Data is at the heart of any institution. It holds the key to making knowledgeable and supportable decisions. How this data is organized is called data architecture.
Data architecture is a set of models, rules, and policies that define how data is captured, processed, and stored in the database.
It also defines how and which users have access to which data and how they can use it.
Many organizations that use traditional data architectures today are rethinking their database architecture.
This is because existing data architectures are unable to support the speed, agility, and volume that is required by companies today.
Modern Data Architecture (MDA) addresses these business demands, thus enabling organizations to quickly find and unify their data across various storage technologies. Reducing time and increasing flexibility and agility is the main objective of MDA.
Modern data architecture typically depends on the implementation objectives. Commonly, modern data architecture has the following characteristics:
- Data can be generated from internal systems, cloud-based systems, along with any external data that is provided by partners and third parties.
- Collection of data via real-time data sources in addition to batch loads.
- Providing analytics to traditional platforms such as data marts along with specialty databases such as graphing and mapping.
- Support for all types of users ranging from customers to data scientists.
For the smooth flow of data in the organization, data should be viewed as a shared asset.
Instead of allowing inter-departmental silos to exist, the stakeholders get a complete view of the company.
This means the decision-makers get a transparent view of the customer insights with the ability to correlate data from all business functions, which includes manufacturing and logistics. This results in improved efficiency.
Only storing data in one place does not enable the smooth functioning of a data-driven organization. Users should be able to access the data to benefit from the shared data asset.
Users need to be provided with interfaces to consume data.
These interfaces vary from user-to-user depending on the position of the user in the ecosystem and the data they need to access, to get their job done efficiently.
With Big Data and Hadoop providing us with a unified platform, it has become necessary to devise and enforce data and access control policies on the raw data.
This is made feasible by security projects such as Apache Sentry. For this purpose, we should look for technologies that let us architect for security solutions without compromising control over our systems.
With the help of a data hub, organizations are now able to use data as a shared asset and give access to multiple users of the same data.
However, it is critical to ensure that all the users accessing the data analyze and understand it using a common vocabulary.
Product catalogs, provider hierarchy, fiscal calendar dimensions, and KPI definitions need to be uniform regardless of how the user is consuming the data.
This is imperative to maintain the integrity of the data throughout the organization.
Data curation includes cleaning of raw data, modeling proper relationships between various data sets, and curating key dimensions and measures.
But without proper curation, users can find it difficult to navigate through the vast expanse of data to find the one which they require. This reduces the perceived and realized value of the underlying data.
With proper curation and modeling of data, the full potential of the system can be achieved.
With every instance of data movement, cost, accuracy, and time are compromised upon. It’s better to reduce the movement of data as much as possible.
Big Data and Hadoop’s value proposition includes a multi-structure, multi-workload environment for parallel processing of data sets. Hadoop scales linearly as the data volume increases.
The data architectures that have dominated the IT infrastructures in the past are no longer capable of the enormous workloads of today’s enterprises. There are various advantages of modern architecture as follows:
Data from large organizations are complex to manage. They often have data fed from various sources into different warehouses and data lakes.
Integrating this data could prove to be a difficult task. Having a centralized view of the data allows users to configure and manage the data throughout the organization.
According to studies, the value of operational data drops by about 50% after about 8 hours. Replicating the data from one place to another increases latency in the process.
Decisions in functions such as inventory stocking, improvement to customer service, or overall organizational efficiency need to be handled in real-time.
MDA enables hyper-connected enterprises. This makes the data available throughout the enterprise for all the users that have access to it in the least time possible.
The earlier data lake initiatives failed to meet the originally intended analytics insights.
While gathering data in your lake is an easy task, it is the processing of data which is a challenging task.
Handling the continuous updates, merging the data, and creating analytics-ready structures is a difficult task.
MDA not only lands the data where it should but also automates the creation and updating of the data as per requirements.
With this in place, the data scientists and analysts can spend more time on the analysis of the data rather than data preparation.
Once the data ingestion and creation of analytics-ready is automated in the data lake, automating the creation of function-specific warehouses and marts would be the next step.
Once the data warehouse automation is in place, data marts can be created and updated wherever required. This leads to increased agility and reduced project risk.
The journey to a successful implementation of modern data architecture is long and complicated. However, with principles and frameworks, it surely can be achieved.
Data is undoubtedly the future of computing and a way of life for businesses to function. And hence, it is crucial that we have the data architecture principles in order beforehand to manage all the data effectively.
You May Also Like to Read:
Data Center Tier 5 Explained