Three Harmful Data Types To Every Business
In 2018, there are as many as 2.5 quintillion bytes of data processed. This data is growing more every year and expected to blow to as much as 175 zettabytes in 2025. This is big data, and it is easy to contaminate with dirty data. Bad data quality can come from inaccurate, incorrect, or misleading data. Duplicates, data that violate rules, and misspelled and badly punctuated data is still dirty data. Apart from dirty data, there’s also dark data and unstructured data. Dark data is information not in use, while unstructured data is information not prepared for use.
Mining all these data is impossible, if not extremely costly. Data outsourcing is crucial in handling all three data types. Businesses across different industries need to know how to process this data to improve business processes.

What Leads to Dirty Data?
With the existence of dirty data and bad CRM data, a question remains: how does it proliferate? The biggest challenge to dirty data proliferation is human error. How can bad data entry pollute a data pool? Inaccurate, incorrect, misleading data compounds with duplications, data that violate rules, and mis-spelled/mis-punctuated data. When building data at a BPO level, there are ways to address data quality upfront. Most BPO organizations and developers can provide tools that provide accurate data merging. Even then, data outsourcing would still need to work on areas impossible to automate. These include regional spellings, duplicates, date-time formats, and word cases.
With that in mind, it’s crucial to execute clean data practices. Systems like the Internet of Things (IoT) use trust, identity, and chain of custody to make sure data is clean. Trust confirms that devices are talking to the right devices and systems. Identity confirms that upcoming data has correct parameters, and the right device sent it. Chain of custody creates an understanding of the history of its data points. Data still runs by the law of garbage-in, garbage-out. Good data entry needs to happen to prevent dirty data from contaminating data quality.

Steps on Cleaning and Structuring Your Data
So, how does a system make sure it gets clean data? There are three steps in creating clean, structured data with clear potential. These are:

• Standardized tracking and recording of datasets
• Clear data addition and integration
• BPO workforce planning

First, businesses need to create an internal standard of sorting, cataloguing, and managing data. Companies need to have a sense for the gold standard for data quality. Data outsourcing needs to anticipate potential regulations, laws, and processes with which the team needs to comply. Once the standard is ready, disseminate the information and educate employees from top to bottom. Next, CRM data integration needs to focus on processes crucial to profitability. Every layer of the company’s tech stack needs careful examination for what gives the most ROI. This can be the speed of processing, accuracy, or even volume of data.
Once the company pinpoints this process, stack alignment follows. The tech stack needs to address data quality issues and allow business process outsourcing to handle the data. Once everything is ready, the company needs to plan how its workforce will process the data. Anything that the tech stack cannot automate will go to the human labor team. These will include quality control, technology gaps, and exceptions. The structure of the BPO team would need to have core teams that handle everything. There should also be teams that specialize in handling functions the core team cannot.

How Can You Improve on Data Quality and Solve Dirty Data?
To resolve the issue of dirty data, the best answer is to find a business process outsourcing partner that does the job well. It offers many different advantages that are hard to deny. First, in-house teams are effective in improving data quality. Even then, the sheer cost and operational needs of such a team kill profitability. This part is where a data outsourcing company comes in to help. Business process outsourcing or BPO can process massive, consistent data pipelines for large companies. Enterprise businesses can clear CRM data en masse, with very little to zero loss in data quality. The right BPO team can provide the level of agility an enterprise needs.
In the long run, the goal should be to build machine learning capabilities to handle structured data. A machine that can learn from human expertise can make data crunching faster too. There are many ways to teach machines how to utilize structured training data and help them learn. These can come from public datasets, pre-trained models, embedded labeling tasks, and combining free data with company data.

Assivo Knows How To Handle Your Data
If your company wants to mine as much dirty data as possible and clean it, it’s crucial to know proper strategies to use it. Clean, structured, visible data is crucial in helping businesses achieve explosive growth. Companies need to have a standard of handling CRM data, improving data quality, and proper data entry. A company needs to know what it needs to boost profitability and streamline the process. In that regard, Assivo can help. Assivo provides reliable, cost-effective business process outsourcing. Data entry comes not only from managed offshore teams that ensure data quality but process automation too. Assivo brings the best people together with the best standards for data entry outsourcing. Every process used is robust, delivering accurate, correct, and usable data. Whatever your company needs to grow, Assivo can provide cost-effective solutions, from research and data collection to monitoring competition. Assivo is there to provide only the best quality support that any business would need.
Get in touch with us today and find out how you can utilize our core services to your advantage.