There’s a staggering amount of financial technology (FinTech) innovation that can help your business. Along with that, machine learning, AI and blockchain, are helping financial services companies to understand customers better and respond to their requirements faster. However, many organizations are unsure how best to implement these new technologies in ways that will truly help their business.
Cloud Underpins Next Generation Technologies
Big Bata, cognitive computing, IoT, AI and robotics are all underpinned by Cloud. This means that Cloud is one of the most critical investment decisions any organization can make, and it should not be considered in isolation of other factors.
Endpoint devices are an important aspect of the Cloud ecosystem. Although many leaders understand the importance of providing endpoint devices specific to job requirements, your device strategy is also critical because it partly determines the security readiness of your organization. Business grade PCs with the right hardware and software features can help prevent against unauthorized data access or transfer out of your organization, hacking, malware and more besides. No wonder, for example, that almost 40% of SMBs* are planning on investing in two-factor authentication approaches in the next 12 months.
Big Data is Becoming an Integral Part of IT Strategy
Much of financial services emerging technology is data-driven. Be it AI or cognitive intelligence or personalized banking, all these technologies run on the foundation of big data. Without adequate compute power it will be difficult for data scientists to harness the full potential of this data.
You need to view all your data in a way that you can make sense of it. Acquiring the right skills for the job is your first step. An equally critical step is equipping these skilled employees – data scientists and analysts – with the right end computing devices to maximize the value of data visualization.
Devices for data analysis and visualization require:
- Processing Power: depending on the quantity of data and type of analysis, you may need a dedicated workstation with server-grade Intel Xeon® processors. Less intensive data processing can be done on regular desktop computers. The key point is to ensure your data analytics hardware is up to date and scaled for the task. It's more expensive to buy equipment for your current needs, only to find in less than a year you need more powerful devices. Always buy more performance (and storage) than you currently forecast as your need.
- Graphics: typically graphics performance is of less importance for finance use cases. Unless you are rendering high-resolution video or animation, you won't need professional-level graphics like NVIDIA Quadro® but if you are connecting multiple displays you probably need a dedicated graphics card to support output at high resolution.
- Displays - don't just focus on the resolution and size of displays; look at the contrast ratio (how 'sharp' images will look) and colour gamut (how many colours it can display). Consider also ergonomic factors.. will operatives be using it for long hours through the day? If so, the simple tilt and pivot adjustment of most displays might not be good enough; consider mounting on an extendable arm which allows greater degree control over both angle and height.
*Forrester Business Technographics