We all know data is one of the most (if not the most) valuable resources for business. So, we’ll bypass the catch phrases, buzzwords, and buy-low-sell-high insights and dive right into some of the key data issues that can lead to missed opportunities, inefficiencies, and financial losses.
1. Data Silos
Data silos are the most common issues. These occur because information is isolated within specific departments or systems, making it difficult for other parts of an organization to access or integrate it. For example, the sales team might have customer data stored in one system, while the marketing team uses a different platform, and neither team can easily share information.
Impact:
Data silos create fragmented and incomplete insights. When different departments cannot access or collaborate on shared data, decision-making becomes slower and less informed. This disjointed view of data leads to inefficiencies, such as duplicated efforts or missed opportunities to cross-sell or upsell. This can also hinder customer experience management, as different teams may be working with outdated or incomplete customer profiles.
You’ll never have a “source of truth,” or reporting process that isn’t filled with late nights and expletives until you unify your data. Data unification is the process of aligning all incoming sources so data is standardized, complete, and in real-time.
2. Poor Data Quality
Data quality issues can arise from incorrect, incomplete, or inconsistent data entries. This may happen due to human errors during data input, system errors, or failure to properly maintain and clean data over time. Poor data quality undermines the integrity and reliability of information, leading to flawed business insights and decisions, or as the technical literati put it: garbage in, garbage out.
Impact:
Bad data costs businesses significantly. Gartner estimates that poor data quality costs organizations an average of $12.9 million annually. When companies rely on inaccurate data, they make decisions that can result in financial loss, customer dissatisfaction, and wasted resources. For example, inaccurate sales forecasting due to bad data can also lead to supply chain issues, including overstocking or stock shortages. But don’t stop there, inaccurate data on vendors or logistics providers can result in everything from losing customers due to quality and/or late delivery issues to inaccurate financial reporting.
Reliable data is a fix for losses associated with inaccurate or obsolete information. There is no one-and-done solution for achieving and maintaining reliable data, but solving the problem will more than pay for itself.
3. Data Privacy and Security Concerns
With the growing volume of data comes the heightened risk of data breaches and cyber-attacks. Companies must handle sensitive data, such as personally identifiable information (PII) and financial details, with utmost care. Compliance with regulations like the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the U.S. adds complexity to data management. Many organizations struggle to balance data collection and utilization with the need to protect it from unauthorized access.
Impact:
Data breaches can have severe consequences, ranging from legal penalties to reputational damage. A single data breach can result in millions of dollars in fines and compensation to affected customers, not to mention the loss of trust from consumers. For instance, companies that fail to comply with GDPR could face fines up to €20 million or 4% of their global annual turnover, whichever is higher. Beyond the direct financial impact, breaches and data mishandling erode customer trust, leading to churn and long-term brand damage.
Addressing concerns in this area include: removing personally identifiable information (PII), data masking sensitive information, data anonymization, eliminating duplicate data, standardizing formats, removing obsolete data, validating data accuracy, creating audit trails that log access to sensitive data as well as controlling data access, encrypting sensitive data, regularly auditing and updating data security policies
4. Inadequate Data Governance
Data governance refers to the processes and policies that ensure data is managed properly across the organization. Many companies lack robust data governance frameworks, resulting in inconsistent data management practices. Without clear data ownership, accountability, and standardized processes, organizations struggle to maintain control over their data.
Impact:
Inadequate data governance results in compliance risks, data duplication, and inefficiencies in data usage. Without proper governance, businesses may find themselves in violation of data protection laws or unable to trace the origin of their data, which can create problems during audits. It also increases operational inefficiencies, as employees spend more time searching for and validating data. Furthermore, poor governance makes it challenging to scale data initiatives across the organization – this means a company’s ability to effectively introduce AI and/or ML is severely impacted. See above … garage in, garbage out.
5. Data Integration Challenges
As companies grow, they often adopt new systems, platforms, and tools, leading to a complex ecosystem of data sources. One of the biggest challenges for organizations is integrating these disparate data sources to create a holistic view of their operations and customers. Mergers and acquisitions can exacerbate this problem, as companies need to merge different data infrastructures.
Impact:
Data integration challenges hinder a company’s ability to gain a 360-degree view of their operations and customers. Without integrated data, it’s difficult for businesses to perform advanced analytics, such as predictive modeling or customer segmentation. This can result in suboptimal decision-making and missed opportunities for personalization or process optimization. For example, a financial institution might struggle to combine customer transaction data with social media sentiment analysis, limiting its ability to detect fraud or predict customer churn. Now imagine a merger where multiple entities each bring “dirty” data to the new structure – the depth and breadth of negative ramifications will span the new entity while it continues to exist, however short or long that may be.
You wouldn’t pack filled trash cans in a move, why do it with your data? Avoid this mistake! Make Step 1 data unification, aligning all the disparate systems to ensure accuracy in real time and interoperability system wide. Then clean the data before integrating it. That is a powerhouse merger.
6. Lack of Skilled Personnel
While companies may have access to massive amounts of data, many struggle to find the talent needed to analyze and derive value from it. The shortage of skilled data professionals, such as data scientists, data engineers, and data analysts, makes it challenging for businesses to fully capitalize on their data assets. As a result, data initiatives often fall short of expectations, and businesses are unable to extract actionable insights from the data they collect.
Impact:
The lack of skilled personnel slows down data-driven innovation and decision-making. Without the right expertise, companies struggle to implement advanced analytics, machine learning, or artificial intelligence initiatives that could drive competitive advantage. This talent gap also makes it difficult for companies to keep up with the rapidly changing data landscape, leaving them at a disadvantage compared to competitors who have the necessary resources.
Have experts train up your internal team. Outsource to experts who will work with your team and prepare them to take over. Have your internal team trained during the expert unification and/or data cleansing process so they can take over, maintain, and scale what has been achieved.
7. Data Overload
While having access to more data can be advantageous, many companies find themselves overwhelmed by the sheer volume of information available. When organizations lack the tools or strategies to manage and analyze large datasets effectively, they become paralyzed by the data rather than empowered by it.
Impact:
Just thinking about how to analyze data can lead to the distraction and time suck of analysis paralysis, stiffing the ability to leverage it effectively. Decision-makers are bogged down by too much information, making it difficult to identify actionable insights. This can slow down the decision-making process and cause businesses to miss opportunities and/or fail to identify risks early enough. Now add bad data to the mix and the amount of data becomes less important and reliability even more so.
Unified, standardized, and reliable data eliminates the daunting effects of data overload, transforming a data challenge into a significant asset.
8. Inconsistent Data Analytics Tools
The varied tools used to manage, process, and interpret data by different departments create conflicting insights and fragmented decision-making. For example, the marketing department may use one tool for customer segmentation, while the finance team relies on another for financial forecasting. The absence of a unified analytics platform can make it challenging to align data-driven strategies across the organization.
Impact:
Inconsistent data analytics tools create confusion and make it difficult to build a unified, data-driven culture. When different departments rely on different metrics or interpretations of the same data, it leads to misalignment in strategies and goals. Furthermore, companies may waste money on redundant tools or experience difficulties in scaling analytics efforts across the business.
Data is undoubtedly one of the most powerful assets businesses possess today, but it also presents significant challenges. Companies struggling with data silos, poor data quality, privacy concerns, governance issues, and a lack of skilled personnel find themselves unable to fully harness the potential of their data. The impact of these challenges is far-reaching, affecting everything from operational efficiency and decision-making to customer satisfaction and compliance. For businesses to remain competitive, it’s essential to address these data problems proactively and develop strategies to ensure their data is accurate, secure, and accessible across the organization.
However, regardless of the type of common data challenge, even if eliminated in its entirety, if the data itself is unreliable, nothing else will matter. So whatever the challenge and goal your business is facing, start with unifying your systems, clean your data, and keep it clean.
