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Enterprise Innovation

Enterprise Innovation

vertiv is both user and provider of Innovative technology, for us our ability to innovate and deliver outcomes that consist of business results is a game-changing capability.

Data Management

Data management is the development and execution of architectures, policies, practices and procedures in order to manage the information lifecycle needs of an enterprise in an effective manner.

Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.

Benefits of Data Management and Data Management Platforms

Managing your data is the first step toward handling the large volume of data, both structured and unstructured, that floods businesses daily. It is only through data management best practices that organizations are able to harness the power of their data and gain the insights they need to make the data useful.

In fact, data management via leading data management platforms enables organizations and enterprises to use data analytics in beneficial ways, such as:

  • Personalizing the customer experience
  • Adding value to customer interactions
  • Identifying the root causes of marketing failures and business issues in real- time
  • Reaping the revenues associated with data-driven marketing
  • Improving customer engagement
  • Increasing customer loyalty

Data Management Challenges

Although some companies are good to gather information, they do not treat it well enough to make sense. It is not enough to collect data; Companies and organizations need to understand from the beginning that the data management and data analysis is successful only think about how they get their value of raw data. They can exceed the raw efficient treatment systems, storage and data validation of data collection, analysis, and effective strategies.

Another challenge for IT management occurs when companies classify information and organize it first checks the answers they hope to collect the data. Each phase of data collection and management must lead to appropriate information and analysis in order to obtain the information necessary for the decisions of firms based on the data.

Data Management Best Practices

The best way to manage data, and finally get the insights needed to make data-driven decisions is to start with a business theme and to acquire the data to answer your that is that problem. Companies need to collect large water equivalent of information from different sources and then request best practices while they go through the process of storing and managing data, cleaning and data mining and then analyzing and visualizing the data to make their business decisions to inform.

It is significant to keep in mind that data management best practices lead to better analytics. By managing the right prepaid and data for analytics, companies are optimizing their Big Data. A few data management best practices organisms and shoulds companies strive to achieve performance:

  • Simplify access to traditional and emerging data
  • Scrub data to infuse quality into existing business processes
  • Shape data using flexible manipulation techniques

Business Intelligence

Now is the time to invest in the right technology foundation for your data center—one that will take you from data overload to data insights. vertiv is enabling the cost-effective adoption of big-data-and-analytics-driven business models that fuel innovation. With Intel® architecture-based advanced analytics solutions, you can efficiently and effectively capture, process, analyze, and store vast amounts of data of all types. Built in partnership with industry leaders in big data and analytics software, our highly available, performance-optimized, open-standards-based solutions will support your most ambitious analytics-driven initiatives.

Big Data Analytics

Big Data refers to the ever-growing amount of information we are creating and storing, and the analysis and use of this data. In a business sense, it particularly refers to applying insights gleaned from this analysis in order to drive business growth.

Big data and associated analytics are shaking up the way decisions are made across organizations.

Vertiv, a leading product and services provider offers solutions that can help organizations capitalize on the transformational potential of Big Data and derive actionable insights from their data.

Our business domain expertise coupled with rich technical competencies enable us to define a Big Data strategy for your organization, integrate Big Data into your overall IT roadmap, architect and implement a solution and empower your business.

Now, the emergence of big data analytics is sparking a third revolution in decision-making, opening up new possibilities. It’s been a given that many low-level routine decisions – deciding if a customer is eligible for a discount, or rerouting orders to achieve greater speed – can be automated through an analytics-fueled rules engine. But dig data also reaches higher up the organization as well. All up and down the corporate ladder, decision-making is shifting outward – either to new players in the enterprise, or to machines

We've had software as a service, platform as a service and data as a service. Now, by mixing them all together and massively upscaling the amount of data involved, we’ve arrived at Big Data as a Service , and Vertiv does it all for you

At Vertiv We Offer:

Big Data Consulting

  • POC/POV, Strategy Road map, Tech evaluation & recommendations
  • Architectural Consulting, Capacity Planning, Performanc Big Data Development and Implementation Services
  • Data modelling and algorithm development
  • Develop map reduce code, transformations, custom code
  • Data integration services and search & document indexing
  • Data quality and metadata management
  • Reports/Visualizations, analytics (machine learning, statistical programming, text mining) Big Data Testing , Provisioning and Automation
  • Installation and configuration
  • Automation (RPA , NLP , Machine Leaning and Cognitive Automation) Big Data Security
  • Data governance
  • Identity and access management Big Data Support & Managed Services
  • 24 X 7 support, preventive maintenance
  • Configuration, security and policy management
  • Backup, recovery, archival support and big data admin
  • BI & Data Visualization
  • Data Science


  • Robotic Process Automation
  • Business Process Automation
  • Accounts Payable Automation
  • Claims Processing Automation
  • Intelligent Process Automation
  • BPO Automation
  • Back Office Automation
  • Call Center Automation


  • Desktop Automation
  • Web Automation
  • GUI Automation
  • Screen Scraping
  • Citrix Automation
  • Mainframe Automation
  • SAP Automation Software
  • Excel Automation

Digitization and Channel Banking

Digitization Channel Banking is loosely defined as a set of proposals by BCBS as framework for the next generation market risk regulatory capital rules for large, internationally active banks. Think about it as the perspective successor of Basel III (some people start to call it Basel IV already).

Compared with existing market risk regulatory capital rules (Basel II/III), FRTB incorporates the following key gradients:


  • Expected Shortfall (ES), aimed to capture general market risk (roughly corresponding to the stress VaR component in Basel 2.5). Each risk factor is subject to a liquidity horizon scaling based on its liquidity profile (i.e., ease of unwinding the positions in market without significant impact on transaction prices).
  • Incremental Default Risk (IDR), which is designed to capitalize the jump to default risk of debt (including sovereign) and equity trading positions. Securitization products ate completely disallowed in internal model treatment.
  • Finally, there is a capital add-on based on stress testing and scenario analysis which is designed to capture the risk of non-modellable risk factors (to be elaborated next).
  • In particular, eligibility and soundness of internal models are assessed and approved or disapproved on a (trading) desk by desk basis, based on criteria including P&L attribution, backtesting and model independent assessment.

Robotics Process Automation

Vertiv provides Robotic and Cognitive Automation (RCA) services to help our clients address their strategic and critical operational challenges. We help clients in a number of ways, ranging from shaping automation strategies and identifying key opportunities to designing and implementing robotic and cognitive enabled business operations and processes at scale, as well as providing robust automated managed services. Our approach places business outcomes and successful workforce integration of these RCA technologies at the heart of what we do, driven heavily by our deep industry and functional knowledge. Our thought leadership and strong relationships with both established and emerging tool vendors enables us and our clients to stay at the leading edge of this new frontier Our global Vertiv firm has a large and growing capability, with a range of thought leaders. Yesterday’s business transformations focused primarily on savings: cutting costs, gaining efficiencies, and establishing the right controls and risk-management procedures. Today, it is that and much more. In this post-crisis world, companies have done a risk reset. Like the CFO position itself, business transformation includes a new focus on enabling corporate strategy, capital agenda and competitive advantage in the marketplace. But today, Taking the business function from number crunching to value creation requires a comprehensive transformation strategy – it requires thought leadership, intellectual curiosity, professional commitment, and an ability to innovate and that's where Robotic Process Automation steps in and does a huge , quick and truly transformative favour to the business processes

Top RPA and Cognitive Candidate Processes

  • Human Resources - Automation of activities such as payroll and absence management, starter and leaver processes and employee data management
  • Finance & Accounting - Automation of accounts payable, accounts receivable, order management, invoicing, collections and reporting processes
  • Procurement - Request management, approval and sign-off, supplier management, invoice reconciliation and asset management processes
  • Supply Chain - Including automation of demand management, supplier and supply management, management of transportation and inventory processes
  • Customer Experience Management - Processes including customer support, technical support, billing and account management and customer loyalty programs
  • Regulatory & Internal Reporting - Processes for the generation of regular reports which require data extraction from multiple sources and manipulation into a report output