The importance of data in today’s modern world is becoming clearer with each passing day. Organizations are creating, storing, gathering, and managing more data than ever before. The world is said to be in the Information Age as we manage big data and analytics projects. And all of this is inclined towards more cognitive processing with machine learning and AI capabilities that rely on the mounds of data we collect and manage. Bad Data exists in a number of forms. Incomplete, inaccurate and empty fields within a database or Customer Relationship Management (CRM) platform is a form of Bad Data. Many are aware that Bad Data exists, however, few organizations realize the high costs involved with ignoring it.
What does Bad Data mean?In this era of constant changes, organizations fail to realize that good data is an essential element in successful sales campaigns. Change is constant in modern business, which means that data has to be constantly updated in order to stay valid and even useful. Data tends to get bad or of no use when there are no updates made in different instants. These include changing roles within companies, changed phone numbers, taken additional responsibilities within their current companies and more. The quality of data becomes bad when there are defects, is irrelevant, non-comprehensive without any minute details mentioned and is hard to interpret. The existence of bad data is considered to be a second leading challenge with Big Data.
Common data fields responsible for consisting bad data:
- Wrong phone number
- Outdated physical address
- Wrong title or job function
- No phone number or email address
- Wrong email address
- Misspelled or incomplete company name
Repercussions of Bad DataThe bigger the data, it appears, higher are the chances of it being "bad" data. And this bad data is costing companies a bundle, according to research. Bad Data leaves many companies trying to navigate the information age in the equivalent of a horse and buggy. As data volumes soar, a growing chaos of business consultants has been warning of declining data quality, particularly as data siloes grow higher and companies struggle to integrate massive volumes of legacy data into new platforms. Along with a loss to the company internally, customer satisfaction with a company's store, website, or product will certainly be lower if the data quality is poor. Furthermore, executives tend to make improper decisions when they are based on the wrong information. If the data is not accurate, many organizations fail to properly manage it, there are severe consequences, including significant fines and more.
Solutions for Better DataData governance encompasses the people, processes, and procedures to create a consistent enterprise view of a company’s data. This increases consistency and confidence in decision making, decreases the risk of regulatory fines, and improves data security. Allowing a separate, dedicated team to manage sales and marketing data is one reliable option for keeping the data clean.
Ways in which data can be cleaned:
- Verifying contact data and immediately updating the same in the central database
- Identifying projects that targets are working on
- Identifying and explaining the decision-making structure to employees
- Refreshing contact data once every 90 days
- Using Sales intelligence to provide insight into top prospects of companies.
Key Takeaways from 'The High Cost of Bad Data'
- Ensuring that the executives at organizations recognize the data quality problem and endorse the need to rectify it.
- Making the whole organization realize the effects of bad data quality.
- Procuring and implementing a data profiling tool to help for showing the existing state of organizational data.
- Considering sales intelligence as a strong factor to manage and mend data.