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Data cleansing helps an enterprise deal with data entry mistakes that both employees and systems occasionally make. Moreover, clean data helps your organization’s analysts gain access to critical insights for developing and launching new products or services, while accurately adapting to market changes. With contaminated or dirty data that contains outdated or incorrect information, there is little hope of matching customer demands or improving overall productivity. 

Unfortunately, with the growing requests for access to enterprise information, the quality of data can deteriorate at an alarming rate, which can waste time and money. In the world of big data, these threats can result in financial loss in terms of lost opportunities, reduced revenues, customer dissatisfaction, and higher opt-out rates. High-quality data only comes from excellent team training using a strict set of guidelines to ensure consistency and accuracy. 

Cleansing dirty data helps maintain quality and ensures a more accurate analysis to greatly aid a company’s decision-making processes. Only up-to-date, correct data is useful in developing the most effective strategies by demonstrating a true understanding of market needs. In addition to achieving better results in the marketplace, clean data will contribute to the overall long-term success of your business or organization. 

Improved Decision Making to Boost Revenues

Analysts try to identify statistical patterns in a data series based on assumptions made about the nature of the company’s information. By mathematically or judgmentally removing invalid data points from a dataset, cleaning can help to: 

  • Consistently Deliver a Targeted Message – Accurate data helps you hit your targets with correct messaging and reduces time connecting with invalid prospects. Clean data sets also have a positive impact on your business.
  • Protect Your Brand’s Reputation – Audiences do not want to receive communications that are not relevant. Clean data ensures the quality of your brand integrity and protects your reputation for doing business. 
  • Save Money and Reduce Waste – The best ROI comes from accurately connecting with those accounts which have a genuine interest in your message. Eliminating bad data saves money and optimizes your team’s efforts. 
  • Increase Productivity by Time Saved – For employees who follow -up on promotions or a new product launch, data cleansing helps to remove records with incorrect details. This minimizes time wasted on follow-up calls. 
  • Reduce Compliance Risks and Avoid Fines – It is important to keep an accurate account of those who have opted out of communications to ensure prospects without contact permissions are not included. 

The importance of having clean and reliable data for making any type of statistical analysis for any team cannot be over-emphasized. Generally, extreme outliers are easily identified but a data analyst should set control limits for variability as well as rely upon judgmental factors, such as identifying misreported sales data. 

How Polluted Is Your Enterprise Data?

Dirty data in a system’s database is comprised of outdated data, incomplete data, inaccurate data, inconsistent data, unreliable data, duplicate data, formatted data, and even blank spaces. The larger the business, the more likely large amounts of problematic data are being saved rather quickly. Any application of bad data typically results in failed communications both internally and externally and often leads to wasted resources, unnecessary expenses, and an overall loss of productivity. Poor organization and improper data recovery can lead to too much data that is incorrect, inconsistent, or not secure. Unfortunately, no enterprise, organization, or industry has immunity against accumulating dirty data. 

Within every organization, there exists a set of vital business assets and infrastructure which need to communicate and work together harmoniously, in order to achieve the successful delivery of quality products and services. Any sign of a reduced ability to execute strategies, less effective decision making, increased operational cost, or consumer dissatisfaction, should be considered a red flag for identifying poor data quality. The Mycelium suite of data products is all about improving the accuracy and integrity of enterprise data through automation, connectivity, and delivery. Permanent removal of data or the relocation of older data from one environment to another, can be necessary to allow enterprise systems to operate optimally.


Author Mycelium

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