Our solution analyzes huge datasets hubs and silos to uncover fuzzy match issues that other solutions miss, allowing you to spot problem, anomalies and perform impact analysis
According to a study published by the Data Warehousing Institute, the two most important challenges facing companies that implement MDM solutions are data quality management and decentralized disparate data sources.
In that same study, it is estimated that 40% of the companies incurred losses, problems or costs as a result of poor data quality. The study also indicates that about 43% of companies may also have experienced similar problems, but failed to detect that problem. Sources with low data quality are varied, as shown in the following graph:
Master data refers to attributes such as product name, description, and dimensions. Information about each order, such as order number and quantity which constitutes transactional data.
A customizable attribute like credit level can be calculated using transactional data and updated more frequently than a static attribute like customer name. All in all, it can be considered master data, because it is a descriptive element of who the customer is, similar to their address or income level.
Although a good data quality program is the result of proper management of people and processes, technological tools also play an important role. Many companies perform data cleansing tasks with homegrown tools, SQL programs, or limited tools included in ETL products. Let CUBO iQ get 80% of the job done in a matter of clicks!
Examine data that is stored within each organization and collect statistics, information and errors about them with the aim of reducing risks when integrating new applications and achieving your proposed data quality metrics.
Normalize the repetition of groups and minimize redundancies in disparate nomenclature and optimize searches and increase the reliability of information with machine learning business rules.
Invoke smart triggers and streamline processes with and streamline processes and impact analysis connecting to third party sources consolidating data repositories or data lakes in a breeze.
Enhance your fuzzy matching accuracy with our CUBO iQ matching engine fixing old and catching new issues allowing you to intake new data efficiently and get the ultimate single customer view.
The person in the organization who needs the project to be executed could be the person most affected by poor data quality or the IT person who wants to introduce this improvement in the organization (project motivator) This is the first step of an master data management implementation. If not done right the problems can haunt for years
See a complete view of your data repositories, data warehouses, data lakes with interactions, preferences, and attitudes so you can deliver an exceptional MDM experience!
We integrate enriched data with your preexisting tools and workflow for your team to have easy to access data that is processable and that yields real measurable results.
We have more than 40 experience years in Big Data, Software and Business Intelligence sector, Specialized in the public and private sectors and Fortune 500 companies.
