Data is essential for businesses to grow and create engaging customer experiences. However, many companies struggle to keep their CRMs and other databases consistent and clean — leading to misalignment between marketing, sales, and customer success initiatives. Poor data health can also affect brand perception, resulting in lost leads and opportunities.
Data quality can be challenging to maintain due to several factors, including duplicate records, siloed data sources, and a lack of visibility into the data.
In this blog post, we'll discuss the top challenges resulting in low-quality data and provide helpful tools to get your valuable data back on track.
Challenge #1: Lack of Standardization
One of the main reasons data quality suffers is a lack of standardization. Poor standardization practices include:
- inconsistent data entry,
- poorly designed data capture processes, and
- a lack of governance around manual data entry into the CRM.
Poor data hygiene practices lead to duplicate and inaccurate records, potentially propagating throughout the data ecosystem and creating even more chaos.
To combat poor-quality data, establish clear guidelines for data entry and put a system in place to enforce those standards. The solution could be as simple as creating drop-down menus for certain fields to ensure consistent data entry or setting up field-level validation to catch any errors. Additionally, having a dedicated data team or administrator to oversee the data quality process can help identify and fix any emerging issues.
Consider these solutions:
Format data actions: To minimize human error and time wasted on manual data entry, use HubSpot Operations Hub's data formatting engine to automate the process while improving data quality. This tool can standardize abbreviations and incorrect formatting so that your team doesn't have to waste time on manual data cleanup.
Property validation rules: Setting up property validation rules in the HubSpot CRM can save many headaches. For example, requiring that all phone numbers adhere to a specific format means that lead and customer data will always be consistent and accurate.
Challenge #2: Out-of-Date, Inaccurate, or Invalid Data
Data can become inaccurate for many reasons: people relocate, change career paths, or depart from companies; businesses go bankrupt; data deteriorates over time, and so on. Invalid entries into the CRM, like an email or the wrong revenue range, are also typical. When subpar data enters the database, it may cause multiple issues:
- Marketing becomes ineffective when the team fails to connect with individuals who have either left the company or changed roles.
- The sales team may squander valuable time pitching products to uninterested leads.
- Customer success representatives will have more difficulty keeping customers if they can't reach the right stakeholders.
To keep your data accurate and up-to-date, you need to have a process for regularly cleansing your database, like running monthly data quality reports to identify errors, deduplicating records, and updating outdated information.
Consider these solutions:
Data enrichment: Data enrichment is the process of filling in missing data points. This can be done manually by research teams or through data enrichment tools. For example, Clearbit's Enrichment API can automatically fill in missing data points such as job titles, social profiles, and location data.
Data cleansing: Data cleansing identifies and corrects data inaccuracies by deduplicating records, updating outdated information, and correcting errors. HubSpot offers data quality automation tools that automatically capitalize names, fix data properties, and more to ensure cross-functional teams work off clean, accurate, and centralized customer data.
Data deduplication: Duplicate records can cause a host of problems, from distorting your data to making it more difficult for sales and success reps to do their jobs. Avoid these issues by deduplicating your data regularly. HubSpot's duplicate manager tool can help you identify and merge duplicate records so that you can have confidence in your data.
Challenge #3: Lack of Visibility
Another common issue that can lead to poor data quality is a lack of visibility into the data. Insufficient reporting or dashboards, siloed data sources, and general knowledge gaps around where data originates and how it's used can result in inadequate visibility. As a result, data quality issues can go undetected for long periods, leading to significant problems down the road.
To battle data chaos, it is crucial to have a firm grasp of your data sources and their use cases. The effort may include:
- building data quality reports and dashboards to track critical metrics,
- developing data governance policies, and
- coaching teams on best practices for data management.
Consider these solutions:
Data quality reports and dashboards: Data quality reports and dashboards make it easy to monitor key metrics and identify any issues that require attention. For example, team leads may track the number of invalid email addresses in the CRM or the percentage of duplicate records. With HubSpot's data quality command center, teams can track and fix potential properties, records, and data quality issues in one place.
Data model overview: Ops teams can use a data model to understand how their company's data is structured. This representation helps identify data usage issues so specialists can take steps to improve existing processes. HubSpot offers a helpful data model overview tool in beta mode that allows users to understand their data structure, see relationships between different data types, and discover potential problems that could impact the entire organization.
The Takeaway
High-quality data drives alignment between your marketing, sales, and customer success teams. It helps achieve a single customer view, where all teams work with the same accurate records to drive their initiatives toward revenue growth.
Sidestep the frustrations of working with incorrect data by being proactive about data management. To ensure error-free and reliable data, consider using the data quality automation suite in HubSpot OpsHub. The software contains powerful deduplication, data cleansing, and enrichment features that can transform any dataset into valuable insights for your business.
