Data has become an important part of every business. Using data, businesses can make decisions that can help them better appeal to their audience, scale up their business processes and overall meet their desired goals. The importance of data has become known to many business owners. There is now a need to ensure that the data businesses use to make decisions using data automation is indeed top-quality data collected from the right target sources. Seeing as data is being validated, generated and exchanged between several systems and processes at every stage, there is a need for a guarantee of the consistency and accuracy of generated and extracted data.

To ensure consistency, accuracy and data integrity, Zenserp ensures that individual data values are collected according to a specific data model while also taking into account other factors like definitions, dates, business relations and others.

There are several setbacks that can affect the quality and integrity of data, and these stumbling blocks may include:

• The use of multiple business intelligence tools by organizations can affect data facts
• Business organization data may lack uniqueness based on the sources it has been collected from –intranet, public files, and others.
• Companies with haphazard data workflow may lose data when it is not properly collected, analyzed and stored.
• There may be errors due to human mistakes and these may significantly affect the integrity of collected data.

With the above-listed problems and many more facing the data collection and web scraping industry, some best practices can be adapted to overall boost data integrity. Below are some of the recommended ways of improving and maintaining data integrity.

1. Data Maintenance and Cleaning

Bad data can significantly affect the quality and integrity of collected data and this is why it is essential for web scraping and data collection companies to pay attention to cleaning and maintenance of data.

Companies specialized in data collection should choose a tested approach to data cleaning which ensures that bad data are easily detected and removed or in some cases, corrected for increased data integrity.

Companies should also pair up their data cleaning approach with a maintenance approach which ensures that the system health is continually monitored and maintained to prevent data discrepancies

2. Streamline Data Source

One of the most common factors contributing to discrepancies in the integrity of collected data is the source of the data. In most cases, companies choose multiple sources for data extraction. However, these sources may have conflicting data even for the simplest of questions. To prevent this conflict which may affect the integrity of the collected data, it is recommended that data collection companies should choose a single and reliable source for all data collection needs.

By choosing a single and reliable source, the chances of discrepancies are greatly reduced. This also means that data scraping companies will spend less on data cleaning and maintenance.

3. Training and Liability

Data entry is an important part of data collection and this is why it is essential that adequate training should be offered to data entry staff to ensure that they are doing the right thing at the right time.

Manual data entry may lead to certain types of errors due to human nature, and this may overall compromise the integrity of the data being collected. To reduce such risks, companies should put protocols in place that address human errors and seek to detect and clean these errors as early as possible.

To ensure an all-around successful training, it is also recommended that companies adopt an easy to understand, hands-on approach to training while also ensuring that staff is not overwhelmed with large training resources within a short time frame.

4. Define Data

One of the common causes of errors in data collection or data entry is the lack of clear cut terms defining each set of data. It is recommended that data should be clearly defined. If possible, each client should have a well-defined data need and how their metric is to be calculated. This is bound to significantly reduce errors.

5. Automation

Humans are prone to errors and this contributes to the decreased integrity of manual statistics. In fact, human errors contribute largely to a decrease in data integrity. A great way to combat this would be to have an automated system in place that handles common processes like data addition, statistical analysis, and others.

By reducing the involvement of humans in the calculation aspect, the chances of calculation errors are greatly reduced and data integrity is boosted.