Data decay negatively impacts your client’s ability to effectively engage with existing and prospective customers, impeding the business growth. It’s imperative for B2B aggregators to implement AI-based tools and future-forward practices to eliminate the risk of data decay.
In the fast-paced world of B2B businesses, data decay poses a significant challenge. But what exactly is data decay? It refers to the deterioration of data quality over time, resulting in outdated, incomplete, or inaccurate information. This decay can be a serious obstacle to B2B business growth, hindering crucial decision-making processes and impacting overall success. All these makes, implementing a comprehensive data hygiene strategy involving regular data validation and cleansing becomes more than necessary for B2B data selling companies.
Gartner predicts that the cost of data decay hovers around $9.7 million to $14 million per enterprise, every year! This is a staggering loss for any enterprise. Most organizations generally experience two major types of data decay in this regard – mechanical data decay and logical data decay.
Mechanical data decay occurs when data becomes outdated due to natural processes, such as contact information changes or outdated customer preferences. It’s like a slow erosion of data reliability that impacts the company’s credibility negatively.
Logical data decay implies that the data is decaying due to logical inconsistencies or errors within the dataset. This decay includes missing fields, inconsistent formatting, or duplicate entries, making it difficult for stakeholders to extract meaningful insights from the said data. Statistics show that 32% of respondents in a PhoneArena survey, reported they change phone numbers every year. People leaving companies or changing email addresses, every data change contributes to logical data decay.
Both kinds of data decay challenges have an enormous impact on business growth. Read further about the impact of data decay and the 5 ways to fix it for your business to make profitable growth.
Impact of data decay on b2b business growth
The consequences of data decay are far-reaching and one can expect the decay to significantly hinder business growth. Abandoned sales – check. Wastage of marketing resources and effort – check. Missed revenue opportunities – check!
Outdated information can easily lead to customer dissatisfaction. Data decay can also impact every effort that the marketing team puts in, to nail personalization and service to its prospects and customers. Additionally, expect inefficient data management leading to higher operational costs. Absence of genuine leads and up-to-date information can cause disarray in marketing efforts. Also, bad data erodes trust among clients and partners. In some cases, fabricated data leads to distorted insights and flawed decision-making too.
Common challenges of data decay in b2b businesses
Identifying the common causes of data decay is important for businesses to tackle this issue effectively. Organizations can then take proactive steps to address the root causes and minimize the impact of data decay.
- Outdated contact information: Databases are rife with outdated contact information and hence maintaining up-to-date data is paramount for effective communication and customer engagement. However, contact details become redundant from time to time, as people change jobs, addresses, companies merge, or contact preferences change. Consider the fact that 25-33% of email addresses become outdated annually. If you are targeting heads of client companies for email marketing, consider the fact that 21% of CEOs change every year. Organizations need to employ periodic updates in customer information by keeping track of these changes regularly.
- Incomplete or missing data: Incomplete data continues to be a raging problem in B2B databases. Businesses find it tough to sort out missing fields or complete incomplete records despite hours of effort put in research and discovery. Incomplete data renders an incomplete understanding of customers and prospects and leads to flawed analysis of trends and patterns. Obviously, the faulty analysis culminates in ineffective decision-making.
- Mushrooming of start-ups: The business landscape continues to evolve briskly, with the emergence of new start-ups and the new ones scaling up their customers and profitability. This dynamic environment forces tracking of customer information changes on a regular basis. They need to identify potential prospects and the authenticity of their information with quick and effective updates on every change to stay ahead of the competition.
- Unreliable sources of data: Several companies make the cardinal mistake of relying on unreliable sources for acquiring data. Old databases often include inaccurate and obsolete data, meaning that the source itself is decayed. It’s crucial for businesses to verify the credibility and accuracy of their data sources. They should adjudge the relevance and recency of the data points to ensure the quality and reliability of the information.
- Data hoarding beyond stipulated periods: Holding onto outdated data for extended periods of time, especially years, can aggravate data decay. Businesses should establish policies and practices for timely data retention and archiving for specific periods of time and purge irrelevant or outdated information in a timely manner. Hoarding irrelevant data beyond the stipulated periods will only waste processing power and affect the quality of insights.
- Changes in job roles and responsibilities: As employees change roles or leave organizations with due course of time, their responsibilities and contact information may change. Without updating this information, enterprises are bound to be impacted with lack of communication with their prospects and customers. The lack of updated data presents lost business opportunities, which means businesses need to incorporate processes to capture and update changes promptly.
- Business mergers and acquisitions: The corporate landscape keeps changing as companies shut down, or merge with other entities or acquire another company from time to time. These events can introduce changes and challenges to data quality as well as logical continuity. Enterprises need to integrate and consolidate data from diverse sources and different systems with every announced merger and acquisition to maintain accuracy and completeness of data.
By addressing these common challenges, businesses can take proactive steps to combat data decay and ensure the reliability and integrity of their data sources.
5 ways to fix data decay challenges for consistent b2b business growth
Drawing from our data mining and management expertise, we present five effective strategies to tackle data decay challenges that include data maintenance and cleansing techniques with effective fixes to existing databases to weed out duplicates, inconsistencies, and missing information.
Data governance measures, customer data enrichment, and more are an integral part of enterprise data cleansing strategies.
Let us explore each of the 5 approaches that are useful for fixing existing data as well as establish measures for including clean data:
1. Fix existing data decay challenges
Fixing existing data issues is important to elevate data quality. Fixing those issues affect the reliability, accuracy, and effectiveness of data in every analysis.
Leaving data exposed to logical data decay stack the odds against you to preserve good data. The “cost of quality” implies that prevention is better than cure. Organizations better invest $1 in prevention than spending $10 on the cure of data decay, which is better than losing $100 on the problems caused by obsolete data to their organization.
Companies need to address data decay challenges by removing duplicates, eliminating irrelevant data, clear formatting issues, standardize capitalization, convert data types, and more. These changes ensure that the datasets are clean, consistent, and reliable. High-quality data enhances the credibility of analysis and reduces the risk of making misguided decisions and strategies based on flawed information.
- Remove duplicates: Companies need to invest in tools or implement algorithms that can identify duplicate records based on unique identifiers. These identifiers could include company names, email addresses, and the like. To remove duplicate data, determine the criteria for removing duplicates, using the latest entry or the record with highest quality data. Eliminate duplicate records systematically so that the datasets retain their integrity and accuracy.
- Remove irrelevant data: Secondly, identify and eliminate data irrelevant to business objectives right away. Typically, an average knowledge worker spends nearly 2.5 hours per day, which accounts for almost 30% of the workday, searching for relevant data. Exclude records or fields that include outdated information that does not help the business. Focus on data essential to decision-making and necessary for business operations.
- Clear formatting: Unnecessary formatting characters can distract attention from important data. Remove unnecessary formatting characters, such as leading or trailing spaces, tabs, or line breaks. Also, it makes sense to eliminate unnecessary characters or symbols that could impact data processing negatively. Your aim should be to ensure uniformity and consistency in all formatting methods across the dataset.
- Standardize capitalization: Much of organizational data suffers from incorrect capitalization across datasets. Convert all text to a specific case format – lowercase, uppercase, title case to ensure harmony across all documentation. This practice ensures minimal duplication caused by inconsistent capitalization.
- Convert data type: Use of appropriate data types also add ensure consistency and accuracy like capitalization does. Organizations need to adhere to standards that define data types for specific fields, matching intended analysis or application.
- Fix errors: Eliminate errors by identifying and correcting common data problems like misspellings or typos. You could use fuzzy matching or data validation rules for the same. Any error that is leading to bad data needs to be cleansed completely with accurate info. Delete rows with missing values on input data based on statistical methods. You need to ensure that the missing values do not matter in the long run for decision-making.
2. Ongoing data acquisition and data enrichment
As an enterprise, pursuing ongoing data acquisition and enrichment is important in fighting against data decay effectively. Robust data acquisition and data enrichment solutions ensure the freshness and relevance of datasets. Here’s how we can accomplish this:
- Manage authentic data sources: Establish relationships with reliable and trustworthy data sources that render accurate and up-to-date information. Conduct due diligence to ensure the authenticity and credibility of the data as well as the providers. For instance, the healthcare industry demands collaboration with reputable medical research institutions to acquire authentic medical data and research findings. Their connection with authentic data sources helps them align their processes with real medical trends.
- Drive in-time data collection: To prevent data decay, implement mechanisms to collect data in real-time or near real-time. Capture and integrate the most current information into datasets promptly. It matters to e-commerce companies for instance, who need automated processes to capture customer transaction data in real-time, swiftly. What this does is, it enables companies to track purchasing trends with minimal delay and devise strategies.
- Capture data from multiple sources: Relying on a single data source is nothing less than suicidal in terms of augmenting the risk of data decay. Diversifying data acquisition efforts is a must. Tap into multiple sources and address this risk head-on, thus ensuring a comprehensive and highly accurate dataset. For example, financial services companies can stay relevant and active by gathering data from multiple stock exchanges, financial news outlets, and economic performance indicators. The diverse variety of sources imbue a holistic understanding of market trends that drive informed investment decisions.
- Collect and analyze structured and unstructured data: You need data both in structured (e.g., databases) and unstructured (e.g., text documents, social media posts) formats. Both types of data are necessary to ensure comprehensive enrichment as part of data decay solutions. Retail companies always benefit when they collect structured data like a customer’s purchase history and pairs that data with unstructured datasets derived from customer reviews to understand customer sentiment and preferences perfectly.
- Append missing data fields: As part of the data enrichment process, identify and fill in any missing data fields that completes datasets accurately. 25% of poorly filed documents usually end to be missing too as knowledge workers spend nearly half of their time creating and preparing documents. Missing data fields could host a lot more details that could improve operations. For instance, logistics companies with enriched shipment records would include weight, dimensions, or delivery status for better customer service through delivery notifications. Having a sound document management strategy is a crucial part of data acquisition too.
- Enhance customer profiles: Customer profiles enable businesses to better understand their preferences, behaviour, and needs. The profiles help tailor your products and services effectively to attend to the needs of the end customers. Enriching customer profiles with missing fields and relevant data can also give insights to businesses on their product usage patterns, support ticket history, or even feedback surveys rendered for similar products and services. This information helps deliver personalized recommendations and support.
By actively pursuing data acquisition and enrichment strategies like managing authentic sources, driving real-time data collection, capturing from multiple sources, and collecting structured and unstructured data, enterprises can combat data decay directly. Organizations can leverage third-party enrichment services, append missing fields, and enhance customer profiles to ensure the continuous improvement and reliability of their datasets on a constant basis. Know how a Californian B2B enterprise strengthened its 50 million records database with Hitech BPO’s robust data management workflows powered by ML algorithms here.
3. Data maintenance and cleansing
Data maintenance and cleansing is an integral part of data decay solutions that help in stemming the tide of bad data. As organizations accumulate vast amounts of data, you need to make sure to keep the data updated and clean with regards to its ongoing quality, accuracy, and relevance. Data maintenance focuses on audits, updates, and validation to identify and rectify inconsistencies.
By proactively addressing data quality challenges, organizations can mitigate data decay risks, ensure data integrity and reliability, and make the datasets suitable for decision-making and analysis.
- Perform regular data audits and updates: Enterprises need to conduct regular data audits and updates to keep the data clean. Leverage social media intelligence, we can monitor and analyze conversations, trends, and customer sentiments across various social media platforms. For example, by tracking mentions of our brand or products, we can identify emerging customer needs or preferences and update our datasets accordingly. This ensures that our data remains current, reliable, and aligned with the evolving market dynamics.
- Data validation and verification processes: Implementing robust data validation and verification processes helps in minimizing decay. Organizations opt for the following techniques to prevent bad data:
Data verification of migrated data during integration process to ensure that the data is transferred from one system to another accurately. We curated and verified financial records for a USA-based BFSI data aggregator to build a high quality and integrated database. Know more here.
- Manual or rule-based validation techniques to review data against predefined criteria or business rules to identify anomalies, inconsistencies, or errors.
- Verification at the source to cross-reference data with authoritative sources or external databases and to validate its authenticity and reliability.
- Switch to automated data cleansing techniques/tools: Leverage automated data cleansing tools like OpenRefine and Wrangler to significantly enhance efficiency and effectiveness of data cleansing. It is preferable for companies to opt for tailored tools that utilize algorithms and techniques adhering to their preferences for this process. The custom tools can automatically identify and address common data quality issues, including formatting errors, inconsistencies, or duplicates. Hitech BPO on the other hand, offers customized data cleansing solutions, designed to align with a company’s unique data repositories and objectives. They address data decay challenges relevant to the organization thus mitigating data decay risks effectively.
4. Implementing data governance with data analytics
Reduce data decay in the enterprise and boost analytics by implementing effective data governance measures. Here’s how enterprises can pursue data governance measures that act as worthy data decay solutions regularly:
- Establish data quality standards and protocols: Define and implement strategies for data quality improvement along with standards and protocols to ensure that the acquired data adheres to company objectives. The standards ensure that the data is gathered consistency, and relevance, reducing the likelihood of data decay over time. For example, establishing rules for data validation and ensuring data completeness can help identify and rectify any inconsistencies or missing information that may contribute to data decay.
- Assign data ownership and responsibilities: Assign data ownership and responsibilities to specific departments within the organization to guarantee accountability for data maintenance and enrichment. By allocating duties for monitoring, updating, and validating data, organizations can save a lot of it reduces the risk of data decay due to neglect or lack of ownership. This fosters a culture of data stewardship and proactive data management.
- Ensure data privacy and security: Data privacy and security are crucial for any enterprise. With robust security measures and compliance to data protection regulations, you can protect data from unauthorized access, breaches, and corruption. After all, a secure data environment will help minimize the chances of data decay caused by external threats or malicious activities, ensuring data integrity and reliability.
- Devise a sound data hygiene strategy: A comprehensive data hygiene strategy involves regular data cleansing, updating, and validation processes. By removing duplicate records, correcting errors, and ensuring data consistency, enterprises can maintain data accuracy and relevance, preventing data decay. It helps to have periodic reviews of customer databases to eliminate outdated information, while keeping the data fresh and reliable.
- Manage data silos: Data silos isolate data within different systems and usually hinder data acquisition efforts. You need to break down silos and integrate data to grant the enterprise a comprehensive view. This approach will minimize the risk of data decay, enabling better decision-making and get a holistic understanding of the datasets.
- Utilize data analytics to identify data decay patterns: Data analytics play a crucial role in identifying patterns that indicate potential data decay. Analyze the data usage, use quality metrics, and decipher data access patterns to proactively detect decayed data. Then, put appropriate measures in place to rectify underlying issues while procuring or retaining data.
- Monitor data quality metrics and performance indicators: You need to monitor data quality metrics and performance indicators regularly for maintaining the health of the data. Use tracking metrics like completeness, accuracy, updates, and relevance to identify potential data decay risks promptly. Monitoring data quality will ensure that the data is trustworthy and useful for further analysis.
5. Manage marketing automation and CRM integration
Data decay affects data accuracy and integrity drastically. Enterprises have now started addressing these issues effectively with the help of robust data management approaches including automation and integrated CRM with their existing systems. All it takes is an enterprise like yours to embrace smart data synchronization, accurate customer data management, and personalized lead nurturing.
- Use marketing automation tools for data synchronization: Marketers can leverage marketing automation tools within the enterprise to manage and synchronize data across different departments. Leveraging these tools can help marketers maintain relevant data with regular updates. For example, marketing automation tools can automate the process of sending emails to customers, tracking email engagement through the campaigns, and updating changes in contact information within the CRM system promptly.
- Integrate CRM systems for customer data management: A CRM system is expected to manage customer data and interactions effectively. Integrating your CRM system with other systems and tools, can do you a world of good! Integrate the CRM with marketing automation platforms and analytics solutions to deliver insightful data that is also up-to-date and accurate. The near real-time updates are crucial in designing personalized experiences to customers and establish robust relationships with them.
- Leverage automation for personalized lead nurturing: Lead nurturing is aimed to nurture potential customers throughout their journey as a buyer of your products or services. They need to be made aware of relevant and valuable information related to business. To ensure that happens, get accurate and up-to-date customer data first. Leverage automation to tailor the nurturing process based on customer preferences. Share information that aligns with their needs and interests. Drive the likelihood of a successful sale to create long-term customer relationships.
Risks of relying on outdated data in B2B business
In today’s competitive business landscape, relying on outdated data can have serious consequences. Outdated data can lead to inaccurate customer insights, inefficient lead generation, and wasted marketing efforts. This can not only reduce business growth but also have a negative impact on customer satisfaction and retention.
At our firm, we understand the importance of accurate and up-to-date data for sustained business growth. That’s why we adhere to proactive data management, ensuring that our clients receive the highest quality data to drive their business success.
- Inaccurate customer insights and segmentation: Outdated data can lead to inaccurate customer insights and segmentation, which can impact businesses’ ability to deliver personalized experiences to their customers. This can result in reduced customer satisfaction and decreased customer loyalty.
- Inefficient lead generation and nurturing: Outdated data can also lead to inefficient lead generation and nurturing. Without accurate data, businesses may not be able to identify high-potential leads or effectively nurture them throughout their buyer’s journey. This can lead to wasted marketing efforts and resources.
- Wasted marketing efforts and resources: Without accurate and up-to-date data, businesses may not be able to effectively target their audience. This can lead to wasted marketing efforts and resources, including wasted ad spend, reduced engagement, and lost opportunities.
- Negative impact on customer satisfaction and retention: When businesses rely on outdated data, they may not be able to deliver the personalized experiences that their customers expect. This can have a negative impact on customer satisfaction and retention, leading to reduced revenue and profitability.
By adhering to proactive data management, we help our clients overcome these risks and deliver the highest quality data possible. With accurate customer insights and efficient lead generation, businesses can score high-quality leads, drive high business growth regularly, and maintain their competitive advantage.
How data quality propels business growth
At Hitech BPO, we understand that high-quality data is essential for driving business growth. With accurate data, businesses can make informed decisions, efficiently target their audience, and measure the ROI of their marketing efforts. That’s why we deliver high-quality data to propel our clients’ businesses to score high-quality leads and drive high business growth regularly. Here’s how data quality improvement propels the growth of any business.
- High-quality leads and high business growth: With accurate data, businesses can score high-quality leads, drive high business revenue regularly, and maintain their competitive advantage. High quality leads also ensure that businesses can target real prospects with the help of clear messaging and branding strategies.
- Improved decision-making: Accurate data also enables businesses to make informed decisions and optimize their operations. By leveraging data insights, businesses can identify opportunities for improvement, reduce costs, and increase efficiency.
- Measure ROI of marketing efforts: High-quality data enables businesses to measure the ROI of their marketing efforts accurately and optimize their campaigns for maximum impact. By tracking conversion rates, click-through rates, and other performance metrics, businesses can quickly identify which marketing channels are most effective and adjust their strategies accordingly.
- Personalized customer experiences: Accurate data also ensures that businesses can deliver personalized experiences to their customers and improve customer engagement and boost customer loyalty. By leveraging data insights, businesses can tailor their communication, marketing, and service offerings to meet the unique needs and preferences of each customer.
Future of b2b database cleaning solutions
As businesses continue to gather more data, one can expect the future to feature better, cutting-edge data cleaning solutions to address data decay effectively.
- Rise of predictive analytics and AI: One of the most exciting advancements in this space is the rise of predictive analytics and AI-based tools. These tools will soon be crucial in identifying and resolving data decay issues rapidly. With these tools, businesses can quickly and accurately assess the quality of their data, identify areas of concern related to data decay, and take proactive steps to nip the problems in the bud.
- Use of statistical modelling techniques: Enterprises are beginning to rely on statistical modelling techniques including linear regression, random forest, and neural networks to address data decay issues. These models effectively analyze and integrate data, identify patterns and trends, and make informed decisions based on the derived insights.
Data decay is a silent threat that can hinder the growth and success of B2B businesses. The deterioration of data quality over time leads to outdated, incomplete, and inaccurate information, impacting decision-making processes and customer satisfaction. However, by understanding the types and consequences of data decay, implementing data maintenance and enrichment strategies, and adopting smart data governance policies, businesses can overcome these challenges.
By eliminating bad data and embracing accurate data insights, B2B businesses can propel themselves towards consistent success. Overcoming the challenges of data decay ensures effective communication, efficient marketing efforts, and improved customer satisfaction. With accurate and reliable data, businesses can make informed decisions, foster trust with clients and partners, and stay ahead of the competition.