MassTLC Published Second Part of the Post on Effective Utilization of AI in Enterprises – Part 2 Focused on Proper Use of Metrics

The second part of the post on 'AI for Enterprises' titled ‘AI for Enterprises. Part-2: Metrics for Matriculates?’ has been released on October 25, 2022 at the MassTLC website and the corresponding extended version titled ‘AI for Enterprises. Part-2: Metrics for Matriculates? [Extended version]’ at ReSurfX (this) website. The approach taken in this post was similar to the previous post in the context of data quality and data utilization in the previous post ‘AI for enterprises. Part-1: Where are we in tackling the popular adage GIGO?’. With focus on data quality (garbage-in-garbage-out) as the central theme – that previous post also had a companion version ‘AI for enterprises. Part-1: Where are we in tackling the popular adage GIGO? [Extended Version]’ in this website that included more details on ReSurfX solutions. In both cases the extended version had lot more information on ability of ReSurfX solutions to deliver superior outcomes in part by tackling problems and pitfalls outlined in the posts and approaches outlined to overcome them. The second part of the post focused on proper use of metrics in establishing and expansion of (deriving value from) digital transformation and artificial intelligence (AI) in enterprises much more than technical aspects, including aspects such as enterprise architecture and roll out problems. The point was more about use of classical metrics when they are inadequate or when their use are is inappropriate – hence need to design new ones, the capability to do that and knowing these is are important for enterprises which also allow them to be open to adopt them. The post also covered many bite sized concepts of guiding principles used across all initiatives at ReSurfX when designing product functionalities or in discussing relevance of new findings in the context of value to customers. Together these two posts covered lot of grounds in effective data utilization both as improvement in efficiency and innovation (as prediction of insights – reliably detecting known insights that typically are difficult to predict even with lot of data and resources as well novel insights to guide decisions) for better outcomes vertically across enterprise operations.

The two posts tied together the problems, pitfalls and solutions and moved on to discuss how ReSurfX handles these in addition to leveraging the novel data-source agnostic (hence sector agnostic) machine learning approach “Adaptive Hypersurface Technology (AHT)” which lead to development of incredibly powerful functionalities and product yielding valuable results improving outcomes, innovation and return on investment (ROI) of enterprises. Lot more details of the enterprise SaaS product of ReSurfX, ReSurfX::vysen, and results derived from them were covered in the extended versions of the posts in ReSurfX website. The extended versions also covered the immense depth in motivating factors behind the choice of the beachhead data/technology of ReSurfX::vysen from multiple key perspectives including data, machine learning (ML) to prove their value as well as the incredible increase in information content.

When asked if he would like to add anything besides what he covered in this article, our CEO and Cofounder Suresh Gopalan said:

  1. The results we added there speak for themselves. When thinking of the power and reliability they convey even from the small fraction we have shared publicly I am reminded of a statement by a research based customer from a healthcare system who when talking to us about value remarked: “when it comes to insights or aiding improvement for that, it is difficult to put a value on it, as the right one is priceless”. That statement is understandable as many advances in life sciences and healthcare (including patient care solutions and decision support) are result of novel insights that are few and far between and result of a long and protracted effort. I am very pleased that our culture at ReSurfX also places high value on customer feedback.
  2. Suresh also added when putting together the simplistic pictorial depiction of SyRTOP configuration used in extended version in second part of the posts he was reminded of the first question our tech lead asked about the picture (which he did not get to see before release) in our post ReSurfX in 2021 – Best-in-class Outcome Predictors, Innovation Catalysts and ROI Multipliers. He said the question was “why the pictorial in that post did not include a data lake which is of incredible value to customers – as data ingestion layer remains a big pain point for most enterprises, and the variation of data lake concept that we use at ReSurfX is so much ahead of most solutions in the market”.
  3. How our product and functionalities are built and their power are reflected in results shared some of which are articulated in these posts – but what could not be expressed adequately in the posts is the pleasure in working with incredibly talented team where each member is focused on the same set of values.

Consistent with comments mentioned earlier, our CEO expressed that sentiment by sharing the post on in LinkedIn® as: “AI for Enterprises. Part-2: Metrics for Matriculates? [Extended]’ covers technical through enterprise architecture aspects in adopting the AI wave. I highlight seemingly simple guiding themes we use at ReSurfX that help our awesome team develop immensely creative, powerful and robust solutions for complex problems of great value to customers.”.