Data Management

Ingest, store, organize, maintain and make accessible accurate analytical information that drives decision-making and strategic planning

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Data is a Corporate Asset

Increasing seen as a asset, data is used for more-informed business decisions, improve marketing campaigns, improve business operations efficiency and optimize costs, driving towards the goal of increasing revenue and profitability.  But lack of proper data management strategy and approach can saddle your organization with challenges.  Challenges like incompatible data silos, inconsistent data sets, data quality and maintainability limits your ability to effectively utilise your data as an assets for business intelligence (BI) and analytics applications.

As businesses are subjected to increasing regulatory data compliance requirements, data management in an organisation has grown in importance.  In addition, ever-larger volumes of data and a wider variety of data types are being captured, both which are hallmarks of the big data systems that organisations have deployed.  Data management enables repeatable processes to keep data and metadata up to date, allowing organizations to effectively scale data and usage. Without good data management, such environments can become unwieldy, hard to navigate and costly to utilise as assets.

Data Management Functions

Data Management Functions

Data management ecompasses several process and disciplines, from data processing and storage to governance of how data is formatted and used in operational and analytical systems.  In large organizations with lots of data to manage, development of a data architecture is often the first step. A data architecture is the blueprint for the data points and data platforms that will be deployed and managed.

Some of data management’s fundamental disciplines include data modeling, which defines relationships between data and how it flows through systems; data integration, which ingest data from different sources for operational and analytical uses, achieved by building a data lake, data warehouse or through data virtualization;  and data quality management, which aims to fix data inconsistencies and errors.

Benefits of Good Data Management

Benefits of Good Data Management

A well-executed data management strategy and approach can help you access the right information at your fingertips, making all the difference in a competitive environment.  Organizations with well-managed data are more agile, with increased visibility of your data assets, making it easier for people to quickly and confidently find the right data for their analysis.

Effective data management provides companies with adequate, relevant and accurate data, making it possible to spot market trends and move to take advantage of new opportunities more quickly.  Whether you’re in marketing, sales, manufacturing, customer service, operations or other business unit, utilise your data as an asset to produce insights and gain potential competitive advantages over business rivals.  Ultimately, the biggest benefit that a solid approach to data management can provide is better business performance.

NTT DATA has the know-how

The torrents of data in organisations of all sizes is growing. Having the right data management strategy for organizing, governing, analyzing, and deploying an organization’s information assets is becoming imperative. Where others see only data, we see the possibilities and benefits that a coherent data management strategy can bring to an organisation.  While the “plumbing” aspects of data management may not be as sexy as the predictive models and colorful dashboards they produce, they’re vital to high performance.

We always seek to deliver relevant, superior data management and analytics—essential capabilities that support managerial decision making and ultimately enhance financial performance.  With our deep experience in analytics, we are highly strategic in our approach to data management, which is why we are big fans of delivering a relevant data management solution with the appropriate technologies.  We give our clients the ability to take charge of their data silos and rogue data sets,  providing timely access to accurate data, without ambiguity or conflict.

Data Management Techniques

Data Warehouses and Data Lakes

Both data warehouses and data lakes are used for managing analytics data.  Enterprise data warehouses is a more traditional approach, based on a relational or columnar database storing structured data for advanced querying and analytics.  A data warehouse is a repository of data that is already structured and filtered and has been processed for a specific purpose.

Data lakes, on the other hand, is a large set of raw data, which does not yet have a defined purpose.  It is essentially a highly scalable storage repository that holds large volumes of raw data in its native format until it is required for use.  Although most commonly built on Hadoop clusters, data lakes are also done on cloud object storage, or is a combination of different platforms in a distributed data lake environment.

Data Integration

Data integration is the technical and business processes used to combine data from different sources into a unified, single view of the data.  The traditional data integration technique is extract, transform and load (ETL), a batch integration process that run at scheduled intervals. However, data integration platforms now also support a variety of other integration methods.

An alternative data management approach is to do real-time data integration, using methods such as change data capture, and streaming data integration, which integrates streams of real-time data on a continuous basis.  We work with technology like Qlik Data Integration Platform (formerly Attunity) to enable real-time data integration.

Data Virtualisation

Data virtualisation uses an abstraction layer to create a virtual logical view of data from disparate systems.  It is a integration option that avoids having to create a whole new integrated physical data platform.  Data virtualization allows you to retrieve and manipulate data while shielding you from the technical details of the data, such as how it is formatted at source, or where it is physically located.  It also unified data for centralized security and governance, and delivers it to business users in real time.

Data virtualization can efficiently integrate and bridge data across data warehouses, data marts, and data lakes without extensive ETL and data storage.  We work with data virtualisation technology like Denodo, and DWaaS Snowflake to enable your data virtualization journey.

Data Modeling

Data modeling is a part of data management, it is the process of designing and formalising relational rules between both data, and data to business requirements, and building business-useful definitions for the data.  This creates a series of conceptual, logical and physical data models that document data sets and workflows and map them for processing and analytics.

Data modeling imposes upon data a structure that increases consistency in naming, rules, semantics, and business definition of data, while also improving the suitability of data for analytics.

Data Quality Management

Data quality management is the process for improving the data that’s used for analysis and decision, based on metrics such as accuracy, completeness, consistency, integrity and timeliness.  Data quality improvements leads to better analytics results and decision-making across your organization. The better the quality of data you have, the more confidence you can have in your decisions.

Data quality techniques include data profiling, which scans data sets to identify trends that help in discovering, understanding and exposing inconsistencies in the data, for any corrections and adjustments; data cleansing, also known as data scrubbing, which detects and rectify untrustworthy, inaccurate or outdated data; and data validation, which checks data against preset data standardization and quality rules.

 

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