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Innovation Beyond The Four Walls

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November, 20, 2014

Cognizant Completes Acquisition of TriZetto, Creating a Fully-Integrated Healthcare Technology and Operations Leader

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The Cognizant Difference

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BIG DATA ANALYTICS

We live in a world of data abundance, but are underwhelmed by paucity of insights. The sheer complexity of storing and indexing large data stores, as well as the information models required to access and make sense of them, is burdensome to all companies, regardless of size and industry. Uncovering new insights from big data – contained in both structured (transactional) data and unstructured/semi-structured (interactional) formats -- requires prioritizing, organizing and validating on the back end and employing front-end analytical tools that are accessible to knowledge workers. It’s bigger than merely changing out technology; it also requires a delicate overhaul of key knowledge processes to ensure that signal is applied and noise is discarded.

A well-executed big data program provides the opportunity to uncover hidden markets, reveal unmet customer needs and cost savings opportunities and drive game-changing, meaningful improvements in everything from shipping efficiencies to medical treatments, to social media campaigns and related digital marketing promotions.

BEST PRACTICES

Our work with leading clients worldwide reveals the following big data analytics best practices:

  • Establish processes and organizational structures, then choose the best tools to solve high-value business problems.
  • As you experiment, continuously refine existing processes -- and create new ones -- and acquire tried and true tools to support big data. Be cost conscious, of course, but focus primarily on agility and speed of decision making, as well as ease of use by non-technical knowledge workers.
  • Don’t underestimate the importance of master data management. The need for a single version of truth is especially true for big data analytics initiatives.
  • Train your staff in the databases, technologies and ontologies required to build, manage and extend big data capture and subsequent analysis. If you lack big data analytics skills, you are not alone. Consider analytics as-a-service to provide a cost-effective, standardized way of supplementing internal talent and adding horsepower to prosper from big data analytics.

A more virtual and globalized world requires businesses to work proactively and anticipate change before it happens. Big data analytics can help businesses meet this challenge, but only if they align tools, processes and organizational structures to advance operational agility and deliver business results.

  • Our Latest Thinking

Gamification 3.0: The Power of Personalization
Gamification is applied in virtually every industry, yet remains remarkably unsophisticated. Gamification 3.0 is the next level of gamification -- combining the power of big data, behavioral insights and elements of psychology and neurosciences to motivate users to advance their personal and professional goals -- all in the context of their personality, emotions, habits and activities.
Building a Robust Big Data QA Ecosystem to Mitigate Data Integrity Challenges
With big data growing exponentially, the need to test semi-structured and unstructured data has risen; we offer several strategies for big data quality assurance (QA), taking into account data security, scalability and performance issues. Our recommendations center around data warehouse testing, performance testing and test data management.
Making Analytics Actionable for Financial Institutions (Part II of III)
To identify meaningful use cases for analytics-driven banking and financial services solutions, organizations need a thorough understanding of how customer interactions align with context and anticipate needs, while simplifying the decision-making process.
Maximizing Benefits via a Scoring Framework for Location-Based Services
With location-based services (LBS) growing meteorically, we offer assessment formulas for measuring completeness, correctness and coherence in the associated business databases.
Exactly Who Are Your Customers?
Predictive analytics provides the answers, enabling marketers to create value with consumers -- and drive brand growth for brand owners.
Trade Surveillance with Big Data
Real-time, high-frequency trading (HFT) is placing increasing pressure on regulatory compliance teams to keep up with and monitor the industry's widening pools of structured and unstructured data. Emerging technologies can help capital market firms use big-data analytics to collect, classify and analyze high volumes of data to formulate strategies for better surveillance, compliance and spot abuse.
Semantic 'Radar' Steers Users to Insights in the Data Lake
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Informed Manufacturing: Reaching for New Horizons
Although still in its infancy, informed manufacturing -- making the right information available in the right form at the right time -- is advancing across industry sectors. Cognizant's recent in-depth study involving interviews with manufacturing CXOs, engineering firms, service and IT providers, academia and industry analysts worldwide, revealed that while most companies understand the significance of informed manufacturing, many are proceeding carefully -- working to balance the conflicting priorities of managing day-to-day business while focusing on innovation and breakthrough initiatives. They see external support as a critical success factor.
Developing a Comprehensive Safe-Driving Program for Teens
Teen driving is a critical concern for families and a top-of-mind issue for insurers. Today, using advanced technologies such as telematics, the SMAC (TM) Stack (social, mobile, analytics and cloud), insurers offer all-inclusive safe-driving programs that help predict and prevent teen-related accidents--in real time.
Making Analytics Actionable for Financial Institutions (Part I of III)
To maximize ROI from their analytics platforms, financial institutions must build solutions that explicitly, visibly and sustainably enable real-time translation of data into meaningful and continuous improvements in their products, services, operating models and supporting infrastructures.
 

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