Transforming Data into Information: An Engineering Approach

A standardized, recipe-based approach adds value and turns data into actionable knowledge.

By Adam Fermier, Paul McKenzie, Shaun McWeeney, Terry Murphy, Gene Schaefer, Janssen Pharmaceuticals, Inc., a pharmaceutical company of Johnson & Johnson

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Patients, doctors, nurses, and regulatory agencies continue to express frustration over the cost of pharmaceutical products and the industry’s inability to control its contribution to the cost of healthcare. Despite ongoing discussions about risk management within the industry, drug manufacturers do not tolerate risk, or change, very well. For example, they may fail to invest in systems improvements though they have long-term goals of reducing overall costs. They often have trouble justifying the implementation of new technologies.

In their defense, these manufacturers undergo more scrutiny than those in most other manufacturing industries, due, in part, to past instances of poor quality control and compliance, such as those which resulted in the Barr decision in 1993 [1]. Lately, however, regulatory agencies around the world have encouraged drug manufacturers to manage risk more effectively and move to 21st century methods of R&D and manufacturing, embodied in FDA’s recently revised process validation guidelines, and ICH Q8-10, often referred to as FDA’s Quality by Design (QbD) guidelines. But even with passionate support, cultural change has been slow to take hold in the industry [2].

One hypothesis used to explain this resistance to change is a lack of guiding examples on the value of knowledge generation and management. Some companies are beginning to formulate knowledge management strategies to support a QbD approach, but to be effective such strategies will require a well-designed set of integrated tools [3]. Within the past 20 years, IT tools have become available to help transform data into knowledge, but they often represent point solutions to small pieces of the problem. The perceived costs of customization, integration and implementation have fueled the industry’s aversion to risk, which has prevented or limited implementation. 

In this article, we present an engineering approach to help transform data into information, and subsequently, knowledge. 

This approach removes the manual data integration steps required from the lab and plant to regulatory filings and quality assessments. By adopting a long-term, well-engineered strategy for knowledge creation, generation and retention, we believe that the pharmaceutical industry can improve the value that it presents to healthcare.

ANSI/ISA Standards

Knowledge management has been an elusive goal for the industry, even though it can clearly provide significant competitive advantages [4]. A manufacturer has plenty of knowledge about a medicine, but it may not be able to organize that knowledge in a manner that can be understood throughout the organization. The sheer volume of data gathered on a product can exceed billions of discrete data points across isolated systems associated with various parts of the supply chain. These systems may describe materials, equipment, quality control test results, development of quality control methods, and process data generated in development and commercial organizations.

Yet the isolated data systems reveal a significant opportunity if appropriately integrated in a common, system-independent, flexible data model. Even acknowledging this fact is a large step towards an enterprisewide architecture that can positively impact an organization’s capabilities and flexibility [5].

Fortunately, industry standards (ANSI/ISA–88 and ANSI/ISA–95) [6] have been developed to address this problem. These standards were designed specifically for process plant data, but can be applied to analytical and business enterprise processes. When process plant, analytical and business enterprise data are modeled together, they provide a framework to deliver enhanced knowledge management.

However, data standards alone will not achieve the ultimate goal of knowledge management, if the discrete data points aren’t well managed. A data warehousing approach solves that challenge by providing a system-independent data storage mechanism. 

Data warehousing is a mechanism that routinely takes discrete data, aggregates it into information by associating it with context or metadata and reports to a variety of consumers and investigators via a common integrated data source. 

ISA-88 and ISA-95 describe people, materials, and equipment and sequentially coordinate these resources into a recipe that can be executed to produce a product. They are generic enough to work across many industries, and have been successfully implemented in process control systems (PCS), data historians, manufacturing execution systems (MES), and ERP systems.

So far, however, these standards have not been applied to such pharmaceutical-specific systems as electronic laboratory notebooks (eLNs), laboratory information management systems (LIMS), or computerized maintenance management systems (CMMS). Through direct collaborations with system providers, the adaptation of these standards is proceeding and enabling smoother transfer of information. Many providers are acknowledging that secure execution is no longer an appropriate benchmark. Rather, the new benchmark is the flow and context of information. 

In building a data warehouse within development, precautions must be taken to ensure that the work adheres to the spirit of the regulations from a risk-management, patient safety and value perspective. Combining ISA-88 and ISA-95 data standards for workflow execution with a data warehousing strategy [7] offers the required flexibility for a long-standing knowledge management strategy. We call this approach the “Recipe Data Warehouse” strategy.

Recipe Data Warehousing

The Recipe “Data” Warehouse is so named because it combines the knowledge management strategy of data warehousing and the modularity of recipes (ISA-88 and ISA-95). The data warehousing strategy presented in Figure 1, commonly known as the “Kimball Model” [8], breaks the process into discrete functions. Aligned with good engineering approaches, a design is chosen to isolate and modularize a larger process into smaller functions to help isolate risks and enable scalability. 

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