Alation's data intelligence strategy is being outlined

Alation's data intelligence strategy is being outlined ...

Many enterprise executives are concerned about data quality, a subset of data intelligence. 82 percent of customers consider data quality as a concern for their businesses. What you can do with this range of data quality solutions, according to the market.

Satyen Sangani, the CEO of Alations, said today that the announcement of its Alation Open Data Quality Initiative (ODQI) for the modern data stack is designed to offer customers the freedom of choice and flexibility when it comes to selecting the best data quality and data observability vendors.

Alations Open Data Quality Framework (ODQF) has expanded its data catalog to any data quality provider in the data management environment and modern data stack. Initially, data quality and data observability providers such as Acceldata, Anomalo, Bigeye, Experian, FirstEigen, Lightup, and Soda have joined the organization, as well as industry partners including Capgemini and Fivetran.

Some of these were Alations partners already, while others are new and drawn to the idea of establishing a standard. The company hopes the ODQF will become the de facto standard.

From data catalogs to data intelligence

Sangani, who has a background in economics and managerial roles at Oracle, founded Alation in 2012. However, the company remained in secret until 2015, working with a few customers to determine what the product was intended to do, and for whom.

Sanganis'' experience enthused Alations'' approach, while implying that selling large-scale packages to small-scale businesses to assist them in analyzing their data resulted in them not actually understanding the data themselves.

There would be hundreds of millions of dollars spent on hardware, and thus a lot of that time was spent researching which systems have the correct data, how the data was used, and what the data entaild. Often there were multiple copies of the data and conflicting records, which often were outside the company.

Data modeling, schemas, and other techniques are all part of a knowledge management problem, according to Sangani, who believes it incorporates human psychology as well as a didactic aspect, in terms of allowing and teaching individuals how to use quantitative reasoning and thinking.

Alations'' trajectory has been linked to a number of terms and categories, including metadata management, data governance, and data cataloging. Today, Sangani claims these three are all coming together in a wider market space: what was originally identified by IDC as data intelligence.

After Alations'' launch in 2015, the company was attempting to broaden the data catalog category, which many liked, according to Sangani. Finally, other professionals from metadata management and data governance were able to converge on building a data catalog.

Parallel to this, the calendar from 2012 through today includes technology advances, such as the democratization of large data via the Hadoop ecosystem, as well as the passage of regulatory standards such as HIPAA and GDPR. All of those involved in the need to create inventories aimed at facilitating data use by people, which Alation sees as a competitive diveriator.

Alation as a platform for data quality

Alation''s data catalog serves as the basis for the worldwide data intelligence category. Sangani claims that data intelligence has many components: master data management, reference data management, data transformation, data quality, data observability, and more. Alations'' strategy is not to own one box of every single one of these things, as Sangani said.

The real issue in this space isn''t whether or not you have the capability to tag data. The largest obstacle in this area is engagement and adoption. Most individuals do not use data properly. Most people do not understand what data exists. Most of the data is under-documented, according to Sangani.

The purpose of the data catalog is primarily about engaging people into the data sets. However, if we focus on engagement and adoption, we should make certain things that strategically were not doing, according to the researcher. What was not doing is constructing a data quality solution or a master data management solution.

Alation wanted to extend its offering in the data quality industry, but it was opposed. Its a fast-moving, densely populated country, and solutions can vary greatly. Sangani said that sharing can transform Alation into a data quality platform and that the Open Data Quality Initiative is aiming to achieve.

According to Sangani, customer acceptance is key to whether standards exist or die. This is a follow-up to the Alations Open Connector framework, which allows third parties to construct metadata connections for any data system.

Plumbing is the foundation for value-add applications.

Alation will continue to build open integrations and frameworks over time, according to Sangani, because there must be a consistent way to share metadata in the world of data management. In a way, what Alation has established is now plumbing, and the ODQF is an example of additional plumbing.

Despite being plumbing a necessity, the company has already begun moving up the stack to offer value-add capabilities. Examples include, for example, using natural language processing (NLP) to perform name entity recognition for recommendations or allowing people to compose English language sentences and convert that into SQL to be able to interact with queryable datasets.

As a result of Sangani''s ability to develop a more intelligent data intelligence layer, technology such as knowledge graphs, AI, and machine learning are all used.

In fact, I''m probably more excited about what we have accomplished in the next five years than we have ever done in the previous five, because all of it lays the foundation for some really cool applications that will start seeing in the near future.

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