Today, enterprises are swamped I mine with information from customer interactions, online transactions, blog posts, tweets, social media and sensor data. They must manage this data efficiently in order to make informed decisions, enhance customer experiences and drive innovation.
Data management is the process of acquiring, organising, storing and utilising data throughout the data life cycle in an enterprise. The goal is to ensure that the data is accurate, accessible, secure and aligned with business objectives. It involves a set of practices, processes, policies and technologies that govern data collection, storage, processing and sharing.
Challenges in data management
Enterprises face quite a few challenges in managing data effectively and efficiently.
Data is in silos, segmented across various platforms, channels, tools, and business units, making it challenging to access it. This leads to inefficiency and data inconsistency.
Data accuracy, completeness, and timeliness is an issue.
Bringing offline data online is a challenge.
Managing regulatory compliance, establishing data security, and data privacy are all challenging tasks. A poor data management strategy may lead to an enormous amount of data in a completely unmanageable format.
Next-generation data capabilities
To manage the huge and different types of data that enterprises collect and process, they must build the following data capabilities.
Data processing techniques:
Unstructured data processing covers entity extraction, concept extraction, sentiment analysis, NLP, ontology, etc. To automate data extraction, machine learning techniques must be leveraged.
Agile data delivery: Agile development methodology can be used to reduce the overall cost of data delivery.
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