Data Unification

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Modak Unification

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

Data Unification

Data unification involves merging data from multiple diverse sources and making them useful for developing business strategy.

Doing so requires a process of collecting, cleaning, de-duplicating and exporting millions of data points from multiple sources. It’s a task that requires human programmers and machine learning.

In organizations, there will be massive amount of databases from which the collection, analyzation of the data will take place. But most of the companies will have a hard time facing problems as how to store and handle this data due to multiple varied data sources and systems.

Many organizations have 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.

While dealing with big data, most of the companies tend to use only certain amount of data which is available because the data is centralized and will be cluttered in data lakes. Without identification, it is impossible for the data to be analysed or used.

The Problem

Traditional data management techniques are adequate when datasets are static and relatively few. But they break down in environments of high volume and complexity. This is largely due to their top-down, rules-based approaches, which often require significant manual effort to build and maintain.

In most organizations, data is collected from many different sources. It’s like a team mapping out local roads in an urban environment. Like the term “data,” the term “road” includes distinct types of road, including freeways, primary street roads, neighborhood streets and service roads.

Now, imagine an organization that collects information on a large urban area’s roads, but then keeps information on each different type of road separated from each other. It’s impossible to get a full picture of the entire roadway system and how traffic does (and doesn’t) flow.

That’s the issue with data in many organizations. While collection has increased rapidly, different types of data are kept “siloed,” with no ability to combine or cross-reference the data. It’s a huge issue. Data lakes lead to businesses not having the ability to get the most insight out of the data they have collected.

This is where those with data science degrees come in. Data unification allows the merging of data that is able to be mined for useful insights into past business activity or in creating predictive models.

Why is data unification useful?

“Data unification” technology leverages machine learning techniques and it’s a reinvention of traditional data management capabilities – such as those found in MDM and ETL processes – to meet the requirements of the Big Data era.

Focusing on connecting and mastering datasets through the use of augmented learning, which leverages signals in the data to determine how it should integrate. Using automation guided by human intelligence to integrate and master datasets drives substantial benefits around speed, scale and data model flexibility while ensuring the highest levels of accuracy and trust in the results. At its most fundamental level, data unification brings the promise of machine learning to the preparation of datasets at scale.

Data unification fuses the worlds of automation and human expertise to capitalize on the benefits of each in preparing datasets for analysis. By using machine learning-based automation to recommend how attributes and records should be matched, organizations benefit significantly along the dimensions of speed and scale. The platform enables connection of a growing number of data sources in a more efficient manner, addressing one of the most significant needs in large, fragmented IT environments.