Critical Data Problems and How to Solve Them
Business data is the lifeblood and the center of decision making of a company. This valuable asset can help you to personalize and customize your marketing messages efficiently, provide background for your sales team when they interact with potential prospects, and assist you to support and better serve your customers. According to Gartner in their survey of a wide range of companies, data quality can cost businesses over USD 14 million a year. And as people are more and more connected today, the problem with data can become very critical and cost companies exponentially. However, data quality has not yet become the main focus for companies nowaday, since too much attention is still paid to collecting more data. By fixing data issues such as missing and incomplete data, inconsistent formatting, and inaccurate data can help companies make better decisions therefore reduce the cost of making bad business decisions.
Get a holistic view of your business and avoid missing or incomplete data
Making a data-driven decision requires high-quality data. Utilizing data without knowing its origins or how it was obtained can be very misleading and do more harm than good for your company. However, if you check on your customer data now, there is a high chance that you find missing and incomplete data. One way to fix this issue is to invest in data enrichment. This process will allow you to fill the missing pieces and obtain a more accurate picture of the context by gathering high-quality data from other sources. Another way to reduce incomplete data is by increasing the number of required fields in your system.
Establish a system to prevent inconsistent formatting
One of the most common issues in big data management is having inconsistent formatting of data in the database. Fields like addresses, zip code, phone numbers, are very common to vary in their formatting. Different capitalization standards also often bring issues to inconsistent formatting that later on can lead into misleading data analytics. Implementing appropriate restrictions in data input as well as system validation can help prevent data inconsistency. If you want to fix the inconsistent formatting for data that is already in your database, investing in data cleansing software can help you solve this issue.
Validate inputs and outdated data to reduce data inaccuracy
Based on an analysis by Integrate, 40% of leads contain inaccurate data. On average, companies assume that up to 33% of their customer data is somehow unreliable. This incorrect information derived from inaccurate data can lead to misleading decision making that is not good for your businesses. To fix this problem, it is important for companies to invest in the data enrichment process, because of its ability to identify outdated and inaccurate data.
Implementing strategy to clean and manage critical data problems such as incomplete, inconsistent, and inaccurate data can help companies reduce the risk of making misleading decisions, and therefore avoid unnecessary cost derived from bad data. Implementing Big Data Indonesia with low quality data can harm your businesses, including jeopardize your sales and marketing process, impact your brand image, and affect your customer services. Being able to identify critical data problems is the first step to actually solve your business problem.