In the digital era, data is at the heart of an enterprise. So, managing the data has become the topmost priority of any organization today. As the data volume increases at a 10X rate, this data growth impacts many organizations in the form of the multiplication of in-company heterogeneous storages, remote sites, and cloud storages. This will require moving the data which results in time, infrastructure, and cost constraints. So, the need for a consolidated data management solution arises which uses AI automation to reduce time and infrastructure costs across the data value chain.
As enterprises are increasingly standardizing on augmented analytics, it brings together two distinct worlds of data and analytics. This collision enhances interaction and collaboration between the two worlds shaping the associated paradigm in the market i.e.
augmented data management. Augmented data management (ADM) utilizes AI/ML to a…ment tasks
This emerging trend is impacting enterprise data management
Traditionally, data scientists used to spend 80% of their time cleaning the data through the extract, transform, and load (ETL) process. By automating the process of ETL
Organizations will no longer require candidates with experience in statistics and mathematics background to do BI. They will be able to hire less technical people who understand the business, derive insights, and offer recommendations based on delivered data.
A new role like citizen data scientist is also gaining traction these days in an organization to fill the data science and machine learning talent gap caused by the shortage and high cost of expert data scientists. It also makes expert data scientists more productive and collaborative, freeing them for high-value tasks.
Implementing ADM assists enterprises in organizing and maintaining data quality through cutting-edge technologies, resulting increase in effectivity and productivity. Below are a few points of how augmented data management transforms an enterprise:
- According to a survey, data scientists spend 80% of their time in manual data preparation. ADM automates routine tasks such as cleaning, profiling, labeling, classification, and tagging. This enables insights to be derived from an organization’s data at an impressive scale.
- Enterprise leverage advanced analytics techniques
- Metadata management involves managing data lineage. If not managed properly, silos of inconsistent metadata will be created in an enterprise providing conflicting information and users will not be able to trust the data. By automating the metadata management process like data cleaning, integration, and attribute matching the data lineage is fully traceable and gives a clear picture of the information contained within the data, the purpose it serves within the organization, and improves data governance. So, ADM converts metadata to a “powerful dynamic system”.
- ADM automates some of the data professional’s routine manual tasks such as database performance tuning, hardware configuration, and optimization, and other database administration jobs that are computationally intensive and iterative. This reduces errors on deployments, improves reliability, and increases the speed of implementing changes.
- When data is integrated from multiple sources, inconsistencies in the data occur because of statistical methods based on data matched on names and attributes. By automating this process, more accurate suggestions are made during data mapping and empowering the team to add more data sources highly confident that quality will not be compromised.
- ADM utilizes ML tools or AI intelligence bots to present smart recommendations but still relies on human skills to make decisions.
Apart from the aforementioned benefits, augmented data management can also facilitate data for real-time analytics
This will eliminate data silos, thereby, decreasing the cost of business operations.
Enterprises need to revamp the data management process to stay competitive. By leveraging ADM, it automates the manual data management process and provides faster actionable insights without wasting time or resources.
It improves the productivity of data scientists, freeing them from the mundane tasks of data management. This enhances effectiveness, saves costs, and improves the revenue generation of an organization.
Therefore, deploying ADM practices is the way forward to stay ahead and give blue-chip companies a run for the money.
The article is originally published by Subex
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Category: Telecommunications Carriers Solutions Manufacturers Profile: Subex is a leading telecom analytics solutions provider, enabling a digital future for global telcos. Founded in 1992, Subex has spent over 25 years in enabling 3/4th of the largest 50 CSPs globally achieve competitive advantage. By leveraging data which is gathered across networks, customers, and systems coupled with its domain knowledge and the capabilities of its core solutions, Subex helps CSPs to drive new business models, enhance customer experience and optimise enterprises. Subex leverag ...
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