{"id":15477,"date":"2023-08-07T17:18:55","date_gmt":"2023-08-07T17:18:55","guid":{"rendered":"https:\/\/www.thermofisher.com\/blog\/analyteguru\/?p=15477"},"modified":"2026-08-25T21:36:14","modified_gmt":"2026-08-25T21:36:14","slug":"developing-quality-mam-workflows","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/analyteguru\/developing-quality-mam-workflows\/","title":{"rendered":"Developing Quality MAM Workflows"},"content":{"rendered":"\n

Software landscape<\/h2>\n\n\n\n

Efficient lab workflows<\/h3>\n\n\n\n

Multi-attribute monitoring methods (MAM) are a fitting illustration of streamlined productivity; a simple workflow on the surface that economizes work time while still containing a plethora of information underneath for the \u201cjust in case\u201d scenario of needing it later.<\/p>\n\n\n\n

As scientists, we pay attention to the tools that enable multitasking and simplify our complexity, but we have traditionally struggled since the options are either complex but numerous, or simplistic but functionally incomplete.<\/p>\n\n\n\n

\"Ardia<\/figure>\n\n\n\n

Software conundrum<\/strong><\/h3>\n\n\n\n

To simplify the management of \u201ctoo many things,\u201d we sometimes default to generic tools that can be configured for specialized tasks. Programs like JMP or Excel are examples of generic applications typically employed for data summarization and reporting. However, they must be specialized to handle workflows through generation of specialty calculations or macros.<\/p>\n\n\n\n

On the other hand, specialty applications can offer an abundance of work-specific workflows that generate great results, but too many of them leads to a \u201cdrawer full of gadgets\u201d built for one specific method \u2014 and we still struggle to combine results from them later.<\/p>\n\n\n\n

Connecting people, instruments and data<\/h2>\n\n\n\n

Centralized data<\/strong><\/h3>\n\n\n\n

To challenge the idea that data must live apart at all, we see a structural change where the data is first agnostically centralized \u2014 regardless of the source \u2014 and then able to be utilized by specialty applications that can save results back into the central pool. This is where the \u201cdata lake\u201d ideology really gets started.<\/p>\n\n\n\n

Microservices-style software<\/strong><\/h3>\n\n\n\n

To take advantage of a setup like this, laboratory software is shifting to connect specialized applications through a central platform built around non-specific data of all types. This shift includes the foresight for the platform to have agnostic ability to control data generation (instruments) as a separate application, be able to utilize workflow-specific processing applications for calculations, and be able to host non-specific report writing applications or even trend larger sets of data across time periods.<\/p>\n\n\n\n

This shift is sometimes described as a microservices-style software. Each part is separate and represents a smaller subset of functionality to the overall process of collecting, employing, and summarizing data but they coalesce into a platform.<\/p>\n\n\n\n

Thermo Scientific Ardia Platform<\/strong><\/h3>\n\n\n\n

This platform approach is at the forefront of our minds at Thermo Fisher Scientific. We introduced our Thermo Scientific Ardia Platform<\/a> to house a universe of data and have started the journey of connecting our current applications.<\/p>\n\n\n\n

\"Ardia:<\/figure>\n\n\n\n

Our first fit-for-purpose offering connects Thermo Scientific Biopharma Finder, Thermo Scientific Xcalibur, and Thermo Scientific Chromeleon Chromatography Data System (CDS) to the central Ardia Platform. <\/p>\n\n\n\n