Find an efficient approach to data gathering, analyzing, processing, visualization

One of the Client’s domains is banking & finance. Business owners in this industry are continually on the lookout for a reliable and consistent approach to manage data. Such an approach underpins all the processes related to managing legal documents, corporate documents, client agreements, etc., to eliminate errors and build consumers trust.
Businesses cooperate with the Client to be able to leverage an AI-powered solution for answering key business questions. A data-driven solution requires the involvement of data scientists to bring in their experience and expertise in working with big data.
When stored in a data lake, data provided by end users becomes available to AI and can be exploited to provide end users with valuable insight on various queries related to risk management, teamwork, project status, working schedule, downtimes, accident management, and more.
A streamlined and optimized way of processing data is the kernel of an AI-powered solution. Nobel Link was challenged to provide data science services to enhance the performance and efficiency of the Client’s solution.
The Nobel Link’ team started step-by-step development process to solve the Client’s challenge and deliver a robust solution.

• 1. UK financial organizations have adopted a document supply chain, encompassing the following stages:
  Document gathering
• Accumulation of documents
• Order processing

Documents come from multiple sources and in different formats. The chaotic nature of gathered documents and the lack of common repository prevente extracting value from big data for further important uses.

2. The Nobel Link’ team worked with unstructured data from different sources, such as some listed below:

• Project Data
• Meetings
• Digital Identity
• Work Schedule
• Inspection Data
• Hazard Data
• Etc.

It was needed to provide the solution to turn raw big data into meaningful insight.

3. Nobel Link developed data lakes to aggregate raw data in different formats and store data in files. We used the following open-source services to make the data available for analysis and visualization:

• Apache NiFi for data ingestion
• Cloudera CDH as a data management platform
• Apache Oozie for data processing workflow
• Apache Spark as a data processing engine
• HDFS (raw and parquet) for data storage
• Apache Kudu for data analytics
• Apache Impala for data analytics

The data lake implementation helped structure business data, which then allowed using open-source components to deliver a BI solution to meet the Client’s needs.

4. Since end users require smooth access to data in visually appealing forms, the Client’s solution provides a user-friendly interface. The processed and classified data becomes available via easy-to-interact dashboards. These dashboards utilize data from the data lakes to enable comprehensive insights. As a result, the solution facilitates real-time monitoring of business workflows across an organization and provides visual insight for better decision making.