Tech giants such as Amazon and Facebook are mining data to get valuable business insights. Graziadio Business Review has written a detailed article on Facebook data mining. The social media site’s successful utilization of big data is one of the reasons it’s recent quarterly earnings topped $21 billion.
However, large corporations aren’t the only ones leveraging big data. As a matter of fact, almost all successful companies are using data analytics to get their hands onto useful information. The legal industry appears to have lagged most other professions in leveraging big data. Law firms are the last ones to enter the golden world of data mining, which is a shame. The Wharton School at the University of Pennsylvania wrote that getting law firms to use big data will be the next major challenge. The authors pointed out that big data in the legal profession is still in its infancy.
Law and the legal industry shouldn’t rely exclusively on the power of argumentation, but it should embrace change with arms wide open. By taking advantage of sophisticated technologies, they can predict the outcome of lawsuits and even win cases right after the notice to sue arrives. For the time being, data analytics remains a new practice in the legal field.
Success in the courtroom is guaranteed
Big data has many practical applications in the legal field. If you are still skeptical, then you may want to consider the following hypothetical scenerio.
Imagine the following situation: you’re a lawyer and you’ve been recently been contacted by someone from Houston, namely the victim of a truck accident. The incident wasn’t fatal, but the person has sustained a head injury, therefore, losing some of their motor functions. If it wasn’t enough that the medical bills are extremely high, the poor person has lost their livelihood. As a Houston truck accident attorney, it’s your responsibility to do your client justice. Thanks to big data, it’s possible to streamline the legal process. You can collect information on individuals suffering similar accidents from the applications that are stored and organized in online systems.
The amount of data available to law firms and insurance companies alike is astonishing. There are medical bills that can serve as evidence in a court trial, not to mention the statements collected from the victims, which describe the emotional, physical, and financial impact that people other than the client have suffered. The law and legal industry should understand once and for all that data and technology will touch human analysis. Legal data analytics benefit all legal practices, offering lawyers a way to gain a competitive advantage in any setting.
Caution should be exerted when mining data
Data mining is the process by which someone looks for hidden and useful patterns in datasets. It’s all about disclosing unknown relationships. Attention needs to be paid to the fact that there can be biases in data, so it’s necessary to deploy a fair and unethical formula. Big data models should follow an ethical lead. The second issue to consider is privacy laws. With the technologies available at present, it’s easy to find information and relationships about people based on the extraction of data. The methods for mining data should allow for complete individual privacy.
The bottom line is that it’s paramount to ensure a legal footing for all the data endeavors. The legal professional should get a clear sense of the problem they’re struggling to solve before diving into the matter. The rapid development of machine learning and data mining has brought forward new opportunities for processing legal materials and legal analytics. But let’s not forget about the potential challenges. It’s important to follow the law and do things right. Otherwise, there isn’t any problem.
Any data lake design should incorporate a metadata storage strategy to enable business users to search, locate and learn about the datasets that are available in the lake. While traditional data warehousing stores a fixed and static set of meaningful data definitions and characteristics within the relational storage layer, data lake storage is intended to support the application of schema at read time with flexibility. However, this means that a separate storage layer is required to house cataloging metadata that represents technical and business meaning. While organizations sometimes simply accumulate content in a data lake without a metadata layer, this is a recipe for an unmanageable data swamp instead of a useful data lake. There are a wide range of approaches and solutions to ensure that appropriate metadata is created and maintained. Here are some important principles and patterns to keep in mind. Single data set can have multiple metadata layers dependent on use cases. e.g. Hive Metastore, Apache Glue etc. Same data can be exported to some NoSQL database which would have different schema.
Once you have the raw layer of immutable data in the lake, you will need to create multiple layers of processed data to enable various use cases in the organization. These are examples of the structured storage described earlier in this blog series. Typical operations required to create these structured data stores involve:
- Combining different datasets (i.e. joins)
- Cleansing, deduplication, householding
- Deriving computed data fields
Enforce a Metadata Requirement
For some specialized use cases (think high performance data warehouses), you may need to run SQL queries on petabytes of data and return complex analytical results very quickly. In those cases, you may need to ingest a portion of your data from your lake into a column store platform. Examples of tools to accomplish this would be Google BigQuery, Amazon Redshift or Azure SQL Data Warehouse.