Data Engineering, Strategy
and Analytics
Data Strategy & Analytics
Digital analytics advancements over the past decade have revolutionized commercial operations. Advanced analytics is now essential in many organizational tasks, including marketing, pricing, customer service, and manufacturing. But, at least for now, the same cannot be said to be true of strategy.
While strategy development will always require creative and thoughtful executives to set aspirations and make bold choices, analytics tools can give you an edge.
A data strategy is more than a one-off data project. It is the foundation an organization needs to get increased value from their data—and the agility to evolve data practices when demands grow and new technologies emerge.
Every successful firm has a solid data strategy and long-term roadmap outlining the personnel, operational procedures, technological requirements, and data requirements to meet its goals.
Data Strategy Services
Our data strategy consultants can help you regardless of whether you are developing a new data strategy, need a plan to improve your current data strategy, need assistance carrying out your data strategy, or want a professional perspective on a particular effort.
Data and Digital Platform
Companies may create a data and digital platform that offers three to five times the value in half the time, and at half the cost instead of starting a costly multi-year, IT change. Companies can enable new digital services and modernize their core IT by using the Knoldus data and digital platform, both of which are essential for the success of a digital transformation.
At Knoldus, we think that a successful digital transformation necessitates separating the data layer from legacy IT to prevent businesses from being compelled to upgrade their ERP systems all at once, which would be costly, time-consuming, and unsafe. Companies may scale up new digital services more quickly while upgrading their core IT when they integrate data and digital platforms, isolating the data layer from traditional IT.
Thanks to numerous advancements in SaaS, PaaS, cloud solutions, low code platforms, and the open source community, it has only lately become practical to construct the digital platform and smart business layers separately from the existing core.
CASE STUDY
Elsevier enables the user to derive new data insight with the reactive technology stack and architecture
Data Engineering
Implementing properly engineered solutions in today’s data space is a daunting challenge for any organization. The volumes of data, number of disparate sources, and tooling choices require a new breadth of expertise and new approach paradigms. Informed business decisions require data that is readily available and accurate. The key is effectively harnessing the value of that data to create a competitive advantage.
Knoldus’ experienced team of data engineering consultants can help your team develop the strategies for turning vast amounts of data into valuable business assets both with Big Data and Fast Data pipelines. We will work with you to bring all your data sources together, so it is secure, available, and accessible. We also solve real problems connecting systems that are struggling to keep up with high-volume, high-speed transactional data needs.
We work with batching and streaming technologies to build pipelines that would efficiently harness data from different sources and get them into a central repository for analytics and decision making.
EVENT
Blue Pill / Red : The Matrix of thousands of data streams
Data Mesh - Unlocking Data Value in the Organization
Data Mesh is a strategic approach to modern data management and a strategy to support an organization’s journey toward digital transformation. Data Mesh’s primary goal is to advance beyond the established centralized data management techniques of using data warehouses and data lakes. By giving data producers and data consumers the ability to access and handle data without having to go through the hassle of involving the data lake or data warehouse team, Data Mesh highlights the concept of organizational agility. Data Mesh’s decentralized approach distributes data ownership to industry-specific organizations that use, control, and manage data as a product.
From the technology side, Knoldus’ view on the data mesh involves three important new focus areas for data-driven architecture:
- Tools that provide data products such as data collections, data events, and data analytics.
- Distributed, decentralized data architectures that support businesses who choose to move away from monolithic architectures in favor of multi-cloud and hybrid cloud computing or who must conduct business in a globally decentralized manner.
- Moving away from batch-oriented, static, centralized data and toward event-driven data ledgers and streaming-centric pipelines for real-time data events that enable more timely analytics.
Other significant issues, like strong federated data governance models and self-service tooling for non-technical users, are just as crucial for data mesh architecture as they are for other, more centralized, and traditional data management approaches.
CASE STUDY
An American Multinational Corporation Builds A Scalable Data Mesh With Knoldus And Dbt
Data Lake and Warehouse
The concept of Data Lake is to store structurally and spatially heterogeneous data sources with complex storage modes reliably. These data sources would then be accessible at any time to help support your optimal business decisions.
A Data Lake is practically synonymous with a modern data warehouse. As end-users are faced with larger and more complex challenges set by new innovations and the progress of technology, which in turn impose new demands on data storage systems, making the evolution of data processing and storage an inevitable next step in keeping up with such developments.
This shift in ‘Big Data‘ has resulted in new and conceptually different approaches to data storage – storing all types of data in a single location regardless of size and complexity, using increased computing power with massive parallelization and distributed processing. This approach provides customers the ability to process large amounts of data in a negligible amount of time and with a minimal load to current systems.
Knoldus’s Cloud Data Lakes and data warehouses integrate data from disparate sources, govern data quality, and deliver a single version of the truth. With data privacy and security built-in from the ground up, these ‘little-engines-that-could’ also deliver cloud-enabled scalability, self-service capability for democratized access, and faster time-to-market for new data products.
A Data Lake is practically synonymous with a modern data warehouse. As end-users are faced with larger and more complex challenges set by new innovations and the progress of technology, which in turn impose new demands on data storage systems, making the evolution of data processing and storage an inevitable next step in keeping up with such developments.
This shift in ‘Big Data‘ has resulted in new and conceptually different approaches to data storage – storing all types of data in a single location regardless of size and complexity, using increased computing power with massive parallelization and distributed processing. This approach provides customers the ability to process large amounts of data in a negligible amount of time and with a minimal load to current systems.
Knoldus’s Cloud Data Lakes and data warehouses integrate data from disparate sources, govern data quality, and deliver a single version of the truth. With data privacy and security built-in from the ground up, these ‘little-engines-that-could’ also deliver cloud-enabled scalability, self-service capability for democratized access, and faster time-to-market for new data products.
CASE STUDY
nD Accelerates Digital Transformation journey With Knoldus.
Advanced Analytics - AI
AI is one of the most effective kinds of advanced analytics. AI has revolutionized industries, including customer service, retail analytics, and industrial automation, by assisting businesses in detecting and anticipating behavior and patterns. It facilitates quicker, more accurate decision-making and can result in a significant competitive advantage. However, just 10% of businesses use AI successfully. The issue is that while many firms run successful pilot programs, it is challenging to integrate AI more deeply and broadly into their operations.
Our 10-20-70 rule summarizes our strategy, AI at Scale: To succeed with AI, you must allocate 10% of your budget to algorithms, 20% to technology, and 70% to integrating AI into new business models and methods of operation. We assist businesses in determining the most effective ways to utilize AI and create the procedures, job roles, and change management that let their efforts grow—and thrive.
CASE STUDY
Knoldus helps HPE not only build customer value, but also gain momentum for analytics transformation.