Data Unification

UNIFY >> FINGERPRINT >> CLASSIFY

Modak Unification

Our Unification process combines human expertise, machine learning algorithms, data science and our in-house developed fingerprinting technology

Data Unification

Data unification involves merging data from various data sources and making it useful for developing business insights. This requires collecting, cleaning, tagging and exporting millions of data from data sources. This process requires both human programmers and machine learning tools.

Organizations have large number of databases from which the data collection and data analyzation takes place. Most of these organizations find it difficult to store and handle this data due to multiple and varied data sources and systems.

Modak has found a way to store this data in “data lakes.” Data lake is a repository of data which could be in structured or unstructured formats.

The Problem

Traditional data management techniques are adequate for static and relatively small datasets. But, for large and rapidly changing datasets unique data management techniques are required. Modak has the capability to handle such large datasets.

Data is collected from different sources.  This data is kept “siloed,” with no ability to combine them or cross-reference when required. It  becomes a challenge for most of the organizations to get insight from the unorganised data in the Data Lake .This makes it difficult to standardize data with various formats.

Data mapping of the data in various formats (at source) with the destination (in standard format) is a cumbersome activity in itself. This complex process is significantly simplified by Modak.

We use Data unification that allows merging of data that can be mined for useful insights into past business activity and for creating predictive models.

Why is data unification useful?

“Data unification” technology uses machine learning technique. It is a reinvention of traditional data management capabilities – as found in MDM and ETL processes .

Use of automation guided by human intelligence to integrate datasets drives substantial benefits around speed, scale, and data model flexibility while ensuring the highest level of accuracy in the results.

Attributes and records can be matched using Modak’s product namely Nabu. This will benefit the organizations significantly along the dimensions of speed and scale.

Hence, complex data can be unified in less time with significant accuracy.