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Pharmaceutical Technology BIOLOGICS AND STERILE DRUG MANUFACTURING 2018 15 FIGURES ARE COURTESY OF THE AUTHORS. bioprocess characterization and scale comparison project at vari- ous stages of the data analytics continuum. Case study In total, a development project for a fed-batch cell-culture bio- logical process encompassed 75 batches. Metabolite measure- ments were taken once per day. The metabolite data included cell performance metrics, selected process conditions, and nutri- ent concentrations. In addition to the batch evolution trajectory measurements, initial and final batch conditions were registered. Some calculated parameters were added to the trajectory data to en- hance information content and assist in interpreta- tion. These data come from three operating scales (micro, micro+, and pilot) including both stable operating conditions and some runs planned using design of experiments (DOE) (2). Descriptive analytics There are multiple uses of these types of data. In the descriptive analytics stage, data analytics tools such as partial least squares (PLS) or orthogonal partial least squares (OPLS) can be used for batch evolution modeling to understand what happened within and between batches during the batch life- time (3). Popular graphic representations involve control charts displaying time evolution trajecto- ries of important process variables or model sta- tistics to diagnose which batches conform to good normal developmental trajectories and which batches do not (see Figure 2). Another perspective often used in descriptive analytics is to compare batch-to-batch variations by representing the complete batch trajectory in a single summary. In this comparison, an often- used data analytics tool is principal component analysis (PCA) (3). A typical output from a PCA is shown in Figure 3, in which color coding is used to represent batch scale and marker sizing is used to represent peak viable cell density (VCD). The pilot scale has the most consistent operation with high peak VCD, as shown in Figure 3, where the green markers are all of similar size and operat- ing conditions. The micro scale is most similar to the pilot scale, as indicated by the blue markers in Figure 3. The micro scale has consistent opera- Figure 1: Illustration of the data analytics continuum. At the base level of descriptive analytics, data aggregation and data mining provide insight into the past and answer: "What has happened?". Diagnostic analytics examines data or content to answer the question, "Why did it happen?", characterized by drill-down, data discovery, mining, and correlations. Predictive analytics uses statistical models and forecast techniques to understand the future and answer: "What could happen?". At the pinnacle of data analytics, prescriptive analytics uses optimization and simulation algorithms to advise on possible outcomes and answer: "What should we do?" or "How can we avoid this happening?".