How our AI-powered data cleansing tool Digital Eye can save your firm time and money
In today’s dynamic legal market, law firms are no strangers to mergers and acquisitions. With these transitions come numerous data integration challenges, making it imperative for firms to ensure their data is in a good state. Disorganized, incomplete or corrupt data can pose challenges on its own, and therefore maintaining clean data has become more critical than ever before.
Traditionally, law firms have relied on manual data cleansing to ensure accuracy and eliminate redundancies. However, this labor-intensive process is not only time-consuming but also prone to human error. Manual data cleansing is losing firms time and money, especially when there are options (such as artificial intelligence solutions) that work with data faster and far more efficiently. Once data is cleaned and complete, then it is much easier to review it in a precise and structured manner so it can be properly utilized in the firm?s systems.
Understanding structured and unstructured data
Data normalization is a process that establishes uniformity and consistency within a dataset. By standardizing data fields, formats and structures, normalization ensures accurate and reliable information. Implementing data normalization practices in law firms allows for easier data integration, eliminates duplication and enhances the overall quality and integrity of the data.
While automation can serve as a starting point for streamlining data management processes, law firms can go a step further by embracing AI-powered solutions. Artificial intelligence applies sophisticated algorithms and machine learning capabilities that can effectively tackle the challenges posed by poorly maintained data. For instance, AI can automatically sort addresses into proper fields, verify state and zip codes, and even recognize entities such as individuals or companies. This advanced technology significantly reduces the manual effort required, allowing firms to allocate their IT resources more efficiently.
While it?s easy to clean structured data (such as address books, balance sheets, etc?that are organized by rows and columns), unstructured data is much harder to read and difficult to clean up if someone completing manual data cleansing does not know exactly what they?re looking for. An example of this is text documents, emails, social media posts, images or downloaded web pages. Unstructured data presents unique challenges for analysis and interpretation because it does not have a consistent structure that can be easily processed by traditional algorithms. It does not have a predetermined organization or format, and does not fit into traditional rows or columns like structured data.
To overcome the pain points associated with unstructured data, law firms must carefully select the right solution from the outset. By choosing an AI-driven solution that offers features such as entity recognition and intelligent data conversion, firms can mitigate the ambiguity arising from unstructured data and ensure accurate and reliable results. The right AI tool should provide seamless data conversion scenarios, even when dealing with ambiguous unstructured data. Less advanced tools will prompt for a manual response when they hit those roadblocks (i.e. when it is unclear whether a name in a contact list is for a person, street or company. This just creates more work for the IT team, and possibly staff and attorneys as well.
Cleaning data with ease
Once the data is ready and it?s time to start cleansing it, here is where AI can save you time: there are ways to get around having to manually input information when working with a lot of data.
Digital Eye,