Closing the Analytics Inequality Gap
10 MINS READ
To help close the widening analytics inequality gap, big data management, stream processing, AI/ML engines, and real-time analytics are critical to fulfill customers’ needs for an easy-to-use platform for building analytic use cases from all types of data.
The pervasiveness of data and pursuit of digital transformation is forecast to continue and accelerate. The worldwide big data market is projected to grow from $42 billion in 2018 to $103 billion in 2027.1 Fueling that growth will be the 87 percent of organizations classified as having low business intelligence (BI) and analytics maturity2, those which are symptomatic to what we call the analytics inequality gap. Demand for actionable data insights continues to rise, with businesses that use big data seeing an 8-10 percent profit increase and a 10 percent reduction in costs.3 However, there are many obstacles that stall progress, including skill sets, processes, business culture, and technology. This paper addresses the technology side of that equation with a platform-based approach and best-practices methodology to help businesses accelerate their data analytics maturity while minimizing the costs, complexity, and timelines that hinder progress. To illustrate the benefits, this paper will compare and contrast what most enterprises are still doing today – building analytics use cases with a “build and assemble your own” building block approach, to the experience of using an end-to-end purpose-built platform, with the comparison framed by a best-practices development methodology.
The Analytics Inequality Gap
Organizations today are seeking to power their businesses with data and analytics. Data collection has transitioned from periodic data points to continuous streaming information. Forbes projects that more than 150 zettabytes of data will need analysis by 2025.4 The volume and diversity of our data continues to expand, at the edge with IoT devices, in the cloud with online services and infrastructure, in our connected devices, and in our data centers.
Analytics have become ubiquitous. Even consumers are bombarded with TV commercials from major tech firms touting how businesses are implementing artificial intelligence, machine learning, and augmented reality to build better products, streamline operations, and reach new customers. Insurance companies are analyzing telemetry data to deliver personalized auto insurance. Jet engine manufacturers and airlines analyze data from thousands of sensors to improve the performance and safety of air travel. Utilities are deploying smart meters and smart lighting to optimize efficiency, costs, and customer experience.
At the same time, the analytics inequality gap keeps widening; all but the largest organizations struggle to overcome the cost, complexity, and timelines needed to develop and implement use cases for data and advanced analytics. There are three common approaches used by most businesses:
1. Build your own
2. Integrate point products from niche vendors
3. Engage a major platform vendor or systems integrator (SI)
But most businesses encounter problems with each of these approaches. It's too time-consuming and inefficient to start each analytics project from scratch. Few businesses have the requisite in-house data science expertise to implement the analytics models needed for predictive and prescriptive analytics.
Data silos inhibit cross-domain analytics and the development of the connected intelligence that businesses seek. Creating a strategic and truly end-to-end analytics capability in-house can take years. Delivering real-time insights from disparate data sources often requires multiple tools, complex architectures, and extensive infrastructure. Rip and replace approaches with vendor lock-in are too expensive and are slow to implement. And specialty solutions from niche vendors require extensive integration and specialized skill sets.
These challenges have led to the widening analytics inequality gap where although many tier 2 and 3 businesses are making some progress with data and analytics maturity, they are simultaneously falling further and further behind tier 1s.
Introduction to TCS Connected Intelligence Platform
To help close the gap, TCS Digital Software & Solutions Group has introduced TCS Connected Intelligence PlatformTM (CIP) – a scalable enterprise insights platform that accelerates business outcomes with data and analytics. CIP pre-integrates all the technology necessary to help businesses move faster. CIP unifies big data management, stream processing, AI/ML engines, and real-time analytics to fulfill customers’ needs for an easy-to-use platform for building analytic use cases from all types of data – from data ingestion to data transformation to data analysis to advanced analytic modeling, without requiring a Ph.D. in data science, coding, or system integration.
Comparison of CIP to Building Block Approach
Unlike the typical situations where businesses struggle to stitch together numerous components, whether they come from niche vendors or major platform players, CIP is fully integrated to help avoid the need for multiple tools, reduce complexity, and increase speed. The low-code software platform ingests, processes, and analyzes big data (internal/external, IoT, real-time) to quickly convert data and information into visual and actionable insights. With no need to integrate multiple components, business users can get set up in just a day and start delivering use cases immediately. Because many organizations have made some progress with analytics, CIP is designed to complement and embrace customers’ existing IT environments and tools to keep costs down.
With CIP, customers get big data without the typical skills overhead, so existing teams can start immediately. CIP supports multiple use cases on a single platform and can be deployed on-prem or in the cloud and allows customers to continue to use current reporting tools. Furthermore, CIP has built-in enterprise-grade security and data privacy controls to support evolving regulations. Finally, to maximize value, TCS doesn’t charge by data volume. Customers can use as much data as they need, and capital expenditures are minimized with subscription-based pricing.
Value Factory Model for Use Case Development
To support the accelerated development of analytics use cases on an ongoing basis, organizations need a methodology that standardizes best practices into its key steps. TCS has documented a 10-step Value Factory Model so that practitioners can follow best practices with a repeatable process. This model is intentionally generalized, in that it is applicable regardless of the user’s choice of individual tools. CIP is explicitly designed as an integrated solution to leverage this model for the delivery of analytic use cases by providing all the components necessary to move from ideation to production.
Value Factory Model with and without CIP
TCS Connected Intelligence Platform’s architecture supports the Value Factory Model approach with end-to-end data management and analytic features and functionality to accelerate business value realization from big data. Enterprises that practice the Value Factory Model approach with a supporting architecture and ready-on-day-one capabilities like those available in CIP will more quickly start empowering business stakeholders and front-line systems with real-time analytic insights. Technology Partners that do the same gain a buy one, serve many systems to develop, deliver, and support branded analytic-driven apps and services for their clients.
The below table describes the implementation of an analytic use case using the Value Factory Model both with and without CIP.
Conclusions and Essential Guidance for Readers
Demand continues to rise for both batch and real-time data insights, AI, ML, and even deep learning. Analytics practitioners, from end-user businesses, as well as from Solution Provider organizations, require solutions to speed the time to value and meet demand. They must minimize complexity and the need for specialized skill sets. These solutions unify big data management, stream processing, and AI/ML engines to fulfill their needs for easily and repeatedly building end-to-end analytic use cases from all types of data.
The TCS Connected Intelligence Platform leverages the Value Factory Model to simplify and accelerate the implementation of analytics use cases. It provides an effective, flexible, and TCO-efficient way to accelerate the development and deployment of IoT, big data, and analytics initiatives with out-of-the-box pre-integrated, low-code, end-to-end capabilities. CIP can help bypass 12-24 month development cycles with a future-proof approach and platform which supports the continued development of new use cases. CIP aligns with the Value Factory Model to provide superior results, with less effort, in a shorter time, at a lower cost, and with reduced requirements for specialized skill sets.