The idea of data management is relatively simple to understand. An organization must be able to capture, access, transform, analyze, and secure relevant data in a timely and efficient manner in order to apply smart business decisions and processes.
The importance of Data Management for reliable, quality data approach
Data management is concerned with “looking after” and processing row data helping organizations in their digital transformation process. It allows the consolidation of data (and meta-data) in a way that is easy to manipulate, retrieve, and maintain. It ensures data for analysis are of high quality so that conclusions are correct, enforcing well-set data security and information governance strategies.
Good data management allows further use of the data in the future and enables efficient integration of results leading to improved process efficiency, improved data quality, and improved meaningfulness of the data.
Data Management, bringing more value to raw data
Advanced data management tools are needed to collect, cleanse, convert, segment, code, and consolidate content data from disparate content sources for a centralized aggregated “Big Data” ready for analysis. Capture should cover all incoming content in a mixed manner: automated for bulk ingestion of content and interactive for on-demand capture.
The Data Capture phase of a Data Management Framework
Capturing content in an automated manner requires a Capture Process Automation solution. The objective is to define the capture process, the various activities related to it, the various activities related to integration with other content sources, and activities related to the approval and enrichment cycle.
Data capture automation tackles also the issues of migrating existing content to the newly installed system. During the migration, operations such as data cleansing, data linking, data conversion can also be achieved.
Data Integration, a step further to Data Governance
Organizations must be able to integrate data from various, disparate content sources and transform it into trusted information. The ability to integrate information quickly and efficiently is crucial even as those requirements continue to shift and data volumes increase.
Data Integration tools provide the ability to ingest vast amounts of content into Big Data structures in a fast, efficient, and standardized manner. Content is imported in batches through high-performance import procedures while providing classification and organization of content according to existing classification plans. Data Integration Tools are usually based on ETL processes to:
- Extract various types of structured and unstructured data with large volumes and structures ranging from simple to complex and convert the data into a single format appropriate for transformation processing.
- Transform extracted data into a unified, standardized format, for storage, to allow further querying and analysis. The transformation phase involves a data cleaning operation, which aims to pass only “proper” data to the target.
- Loading unified content into the final target
The need for a growing Data Storage Architecture
The above phases will lead to extremely large data sets ingested and stored into the Big Data structures and linked to other internal and external data sets. A solid and performing framework is needed for storing and processing Big Data in a distributed manner on large clusters of servers. Basically, what the framework is requested to accomplish is massive data storage and very fast processing. The core of the Big Data Management framework also consists of the following:
- A powerful Content Services Platform that can manage the ingestion, storage, and processing of very large volumes of content, coming in various flows and in different formats.
- A state of the art Records Management System, fully integrated with the Enterprise Content Management System, to manage physical security archives.
- A powerful distributed processing sub-system managing the Big Data distributed storage and exposing its services to the Big Data analytical layers as well as other third-party applications.