How Data Science can also Improve the Activity Control And Management System

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The automation of a process can also be an opportunity to improve control and management tools. Diceus’s consultants drew good practices from their mission experiences to optimize Data Analytics tools for operational efficiency and management.

Reporting and analysis tools, data cleaning, or the role of business teams: an illustration through the processing of commission slips as part of a brokerage activity. Diceus is one of the best agency that are focusing on data science consulting. Here is the best service of data science consulting by Diceus https://diceus.com/expertise/data-science-consulting-services-and-solutions/.

In a context where brokers receive a multitude of commission slips in different formats, record them manually and carry out their management reports in a theoretical way without being able to fully exploit the actual data received, Data Analytics techniques allow these postings to be automated but also to use, in a relevant way, the data to build efficient management tools.

 

Data Analytics To Automate The Posting Of Slips

Data Analytics tools automate various processes such as posting commission slips and uploading them directly into the accounting software. The flexibility of these tools facilitates handling the different formats of forms (Pdf, Excel, CSV, etc.). It allows them to be interfaced with various accounting or commercial management software.

The contributions of these tools in the management of the slips are multiple:

1. Operational Time Saving

It is, above all, a saving of time, which can be reallocated for decision analysis. Many steps are eliminated or optimized: receipt of documents, modification of file formats, and manual processing. And the risks of human error linked to manual billing of slips are reduced to zero. The processing time, for example, of a commission slip from its reading to its entry in the accounting software is counted in seconds because these tools read an average of 280,000 lines per minute.

 

2. Forecast Help

These Data Analytics tools can systematically process all the useful data, making it possible to estimate the expected amounts according to a defined criterion. Data Analytics, therefore, helps in all types of forecasting processes. For example, it is possible to create a predictive model of attrition.

 

3. Improved Reliability

Thanks to these tools, the broker manages to make the accounting entries more reliable and refine and the data in the CRM databases. The cross-references between the forms and the databases are now possible and can refine the processed data.

 

4. Better Control of Partner Regulations

The analytics give the broker better control over the sums paid to him by managers and allow them to identify late payments through the systematic storage of user data, for example.

 

5. Easy to Read Different File Formats

Reading the different types of slips is no longer a difficulty. Thanks to the needs formalized by the customer, the parameterization can be precise on the reading zones of the data sources. As a result, changes in the data source format may, in certain cases, have no impact on the reading process in the Data Science tool.

 

Data Analytics To Build Efficient Management Tools

Thanks to Data Analytics tools, brokers can also improve their control and management tools by relying on slip data.

1. Creation of Management Tools

Automated data mining tools are easy to interface with a wide spectrum of external databases. The information from the slips, read via Data Analytics tools and then stored in a data warehouse, can be used to generate various reports and dashboards. For example, it is possible to configure the automatic editing and send weekly turnover reporting by business line to the Marketing Director’s mailbox at a fixed date and time.

 

2. Relevance of Tools and Analytical Axes

Data Analytics tools also make it possible to generate new, much more relevant reports. In combination with Business Intelligence technologies. Can create dynamic reports of various kinds (Graphs and tables) to make relevant data accessible for decision-making. This gives users the ability to navigate reports according to different criteria (type of products, type of contract, and manager, for example).

 

3. Data Cleaning

These tools also make it easy to analyze and then clean data sources (identifying duplicates, incorrect data, aberrations or extraordinary values, etc.). This is an essential step in creating reliable and complete databases. It cannot do manually. It allows optimal use of the databases to refine the analytical axes (product family, etc.).

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