Data has become the driving force for organisations worldwide when it comes to digital transformation and implementing smarter business operations. Since this information influences every aspect of decision-making for companies, it’s crucial they have good data hygiene. This is particularly true with regards to financial data.
Data hygiene is maintained by storing your data appropriately and ensuring it's up-to-date and accurate. Data migration is the activity of moving your information from one system to another, thus storing it in a new location. But many are perhaps blissfully unaware that data cleaning should be an essential part of this process.
If your data contains inconsistencies or errors, any analysis you conduct in the new system will be flawed. And when decisions are based on these insights, things could start to go wrong very quickly. For instance, in marketing, poor insights could lead to money being wasted on campaigns that don't reach the right people. In fields like healthcare, the wrong information will inevitably have even more of a consequential impact.
If you’re part of a finance team that is making the leap to a new accounting software solution, it’s important to perform data cleaning before, during, and after this switch.
Some business leaders will see technological innovation as a silver bullet for all their problems. But any technology is only as good as the information it utilises. This is why preparing accordingly for this transition (and getting your existing structures in order) is key for getting the utmost from this kind of investment.
Why cleansing data before and after migration is important
In our annual Business Trends Report, 24% of respondents said access to accurate data and information is their biggest challenge, and 35% said they’re planning to move to an off-premise data centre and onto the Cloud.
Data cleaning (often called ‘data cleansing’ or ‘data wrangling’) is an important step before this type of migration. This process involves preparing and validating data before going ahead and mining it for actionable wisdom. It is more than just removing incorrect data. However, a lot of the activity does focus on detecting inaccurate information and correcting it as needed.
It’s arguably just as important to cleanse data after migrating to a new system as it is before. When migrating the data across, there’s always a slight risk some information could be corrupted or lost. Cleansing afterwards helps to identify and correct these instances.
There’s also a chance some of the financial data that has been moved could be duplicated or redundant, so it provides an opportunity to rectify this too. The new system may have different data requirements and formats to the previous accounting solution, so it’s important to iron this out.
Not only this, but there’s also a chance the data will now be structured/stored in a way that isn’t compliant with the latest financial regulations as a result of the migration process. If this is the case, the data will need to be reordered in a way that is compliant, which a data cleanse will make easier.
The aim of data cleansing
The value drawn from data analysis rests firmly on the quality of information which it is based. Our more sector-specific Finance Trends Report found that 27% of employees believe the data in their financial management system isn’t completely accurate.
Companies are prone to losing a substantial amount of revenue due to dirty financial data. Data cleansing assists with generating consistent and structured data that can inform more intelligent strategising. It also identifies weaknesses and potential improvements around aspects like data-entry and storage ecosystems. This goes a long way to boosting efficiencies which save time and money both now and in the future.
The first step of data cleansing is to classify the cleansing rules into those that should be performed manually and those that should be automated. By categorising the rules, the domain experts of the organisation can focus on the manual data cleansing process, while migration experts can design/develop automated solutions for data cleansing.
Manual cleansing is typically performed before data migration, whereas automated cleansing may be performed prior to or as part of the initial data migration phase.
During the cleaning process, data verification ensures the information is available, accessible, complete, and in the proper format. Once the migration has started, businesses can continue to verify the details to ensure they’re optimised before each stage.
Data impact analysis is another essential component of data cleansing. While data cleansing adds or changes values, data impact analysis ensures these changes do not adversely affect other elements in the source and target systems.
It also analyses the effect of cleansing on other systems that currently use the same information (and those that may use it once the migration is complete). Therefore, cleansing doesn’t just help to eradicate duplicate, incomplete, or dirty data, it also ensures data sets are merged in a consistent and competent manner.
How to prepare clean data
Data cleansing is one part of data preparation, which is a more extensive and longer process. This revolves around cleaning and transforming raw data before processing and analysing it. It can involve reformatting data, making data corrections, and merging datasets to enrich the figures and give them more context.
While the specifics of preparing clean data can differ depending on the industry, organisation, and necessity, the workflow remains mostly the same. This includes:
Finding the correct data is the first step in the preparation process. An existing data catalogue or new data source could be ways of getting hold of the right information.
Explore and evaluate data
After collating the right stats, it’s critical to explore each dataset. This step is about familiarising yourself with the data and understanding what needs to be done before it can be used for a specific purpose. Understanding the relationship between datasets is crucial during this phase because making amendments in one place could corrupt information in another.
Verify and cleanse the data
Data cleansing has traditionally been the most time-consuming part of the data preparation process, but it is critical for removing errors and filling in gaps.
Removing unnecessary data and anomalies
This phase starts with understanding different types of errors and interrogating them via the appropriate methods. Eliminate unnecessary stats from your dataset, such as duplicates and irrelevant observations. Duplication is most likely to occur during data collection.
Duplicate data can also emerge when you combine data sets from multiple sources, scrape data, or obtain it from clients or multiple departments. One of the most important aspects to consider in this process is deduplication. Irrelevant observations are those that do not fit into the specific problem you are attempting to analyse. You can also use SQL commands to search for additional errors and use fit-for-purpose data cleanup tools.
Fill in the data gaps
Data gaps appear when there is missing information. Many algorithms will not accept missing values, so you cannot ignore them. There are several approaches for dealing with this, which might not be ideal, but must be considered.
You can remove observations with missing values at the risk of losing information. You can also input missing values based on what can be seen in other observations, which may compromise data integrity. Altering the use of data to navigate null values is a third option.
Put data into a standardised pattern
Personal data may be spelt differently, outdated, or incorrect. With data standardisation, it is possible to address this issue. It is also easier to link all distributed data together when the input data is well-defined and segmented into many smaller output fields.
After cleansing, the figures must be validated by looking for mistakes that may have occurred during the preparation process thus far. During this step, errors in the system often become apparent and must be fixed before moving on to the next step.
Modify and enrich data
Data transformation is the process of upgrading the format or value entries to achieve a specific result (or to make it more understandable to a broader audience). Enriching data means adding other elements to it to provide deeper insights.
Once the data has been prepared, it can be stored or sent to a third-party application (like a Business Intelligence tool or Cloud platform) for further processing and analysis.
Migrating your clean data to the Cloud
Data preparation improves the quality required in data science, analysis, and other data management tasks. While data cleaning is important, it is also time-consuming and may require specific skills.
However, Cloud applications (and the experts who provide them) make the data migration process much easier with minimal intervention. With access to Machine learning (ML) capabilities, data scientists and business analysts can concentrate on data analysis rather than data cleansing.
Cloud solutions also allow finance professionals with no IT expertise to manage and analyse financial numbers rather than wasting time or energy on IT maintenance. However, businesses will still need a solid Cloud migration strategy before Cloud adoption to scale their business capacities and stay on track with digital transformation goals.
It is essential to understand there is no one-size-fits-all approach to the migration process. Every organisation will go through unique Cloud migration steps based on their specific ambitions, requirements, and budget.
How can we prepare your data for Cloud migration?
At Advanced, we’ve helped countless businesses to successfully move their data to a new system when they have felt ready to embrace the latest innovative technology. As discussed in this article, companies must play an active role in getting their affairs in order, but we work closely with them to find a tailored approach that makes the migration as smooth and as seamless as possible.
Our Cloud-based accounting software, Advanced Financials, ensures they have the best chance of keeping their financial data at a high quality once they have cleaned it and migrated it. It also helps them to draw the most value from this information. With powerful financial reporting and analytics capabilities, they’re able to gain helpful insights that drive business strategy.
With all the functions of the finance department in one digital location, there is a single version of the financial truth (rather than using the risky strategy of consolidating data from many separate spreadsheets/platforms). This cohesion facilitates automation, which reduces instances of errors, and gives finance teams more time to look at their data in depth.