The data generation and collection strategies at the center of life sciences organizations and their manufacturing processes have evolved dramatically, especially in recent years. These organizations now collect and store huge volumes of data across their operations, both on and off premise, across multiple geographic locations, in an increasing number of separate data silos.
These advances have coincided with the proliferation of connected sensors and increasingly inexpensive storage, leading to an Industrial Internet of Things (IIoT) ecosystem projected to generate more than 4 trillion gigabytes of data per year by 2020, according to IDC Research.
New, advanced data analytics have a huge positive impact on the growing volumes of data in many sectors, from retail to financial. So why aren’t all these new analytics widely leveraged in the life sciences industry? With so much data and the promise of so many new technologies, why is it so difficult to gain the same benefits as other sectors? Why do so many life sciences organizations still feel like they have too much data and not enough insight?
We believe this gap — between the data these organizations have and the insight they want — exists because most data analytics solutions fail to address the unique challenges and opportunities presented by life sciences applications.
When we talk about data analytics, we mean any software enabling process engineers or scientists to:
• Create a cleansed, focused data set for analysis through assembling, aggregating, or “wrangling” data from various sources, including data historians, offline data, manufacturing systems and relational databases
• Investigate operations data using “self-service” tools to rapidly analyze alarm, process or asset data for ad hoc or regular reporting
• Publish or share insights and reports across the organization to enable data-driven action, or enable predictive analytics on incoming data
• Many data analytics solutions claim to offer some or all these things — with the goal of finally closing the gap between data and insight. But are they successful? And how do you evaluate success?
We propose five questions every process manufacturing buyer should ask when evaluating an advanced analytics solution.
1. IS THE DATA ANALYTICS SOLUTION DESIGNED SPECIFICALLY FOR PROCESS MANUFACTURING, AND CAN IT HANDLE TIME-SERIES DATA AND SOLVE PROCESS MANUFACTURING PROBLEMS?
Many data analytics solutions are general purpose and designed for IT, and as such aren’t a good fit for life sciences, hence the first question.
Anyone who works with industrial time-series data knows it isn’t like other data. No matter the industry — from pharma to mining to oil & gas — the data produced and the assets involved present a tangle of convoluted relationships and contextual challenges.
Whether you’re looking at a pharmaceutical facility, a process development lab or a refinery — there are historians collecting data across many different protocols used by multiple vendors across a disparate array of equipment, of varying ages and implementations.
These systems are typically producing data at speeds and volumes that other industries find dizzying, and at uneven intervals that can confound conventional relational databases. All this data also needs to be cleansed to be useful.
To make matters worse, all these events and signals lack the associated context to make them meaningful on their own — a problem compounded further when assembling data from multiple sources, which requires the addition of these key relationships.
Finally, time-series data is hard to navigate. Sensors have timestamps that need to be aligned and aggregated across specific ranges in time, obstacles not found with transaction data.
The right data analytics solution will work exclusively with industrial time-series data. This will enable the solution to go far beyond spreadsheets or general purpose data analytics software designed for relational or IT-based applications.
This means correctly handling, displaying and navigating time-series data. This enables users to capture the right data to solve real life sciences problems.
Collecting sensor data isn’t a trivial task. It’s also often the start of a longer process that involves cleansing, adding context and performing calculations — a process that needs to leverage the hard-won insights and institutional knowledge of engineers.
2. DOES THE ANALYTICS SOLUTION RELY ON YOUR EXPERTS OR THEIR EXPERTS?
Beware of vendor experts bearing correlations. Many data analytics vendors know their own technology extremely well, but don’t know much about process manufacturing. This can lead to a focus on the analytics themselves rather than the implications of any findings — and, in turn, an emphasis on correlations over outcomes.
The key to positive business outcomes for process manufacturing is empowering internal experts. A typical process manufacturing organization has a great deal of expertise at its disposal, spread out across a skilled front line of process engineers, scientists, team leads and other technical specialists.