We designed The Indium Questionnaire to answer common questions faced by clients we serve in our Digital Services division.
We thought a Q & A format will deliver a structured approach to answering key questions faced by digital transformation leaders.
For this post on Data Landscape Modernization we answer the following questions:
Topics Covered
What do we mean by Data Landscape Modernization?
A study by Mckinsey Analytics conducted in 2019 reported that only 15% of the organizations (that use Big Data Analytics for Decision Making) derive desired value from their data. Yes, there is an explosion of real-time data in most organizations. But, they are not able to derive value from this. Why?
The challenge often is that most data is located in silos. There is customer data silo, financial data, operational data, supply chain data – the list is endless. Yet, companies haven’t invested enough in designing an Information Architecture that is modern.
Data Landscape Modernization refers to designing an Information Architecture that enables or catalyzes the process of deriving maximum value from Big Data. It is truly the first step to make your enterprise ‘Analytics Ready’.
How can you modernize a firm’s Data Landscape?
- Simplify: Reduce complexity of your data ecosystem. Create an architecture that consolidates multiple data warehouses, reporting tools and reduce silos. Ensure you choose a single platform that acts as the gold source of data
- Automate Data Acquisition: The second key aspect to this process is to automate data acquisition. Often, the most time consuming aspect in the early days of an analytics engagement is to blend data from multiple sources. Can this be automated?
- Data Transformation: Of course, the most important step is to create Data Lakes and possibly even Data Hubs and drive the enterprise’s transformation to the cloud from legacy systems. This definitely reduces end-to-end data processing time and reduces ETL complexities.
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Is the process of modernization the same for all enterprises?
Not at all. It entirely depends on the nature of your business and the current state of your data ecosystem. But broadly traditional data warehouses are exploding with data. The cost of data management is going up, while the ability to gather insights from real-time data has not been utilized to the fullest.
The key goals most enterprises are setting for themselves are around three key parameters:
- Insights from Real-Time Data at Scale: Enterprises want to build a data warehouse that catalyzes the ability to capture insights from real-time data at scale.
- Speed is critical: This mean it is critical to build an information architecture that reduces latency in data processing
- Reduced Costs: Needless to say, enterprises are becoming extremely clear about the need to reduce both infrastructure and people costs, by emraching automation.
How can Indium help?
Indium Software, we’ve deep expertise and experience in a range of Big Data & Advanced Analytics Services. We’ve also deeply analyzed the latest tools and technologies in the Big Data ecosystem, helping our clients pick the right combination of tools to build their Big Data ecosystem.
Building a modern Data Landscape is the foundation of any Big Data & Advanced Analytics project.
Our services start with Architecture & Design followed by a whole range of specific technology services including:
- Data Lake Designs
- Data Warehouse/Data Mart Cluster Setup – On-Prem, Cloud and Hybrid
- Big Data Applications
- ETL, Data Pipelines & ELT
- Data Management – SQL & NO SQL
- BI & Visualization
- Data Governance
- Data Quality
- Master Data Management
- Metadata Management
- Migrations & Upgrades – Applications, Database
- Cloud – Migration & Onboarding
- Big Data Testing
- Big Data Security
- Delivery Methodologies – Agile, Waterfall, Iterative, DevOps
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We have recently completed Data Analytics engagements with enterprise customers across a range of sectors including Banking & Financial Services, Retail & ecommerce, App-based Taxi Operator and a global next-generation mobility (connected car) startup.
While the specific scope of each of these engagements were very different, the reason they chose Indium was simple: Our ability to design a Big Data Ecosystem that was futuristic, scalable, cost-efficient and elastic.
Give us a shout to request for a call with one of our Big Data Leaders:
