PICKING YOUR ENTERPRISE INSIGHTS PLATFORM

Data Infrastructure build out shall allow you to pull all kinds of data together, be it customer data, operations data, financial data, marketing data or transaction data or even semi structured data to create a single source of truth. Not getting your enterprise data architecture right can become a key reason for failure of your data strategy.

Technology Trends & Business Drivers

Data Science, Machine Learning & AI, Open API have been trending and showcasing a high level of interest both within the business and the technology community, particularly start-up ecosystems over the past 2-3 years. There are various business driver both old and new which are equally responsible for the excitement around the subject. Zdnet article on Enterprise Technologies to Watch in 2016 rightly highlights some of these trends.

Some of the key business drivers are:

1. Digital Economy

The rise of the digital economy, driven largely by a variety of new generation companies in the online market place space (eCommerce, retail, product discovery), on-demand economy as well media start-ups.

The rise of digitization in industrial and old economy businesses as well as governments across the world steadily over years. Further, there is a pressing realization among these businesses that they need to embrace the critical change in their industry and innovate and compete against the impending digital disruption from the rise of the new challengers in almost every industry vertical. These new age disrupters are using data to constantly track and analyze consumer behavior and optimize product deliverable and business models to serve the consumer need rapidly.

Enhanced Interests from governments at all levels both in developed and developing economies to improve productivity and enhance monitoring and delivery of services through digitization is also pushing the need for Analytics. As a matter of fact, a lot of government initiatives on open data across the world is also helping unlock the potential to use analytics for larger public good and driving more and more citizen analysts.

2. Further Penetration of Smart Devices

Smart Phones & Mobile Applications has brought in and is slated to bring in more than 7 billion people connected to the internet via smart phones and tablets by 2020.

3. Rise of Internet of Things

The rise of Internet of Things and Everything is projected to unlock the potential to gather enormous amounts of data and details about almost everything. This finds application from smart health to smart homes, to smart buildings to smart cities to industries to almost everything that one can really imagine in the wildest of one’s thoughts.

The above drivers are creating new challenges and opportunities to harness and understand the data deluge and leverage analytics for smarter and faster decision making, to better control delivery of goods and services to end consumer.

Technology Architecture & Digital Business

At Cynepia, we believe that Technology Architecture”will play the most critical role in the evolution from the current state of enterprise architecture.

What are the goals for such an architecture?

1. Enterprises need to be able to manage their data assets and derive insights in real-time from these data assets, while looking to optimize costs.

2. Architecture shall further enable enterprises bring all their digital assets together and help them make far better decisions.

3. Architecture shall enable tighter and better collaboration among decision makers.

Right enterprise architecture

At Cynepia, We believe a right technology architecture will be crucial for building out a pervasive, fast and scalable data-driven enterprise. Architecture scalability itself is really an aggregate outcome, which is derived out of the scalability of individual software components underneath which are tied together and the architectural choices made for each of them and how they interface and connect communicate with each-other.

Even though there is a consensus emerging on Lambda architecture at a high level, there are various nitty-gritties which are often left out which further add to the bottlenecks and restrict application scalability and availability. One of the key considerations for architectural scalability shall include query latency measure which in layman’s term could mean “Time it takes to issue a query and publish the results to the end application or dashboard”

Some of the key architectural layers to be considered include the following:

Micro-services Architecture

In micro-services architectures, apps are built as a suite of small, semi-autonomous processes that perform specific tasks and use APIs to communicate with each other. Microservices are meant to be easy to use and scalable, and increasingly figure in web, mobile and internet-of-things apps. We at Cynepia like micro-services since the problem of scaling as well as deployment is distributed and each of these services can be scaled and deployed independently of the other. With some of the other design patterns, it can also help serve the availability of the services.

Analytics, In-memory, Real-time Streaming as the Foundational Service

Even though there is a consensus emerging on Lambda architecture(enables both batch and real-time processing) at a high level, there are various nitty-gritties which are often left out which further add to the bottlenecks and restrict application scalability and availability. One of the key considerations for architectural scalability shall include query latency measure which in layman’s term could mean the following

1. Time it takes to issue a query and publish the results to the end application or dashboard”

2. Ability to gain a single source of truth by integrating business data assets from disparate enterprise or third-party system, which may further be in various formats, batched or real-time, slow or fast data.

Open Sources & Open Interfaces

More and more data systems will be built with some of the excellent work done in the Open-source community. These open-sources will become foundation building blocks in any system design. Further, The architecture shall leverage and provide on various Open Interfaces for tigher collaboration with other enterprise systems or third-party systems.

1. Enable leveraging the memory and compute power of commodity hardware using advancements in various big data technologies such as Hadoop, Apache Spark, No-SQL, Apache Mesos, Yarn etc.

2. Ability to scale up and down the Data Infrastructure (Private Datacenter or Public Cloud or Hybrid) on need basis.

In-built Collaboration

Besides Analytics, ability to collaborate between systems, between data and data users, between analytics and business users will be key component to any enterprise architecture.
For further information on Cynepia and More about our solution architecture, write to us sales@cynepia.com