5 Key Data Conversion Challenges Faced by Small Businesses
Changes in business and new developments in technology are both constants in the business world. That means that we always have to develop and adapt our strategy to meet these changes. The key is to be on top of new technology. However, one of the consequences of ever-changing technology is that data systems are always changing as well so the format of data is all over the place. The most common area we can see these changes in data format is the medical field. You’ll find records scattered around dozens of formats.
My point is that small businesses face a number of data conversion challenges when they start dealing with large amounts of data. Efficient data conversion is a vital part of turning raw data into useful information. Before we look closely at those challenges, I want to take a quick moment to explain what proper data conversion should accomplish.
- It should convert data from its source format into a version that’s appropriate for a business.
- It should convert the data accurately.
- The data must work in its new database.
- The data must be high quality after transfer.
With that in mind, here are 5 of the common challenges that small businesses will face.
Challenge #1: Planning
In order to successfully convert data, small businesses will need to plan ahead. Start by defining the project’s boundaries by asking the following questions:
- What is the current format of the data that needs to be converted?
- Is the current data high quality?
- Does the data require partial conversion or full conversion?
- What part of the data should I move to a new database?
- What part of the data does not need to be moved?
- What formats do I need the data to be converted to?
- What steps are required to convert the data?
- Will the data be converted just once or will you need to schedule regular conversions?
These questions and their answers should be in writing so that you can share them with the appropriate expert.
Challenge #2: Lack Business Engagement
There are times when an individual within the business doesn’t understand just how important it is to convert the data. It’s essential that you explain the importance of the conversion to those people. That’s one way having a written plan will come in handy! Sit down with these associates and explain how important the quality of the conversion is to the overall business. Data conversion is a critical task so it’s important that everyone within a business understand its importance.
The only way that we can guarantee employee engagement is to understand what it is that they want. Aside from more money, most employees want a work environment with clear communication where they are able to approach their manager without fear of backlash. By improving their overall engagement, you will have a much better chance of convincing them to follow new business processes without hassle.
Challenge #3: Defining Data Quality
Defining the overall needs of your business from a data conversion standpoint is only the first step. Small businesses must also define the quality standards for the business as well. Once you have set the goals for data quality, then you will need to find ways to effectively measure and track it. But you need to understand exactly what it is that makes data useful for your specific business.
When data is migrated from one system to another, anomalies and duplicate data tend to both become an issue. This creates a lot of meaningless data that clutters up the system. Small businesses tend to try working around it, rather than fixing the problem. So they end up making their lives much more difficult. Fixing this problem is easier said than done, which brings us to the next challenge.
Challenge #4: Profiling and Cleansing Data
The next challenge is to make sure that the data is properly profiled and cleansed during the conversion process. Small businesses can put procedures into place that help make this process easier. These procedures will ensure that your data conversion is done correctly.
The solution is data validation. You must put a system into place that can be used to verify all data sent between the destination and target systems. There are three main types of data validation methods used:
- Sample Data Validation
- Subset Data Validation
- Complete Data Validation
Complete data validation is the best method because it compares all data records. The problem is that it requires a lot of time and resources. It’s usually best to outsource this task to a third party.
Challenge #5: Data Management
Small businesses struggle with duplicate data. They have different departments reporting the same things so they end up with the same records. This is a challenge that must be overcome. Duplicate records lead to incorrect transactions and unreliable reporting. Find a way to consolidate the way your records are put into the system. Make sure that each department is using the same database.
As you dive deeper into your data strategy, you need to consider:
- Data Policies
Smarter data management can transform your small business into an efficient overachiever.
What are some of the biggest data conversion challenge that you have faced?