Companies rely heavily on relevant data to facilitate efficient decision-making and forecasting. It is essential to clean and glean data in order to maintain a standard and relevance while presenting information across different departments and stakeholders within an organisation. One of the recurring challenges organisations globally face is poor maintenance of data hygiene.

Poor data quality can hinder productivity, increase maintenance costs, and cause system outages. According to IBM’s recent market survey, “dirty” data costs the U.S. economy $3.1 trillion annually. 

Adopting upgraded data-cleansing methods can help glean useful information from datasets and eliminate said challenges. ‘Cleansing’ refers to removing redundant data points in a dataset and is the first and essential part of data collection. 

Companies are steadily turning to data-cleansing methods to extract useful information, focus on the target audience, streamline marketing strategies, and optimize lead generation. 

In this blog, we’ll break down the key data-related challenges most organizations face and how data scientists overcome them with data-cleansing techniques. 

Importance of Clean Data in Planning and Decision Making: Why B2B Data Cleansing Is Essential

Data cleansing can help eliminate typographical errors to validate and enhance data. It ensures that the final presentation of the data meets the following quality criteria:

• Validity
• Completeness
• Accuracy
• Uniformity
• Consistency

Data scientists must be stringent about maintaining data hygiene, keeping it well-formatted, and removing irrelevancies before use to ensure data integrity. The significance of clean data stands out when valuable insights are redeemed through plans regulated under high-quality, relevant data. 

Decision-making assisted by accurate, uniform, and complete data has the potential to reap optimum results for any organization, paving the way for its unprecedented growth. 

By implementing data cleansing methods like SQL queries in MS Excel or Python scripts and data-cleaning automation tools like Trifacta, data scientists redeem information that fits changing trends and evolving customer demands, improving data implementation towards high-quality decisions. 

Clean Data, Better Outcomes: Achieve More With Data Cleansing

Hygienic and structured data evades poor leads and allows companies to develop optimized prospect lists. This enhances marketing efficiency and helps reach the target audience, leading to higher engagement and growth. 

Here is a list of the key challenges that business enterprises face and how data cleansing solutions can help tackle them effectively.

1. Understanding the Target Market

Challenge: If the “target” market does not show interest in buying the products or services offered by a company, they have targeted the wrong audience. For instance, children and teenagers cannot afford expensive toys, garments, or gadgets and depend on their parents to purchase them. Hence, the marketing strategy is bound to fail if the messages only appeal to teens or children and not their parents, who have the actual purchasing power.

Solution: Understanding the target audience is essential for marketers and businesses to implement relevant strategies. Effective market reporting and analysis make cleaning data easy and keep the marketing leads database hygienic. Data cleansing helps generate insightful business intelligence, which, in turn, promotes

• Enhanced marketing techniques
• Up-to-date and viable reports 
• More efficient and useful analytics

2. Keeping Track of Expiring Contacts

Challenge: Customer contacts comprise valuable data for companies relying heavily on marketing for revenue. Like all perishable goods on supermarket shelves, these contacts also come with expiration dates. Studies estimate that nearly 25% of a company’s contact data expires annually. 

Solution: Data cleaning methods like email data cleansing can help monitor and remove expired and already expiring data to leave room for more potential leads.

3. Segmenting and Targeting People With Personalized Content

Challenge: Brands focusing on personalization face roadblocks like expired customer information and incorrect customer details. Many rely on assumptions after a one-time purchase or interaction; others keep no record of a customer’s previous interactions or purchases.

Solution: Data cleansing helps structure data to build strategy and execute new and innovative marketing campaigns efficiently and accurately. Clean and structured data can help build engaging, dynamic, personalized marketing strategies and outreach demands based on the acquired customer info. This may include current website activity, tracking recent purchases, and even significant life transitions (getting married or becoming parents).

4. Handling Inconsistencies

Challenge: Some instances of data inconsistency that most B2B companies face include:

• Inadequate information
• Data values not maintaining specified formats
• Certain data instances contradict the same data objects
• Inconsistent values in data sets
• Data relationships lack integral linkages

Solution: Companies can leverage data-cleansing solutions to identify hidden causes of data inconsistencies. Proper data structuring can help address quality issues by reviewing data conformity, completeness, consistency, integrity, and accuracy.

5. Managing Duplicate Leads

Challenge: The easiest way to make a potential customer lose interest in a company is by copious emailing. Sending someone the same email within a short span of time can result in a loss of trust due to redundancy. Duplicate leads generated by poorly managed or expired data or data entry/collection errors are one of the primary reasons for this.

The result? Potential leads unsubscribe from the company’s mailing list, tarnishing its goodwill and incurring a loss.

Solution: Companies can dodge this bullet with the help of deduplication methods, a part of data cleansing. Email scrubbing is another email data cleansing tactic; it helps clean the mailing list to remove all email subscribers who are not interactive or responsive. 

6. Cutting down on costs 

Challenge: Data inaccuracy significantly impacts company bottom lines. 10-25% of B2B contact databases contain inaccuracies, and poor data quality causes about 40% of business objectives to fail. 

Here’s the 1-10-100 rule to understand what it means to have dirty data piling up:

It costs an average of $1 to verify records, $10 to correct the data errors, and $100 if no cleaning is done. In other words, quality control costs increase exponentially over time because of the failure to clean bad data. 

Solution: Adopting high-grade data cleansing solutions is the simplest way to clean dirty data in a marketing automation system. It helps delete contact data that is irrelevant, unsubscribed, and hard-bounced, ultimately helping to cut down on costs.

Clean data can also maximize profit and revenue, enhance efficiency, boost the morale and confidence of sales teams, and help adopt an improved outlook. 

Conclusion

Data cleansing is one of the best ways to fish leads, identify customer requirements, and eliminate inaccuracies that can result in wasted resources. It is the simplest solution to cut costs, avoid failed marketing campaigns and maladjustment in marketing and sales, to protect the company’s goodwill. 

With Marketboats, brands can optimize lead generation and redeem visible revenue growth through effective lead scoring tactics. Leverage our insight-rich and tech-driven services to skyrocket growth and increase revenue opportunities.

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