Today, enterprises have access to zettabytes of data. But the question is, are they able to leverage it to gain insights?
Many businesses are finding their existing infrastructure that includes servers and racks to be limiting their ability to meet their need for increased storage space and compute power. In the traditional architecture, often businesses went for proprietary end-to-end solutions right from centralized data collection to storage, and analysis, to optimize resources and minimize costs but lost control of their own data.
As technology grows by leaps and bounds, businesses are also facing the problem of the tools being unable to up with the changing times and storage being insufficient for their growing needs. The cost of expanding infrastructure is also formidable, needing viable alternate solutions.
According to a McKinsey report, businesses are trying to simplify their current architectural approaches and accelerate deliveries across data activities such as acquisition, storage, processing, analysis, and exposure, to create a modern infrastructure that can support their future analytics and BI efforts. For this, six foundational shifts are being effected on data-architecture blueprints, leaving the core technology stack untouched. This can result in an increase in RoI due to lower IT costs, improved capabilities productivity, and lower regulatory and operational risk.
The Six Features of a Modern Data Ecosystem
As per the report, the six foundational shifts that facilitate the creation of a modern data ecosystem to support future analytics and BI efforts include:
1. Shifting to Cloud-based Data Platforms: Cloud-based solutions from providers such as Amazon, Azure Google, are disrupting the data architecture approach for sourcing, deployment, and running data infrastructure, platforms, and applications at scale. Two key components of this revolution are serverless data and containerized data solutions.
a. Serverless platforms such as Amazon S3 and Google BigQuery eliminate the need for installing and configuring solutions or managing workloads while enabling businesses to build and manage data-centric applications at scale at almost no operational overhead.
b. With containerized data solutions using Kubernetes, businesses can decouple compute power and data storage while automating the deployment of additional systems.
2. Real-time Data Processing: Real-time data streaming is a cost-effective solution that allows data consumers to receive a constant feed of the data they need by subscribing to relevant categories from a common data lake that is the source and retains all granular transactions. It can be of three types: messaging platforms such as Apache Kafka; streaming processing and analytics solutions such as Apache Spark Streaming, Apache Kafka Streaming, Apache Storm, and Apache Flume; alerting platforms such as Graphite or Splunk
3. Modular Platforms: A modular data architecture that leverages open source and best-of-breed components provides businesses with the flexibility to discard old technologies and embrace new ones with the least disruptions using data pipelines and API-based interfaces and analytics workbenches. They also facilitate the integration of disparate tools and platforms to connect with several underlying databases and services.
4. Decoupled Data Access: For effective reuse of data by different teams, businesses can provide limited and secure views and modify data access by exposing data through APIs. It also enables quick access to up-to-date and common data sets. As a result, analytics teams can collaborate seamlessly and accelerate the development of AI solutions. The two key components that facilitate this are an API management platform or API gateway and a data platform.
5. Domain-based Architecture: Instead of a central enterprise data lake, domain-driven data architecture designs help businesses customize and accelerate time-to-market new data products and services. Data sets can also be organized in a more easily consumable manner for domain users and downstream data consumers. Some of the enabling features of this architecture are its data-infrastructure-as-a-platform model integrated with data virtualization techniques, and data cataloging tools.
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A McKinsey Digital report shows that only 14 percent of companies launching digital transformations are able to see sustained and substantial improvements in performance. This is because, though there are several digital engineering solutions and analytics service options available to draw insights through business intelligence and analytics, , organizations are unable to suit the right architecture for their business. What is popular or obvious may not be the right fit for them.
Indium Software is a data engineering specialist offering cross-domain and cross-functional expertise and experience to understand the unique needs of each business. We use commercial and open source tools based on the cost and business requirements to meet your unique data engineering and analytics needs.
To accelerate your data modernization journey to make your business future-ready, we help you with large-scale data transformation and Cloud Data & Analytics. To ensure the efficiency of your data modernization process, we help you with aligning your data management strategy with your business plan.
Our solution encompasses:
ETL Modernization: The three-step process of extract, transform, and load is designed to help you overcome your business challenges.
Data Governance: User- and role-based data access ensures data security, privacy, and compliance while enabling informed decision-making.
Data Visualization: Our experts use cutting-edge business intelligence solutions to enable data visualizations for actionable insights
Data Management: Data abnormalities are identified as they occur, reducing time and money in rectifying mistakes.