesforce Business Intelligence Plus application integrates Creditsafe’s global company data with Salesforce through automated workflows, enriching, updating and monitoring customer records continuously. With real-time risk scores and suggested credit limits derived from over 200 data points, credit teams can make informed decisions faster, reducing manual errors and enhancing efficiency. These automated processes speed up credit and compliance decisions by 70% with just a few clicks, improving overall credit and risk management.
Incorporating Business Intelligence Plus into Salesforce transforms it from a static repository into a dynamic, revenue-generating tool by providing real-time, reliable data. This enables businesses to make smarter credit decisions, streamline customer onboarding, mitigate risks, and reduce operational costs. Ultimately, data becomes a strategic asset, driving sustainable growth through enhanced decision-making and improved efficiency.