The frustrations of handling data are one that no one including highly skilled data scientists love doing. It is an endless process which involves handling thousands of datasets which is time-consuming, and any mistakes can be costly as it could affect the integrity of the entire project.
At a time when everything is becoming automated, the largely manual process has been made better with the introduction of artificial intelligence based systems. The current data landscape which has been enhanced by digitization has necessitated the rapid transformation which has redefined business intelligence tools. This modernization of data analytics has bridged gaps which for long lots of businesses have failed to take advantage of due to the costs factor.
Intelligence based and automated data preparation has been the long-awaited link to self-service which every organization deserves. Data is present in many forms and leaving the entire analysis process to the annual, bi-annual, regularly outsourced or intensive internal audit services makes irrelevant the primary intention. Real-time and business-ready insights are what every business requires as data evolves over time and analysis conducted later might not have the desired impacts. Every day an enterprise handles data from various sources and the speed to which that data is made suitable for day to day operations is what counts.
The rapid rise of reliable self-service data preparation and analysis tools has resolved lots of problems which include;
- Significantly cutting on the amount of time spent on preparing data. For most data scientists, close to 80% of their time is spent on data preparation which means most of an organization’s resources are spent on an element that can be automated. For a cost-effective and efficient data handling technique, intelligent data preparation platforms are not a choice but the solution.
- Eliminating the need for data scientists for data preparation. By taking the time for preparing a business for artificial intelligence data systems, companies get to cut on the necessity of having data scientists for the preparation stages. The services of data scientists will, therefore, be of the complex works they are trained for such as modeling, designing programs, and finding insights. This instantly leads to tremendous cost saving given that the bulk of the work would have already been done.
- Reducing over-dependence on the IT department. While the role of the IT department cannot be ignored, the impacts of solely relying on the department can lead to project delays.
Most organizations that have active IT departments already know that they have lots of duties to handle on a daily basis and exclusively letting them handle data preparation is a recipe for disaster. The benefits of automated andself driving data systems are that non-IT individuals can be able to manage them and analyze their projects independently. - Data preparation in relation to context. A common problem when outsourcing IT services especially for data preparation is the lack of understanding of the business context upon which it is relevant. By having an automated system which can lead to great data discovery and continuous insights in varied contexts all the struggles for business-ready solutions are entirely solved.
Author: Latrina Samford
