As a business owner, it is inevitable that you will be dealing with data. Data is one of the essential currencies that drives the business world. Whether it be sales figures or stock trends, a good part of doing business involves dealing with data. This is made even more important by the fact that we are in a digital world and so much of business is conducted through the online space.
This, however, means that there is much more ease of data collection than ever before, whether this is regarding internal affairs, customers, supply chains and so on. Needless to say, the issue of data cannot be ignored by any business owner. However, it must also be acknowledged that not all data is valuable and one of the biggest issues your company can face is having to deal with bad data.
What is bad data?
The term bad data might initially sound a bit vague as businesses are often told to avoid making use of bad data but do not always know what it is. Simply put, bad data refers to data that is inaccurate for a business. This inaccuracy does not simply mean that the data is false—true data can also be bad data.
Bad data could include data that is missing key elements, data that is not relevant for the purposes it is to be used for, data that is duplicated, data that is poorly compiled and so on. The use of bad data by a business can significantly affect the performance of the business and in some cases, prove catastrophic altogether.
Businesses are often cautioned about their data collection and management practices as these can be just as important as the actual product or service that is being marketed to the public.
The reasons why bad data so harmful for businesses vary with the type of flaw within the data itself.
- False Information: Data is often compiled by businesses to give them a greater insight into the current workings of their industry or customer base. Naturally, if the data that is compiled by a business contains false information, the business’ purpose has been defeated. For example, should a business wish to carry out a survey about customer behavior towards business practices and the feedback received is false information, the entire purpose of the survey has failed and the business will not receive the information that they are seeking.
- Incomplete information: One of the qualities of good data is that it is complete. This is because data is multi-faceted and often, one single piece of data and one aspect cannot give the full picture. For example, a business may decide to carry a customer survey to determine reactions to certain business practices. The data provided might tell them what the current customer attitude is towards these practices but may not tell them why these attitudes are forming. In this case, only a small part of the bigger problem has been solved. Should you find yourself dealing with incomplete information, your business could go on to make decisions based on only a part of the whole story and this can lead to a waste of time and resources as a result.
- Irrelevant data: Few things constitute a waste of time and resources for a business more than gathering data that is irrelevant to them. This could be data that relates to a completely different field than your own, data that does not properly target your business sector and data that is too vague in nature.
The reason why bad data can affect a business performance can be found in the purposes of the data itself. Oftentimes, when a business decides to compile data, they are doing so with the view to making decisions based on the information collected.
Businesses often carry out data collection before the new products are launched or before new investments are made. This means that the quality of data that is collected is directly tied to the decisions that a business makes in the short term and in the long term. This is not limited only to decisions made with regards to putting out products or investments but also with internal decisions. Internal data can be collected within an organization and internal policy is formed based on this.
If a business makes decisions based on false data, for example, it will likely end up wasting their own time and resources. The same rings true for incomplete information or data that is irrelevant. Business resources are finite and business will not last long if they continue to make decisions repeatedly based on bad data.
So what is the solution?
1. Straight to the source
Dealing with bad data often starts with going back to the source of the data. It is not unusual for the data quality to be poor because it is being sourced from the wrong places. Look into what the end goal of your data is and determine whether your data sources are truly accurate.
If your organization wishes to make use of data to review its internal policy, make sure you are collecting data from the relevant parties within the organization and outside and that irrelevant factors are not skewing results.
You should also make sure that your current data extraction techniques are accurate. For example, your business might be compiling qualitative data rather than quantitative or making use of open-ended response forms rather than close-ended ones. These small errors could go on to affect your data quality negatively.
2. Finding your data type and refining your process
Even if your data source and extraction techniques are correct, it is possible that the data your organization is seeking out is not relevant. If your business is consistently suffering from poor data, it would be in your best interest to go back to the drawing board and review what type of data you actually need. If your business has consistently made use of a certain type of data such as sales figures, consider looking into a different type of data and comparing results.
Beyond this, it is important that your business finetunes its data processing technique to remove the margin of error. This could mean double-checking data sources, having third parties review the data after collection or processing the data through multiple methods to get the best results.
3. That’s where data management comes in
It is not enough for businesses to understand just how damaging bad data can be to their practices and performance but it is also up to them to ensure the data is properly managed, collected and processed. Oftentimes, when businesses choose to carry out their own data collection and management processes, there is a certain level of bias involved. This is because businesses are not always objective enough in data collection and should the data be managed internally, there is a greater margin of error. It would be beneficial for most businesses to outsource their data management needs to third parties.
This process will involve the third-party analyzing the business’s current practices and needs to determine what sort of data should be collected. This prevents the collection of irrelevant data on behalf of the business. The third-party will then collect the data as well as analyze it and will ensure that the data is not poorly recorded or incomplete.
More importantly, as the business grows and expands, their data collection needs will change and the sort of data that is relevant to them will evolve with time. The third-party will, as a result, adjust the data collection and management process to make sure that they are consistently meeting with businesses’ needs at any given time. This means the data collection will be more relevant, complete and cost-effective for all parties involved.