LogiMDI

Master data management (MDM) provides a trusted view of master entities to help achieve customer centric objectives and business results. Without a clearly defined master data, the enterprise runs the risk of having multiple copies of data that are inconsistent with one another. Since there are disparate systems within the organization, providing a single source of truth is difficult. In case of merger/acquisition, each of the organizations will have its own master data and how to merge the two sets of data will be challenging.

Data Challenges
Some of the services offered by ElogicSquare in this space are:
Why Master Data Management & Governance? (MDM/MDG)
The end result of all this, from a Product Information Management perspective, is to be able to:

Company’s First flagship product in this space, LogiMDI, represents the next generation of master data index (MDI) and consumer master data management (MDM) technologies. It is a software-as-a-service (SaaS) solution that is powered by customer matching technology. LogiMDI is the most accurate, easiest to implement, most secure, and most cost-effective MDI on the market.

LogiMDI: The challenges faced by Master Data Index (abbreviate MDI) during the match and link process are magnified due to organization -specific standards with regards to data quality, completeness, metadata coding, other governance aspects, but also between systems within the same organization.

We at ElogicSquare have built a Master Data Index solution , LogiMDI on our Multi-Tenant Platform. The algorithm is a hybrid model built based on probabilistic and fuzzy matching heuristics, resolving patient identities across systems and organizations leads to:

Access to patient's complete medical history

Platform generates and stores tenant-specific metadata, as it discovers dynamically in the ingestion process, while enriching and transforming from tenant-specific to canonical payloads. Metadata is also loaded onto a clustered cache, for data reads by the pipeline processes

LogiMDI uses probabilistic algorithms to match patient records. The algorithm assigns a rank to different data elements based on a preset acceptable level of certainty and scores the likelihood that two or more records belong to the same patient. The higher the score, the higher the probability that there is a match between two records

The challenges faced by MPI (abbreviate MPI) during the match and link process are magnified due to organization -specific standards with regards to data quality, completeness, metadata coding, other governance aspects, but also between systems within the same organization.

We at ElogicSquare have built a Master Patient Index solution on our Multi-Tenant Platform. The algorithm is a hybrid model built based on probabilistic and fuzzy matching heuristics, resolving patient identities across systems and organizations leads to:

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