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The proliferation of data generated by enterprise applications, internet of things (IoT), sensors and click streams has presented a plethora of business opportunities to get closer to the customer and gain profitable insights. Our clients & their respective IT departments face the daunting task to provide a complete end to end framework to capture, analyze and distribute data within the organization. With advanced analytics and timely insights being the goal, there is a more acute need for a unified conceptual framework to cater to multiple data formats and sources.
Opportunities missed with the traditional analytics framework:
Let’s consider the traditional analytics framework where a question or a hypothesis is framed first and then the data is collected accordingly. It is then housed in data warehouses/marts, and subsequently analytics and insights are built on top of these data marts.
Will this process stand fit in the cognitive age where AI and machine learning methodologies have taken over to provide automatic insights never imagined before? Will this methodology cater to providing analytics with deep learning, unstructured and IoT based data sources?
The answer is very simple. Customers have already started realizing drawbacks with the current non-agile, non-flexible system and are looking towards the next generation analytics framework to cater to the ever-increasing need to form multiple personas within the same business including data scientists, business analysts, data stewards, security architects and many more. With each persona, there is an increased demand for data in a different format and capabilities that are not currently available with the existing framework. The growing needs driven by cognitive, IoT, an unstructured data increase this gap even further.
How will businesses cater to all the needs and capture the opportunities that will keep them competitive in the current market place and help set the tone for future?
The IBM Enterprise Analytics Reference Architecture provides you answer to these questions.
Warehousing and Analytics is changing …
- Gartner on it’s Emerging “Logical Data Warehouse” for business agility
- Forrester on it’s Emerging “Information Fabric 3.0” for business agility
From a Warehousing and Analytical Point-of-View …
- Traditional approaches lead to costly dumping grounds
- IT is currently solving by over consolidation which will have a reverse effect of higher costs as well as reduced business agility
A Quick Overview of The Enterprise Analytics Reference Architecture
Organizations are ever growing with acquisitions and mergers. A single organization houses zillions of applications. Solely for analytics, it hosts multiple applications for synthesizing, housing, governing and producing actionable insights from data.
The IBM Enterprise Analytics Reference Architecture provides a unified framework for the core architectural components and extensions to provide enterprises with capabilities to cater to ever growing analytics requirements. With this architectural framework, enterprise IT directors can have a bird’s eye view of all the capabilities and applications.
- New sources of data in a variety of formats can be quickly brought into the ecosystem and combined with existing sources to generate new insight, support analytics proofs of concept, and accelerate project delivery
- This architecture provides a common definition for data temperatures (Hot +warm +cold) across the business. This is a crucial aspect in today’s world where seamless integration of multi temperature data spread across the applications has become the norm.
- The architecture allows businesses to fully exploit information and insights. Information is available in a range of formats, is easy to locate and navigate, is presented intuitively and consistently with value adding context to link it to relevant business challenges. It helps the business exploit information to make better decisions faster by embedding analytics in their business rhythms
- Well organized analytics environments can help businesses to identify opportunities to apply predictive and prescriptive analytics which can effectively exploit big data to generate competitive advantage through Big Data labs
- The Reference Architecture provides the framework to help the business identify, acquire, ingest, store, and analyze unstructured and voluminous data. Thus, helping data scientists & analysts to address businesses most challenging business problems — using data mining techniques & machine learning techniques to generate new insights
How does The Enterprise Analytics Reference Architecture bridge the gap and set you on path of Next Gen Analytics?
Enterprise Analytics Reference Architecture will provide for….
- Integrated data platform that leverages existing assets and synergies with ongoing initiatives
- Enterprise wide registry of tools and assets to reduce redundancy and encourage reuse
- Data architecture that meets the needs of all types of audience with varying purpose, governance limits, etc.
- Managed inventory of assets and tools that are leading edge and collectively meet business needs
- Managed data environment with agile processes to accelerate response time to address dynamic business needs (e.g. new data sources)
- Tool assessment and adoption support to help business quickly cater to new operational and marketing needs
- Secure, compliant and scalable infrastructure (e.g. cloud based) to accommodate growing data and analytic needs
- Tool identification and evangelization to meet anticipated needs of business for scalability and analytics
The Journey Toward The Enterprise Analytics Reference Architecture….
The reference architecture framework is applicable to every customer in the analytics space embarking on the ‘next gen’ analytics journey. Analytics framework and infrastructure will be the base for enabling all round capabilities in future. Following the reference architecture will not only provide the customer a roadmap for the future operational architecture framework but also enhance his decisions making capability when implementing analytic solutions.
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