Data Quality Empowers AI, ML and automation

Data quality is of critical importance, especially in the era of automated decisions, AI, ML and continuous process optimization. Corporations need to be data-driven and data quality is a critical pre-condition to achieve this.

The “Data-driven” here implies much more than just technology and data.  it requires a special mentality,  a leadership attitudes, that makes it possible to take informed decisions by systematically using data to improve business performance.

To lead the data-driven culture, corporations need to first establish a stream of clean, accurate, reliable, and active data feeds reflecting all major business activities. All these qualities are critical for employees to trust the data, the technology, and the tools – any limitation can prove to be the single point of failure in making the data-driven vision happen.

The success of machine learning (ML) and artificial intelligence (AI) applications requires large quantities of training and test data. This need creates critical challenges not only concerning the availability of such data, but also regarding its quality.  ML and AI are highly dependent on the quality of data used to train models. Incomplete, erroneous, Poor data quality can lead to can lead to inaccurate predictions, unreliable model performance and ultimately poor decisions.

This why this session will be focused on the Data quality which is of critical importance especially in the era of Artificial Intelligence and automated decisions. Share real success stories on how the use of data allowed smart decisions with real financial gains for the company

There are many aspects to data quality, including consistency, integrity, accuracy, and completeness. According to Wikipedia, data is generally considered high quality if it is “fit for its intended uses in operations, decision making and planning, and data is deemed of high quality if it correctly represents the real-world construct to which it refers.”

The challenges and the good practices in the data quality leading to a good AI and ML model

Debabrata Sanyal_MSNDebabrata Sanyal, Senior General Manager – Corporate Quality Assurance and Digital Automation – MSN Laboratories Private Limited

Debabrata Sanyal is Senior General Manager in Corporate Quality Assurance and Digital Automation in MSN Laboratories Private Limited. He brings 22 years of experience in diversified field of Quality Management predominantly in Quality Assurance, Quality Compliance and Regulatory Affairs. Debabrata previously worked for Regulatory Affairs and Quality System in renowned organisations like Cadila Pharmaceuticals, Torrent research Center, Aurobindo Research Center and MSN Laboratories Private Limited. In his recent assignment at MSN Laboratories Private Limited, Debabrata works for transforming the long-term digitalisation and automation targets into process and solutions.

Use Cases of drug discovery automation and ML models

Samiron Phukan, Senior director Computational (AI/ML) Modelling & Digital Transformation integrated Drug Discovery and Development at Aragen LifeSciences

Samiron Phukan has nearly 20 years of experience in the field of informatics driven solution in drug discovery and development. He had worked in various pharmaceutical companies and CROs in India in the field of drug discovery and development from concept to clinic in various therapeutic areas like oncology, metabolic disorders and anti-infectives using computational modelling and informatics. He had set up the state-of-the-art informatics laboratories in various pharmaceutical companies like Jubilant Biosys, Dr. Reddys’s Laboratories, Lupin ltd. Presently he is heading the CADD/informatics division in Aragen Lifesciences Hyderabad. In addition to his current role of heading the scientific team of computer aided drug design scientist, he is involved digitization, automation and development of various proprietary platform technology using ML tools to aid drug discovery and development.

How communication and data standards enable the automation and data management lifecycle

Burkhard Schaefer SILABurkhard Schaefer, AnIML Task Group Lead, SiLA Consortium

Communication and data standards form the bedrock of efficient laboratory processes. This presentation explores the role of SiLA (Standardization in Lab Automation) and AnIML (Analytical Information Markup Language) in driving automation, data management, and machine learning. SiLA ensures seamless instrument control and workflow orchestration, while AnIML structures data for consistent analysis and collaboration. Learn how the synergy between these standards fuels closed-loop experimentation, enabling optimized parameter adjustments using real-time insights. Through real-world cases, this presentation discusses the impact of standards in shaping modern laboratory practices, fostering innovation, and accelerating research.

Followed by Panel Discussion: How to set standards for the Lab 4.0




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