Author: Patrick Murphy

Analytics – a buzzword you rarely, if ever, associate with brewing. This mysterious subject seldom finds its way into the garages and kitchens of the modern-day homebrewer, and yet analytics has been the catalyst to thrust the beer industry into its next great paradigm shift. Professional breweries of all sizes recognize the dynamic ability of analytics to not only increase their bottom line, but also improve the progress, quality, and consistency of their beer in the most efficient way possible. As homebrewers, we tend to focus on more tangible means of improving our beer, often trusting in better equipment to rectify any faults or difficulties, even though this method is costly and finite. To achieve sustained improvement, homebrewers must adopt an analytical approach by gathering and analyzing data throughout the brewing process with a special focus on fermentation.





What Is Analytics?

What is analytics anyway? Before we jump into that, let’s identify a few things that analytics is NOT. Analytics is not intimidating, and it is not difficult. Anybody with an elementary understanding of mathematics can wield analytics as a tool in their brewing repertoire.

By definition, Analytics is “the patterns and other meaningful information gathered from the analysis of data.”1 Think of it like a method used by detectives – the data points are clues, the detective looks at all the clues and uses them to paint a picture, and then the detective looks at the whole picture to solve the mystery. Break it down into three simple steps:

Gather Data (gravity, temperature, etc.)

Transform Data (graphs and charts)

Analyze (develop questions or answer them)

Most analytical projects begin with what’s called descriptive analysis, which recounts past events. But it’s not always about uncovering what happened, predictive analysis forecasts what is likely to happen in the future. A homebrewer might use descriptive analysis to figure out what happened with the last three batches of American Pale Ale and then use predictive analysis to predict what will happen with the fourth batch.

Analytics accommodates any kind of data, so don’t get wrapped up in the numbers. It’s just as easy (and more fun) to gather subjective data and analyze it in a similar fashion. Subjective data elements are things like taste, aroma, clarity, mouthfeel – anything you can gather with your senses. By collecting both objective and subjective data, you can compose a thorough foundation to analyze and improve your beer.

The difference lies in the graphics. Gathering data and compiling it into tables or spreadsheets yields tedious and unsatisfying returns. Although the answers are in the data, they are likely not immediately recognized. Transforming the raw data into a visual representation (i.e. – a graph or chart) changes your perspective and increases the rate at which you can process the data. According to an article by Thermopylae Sciences and Technology, “humans respond to and process visual data better than any other type of data… the human brain processes images 60,000 times faster than text, and 90 percent of information transmitted to the brain is visual.”2

Solve Problems

As a homebrewer, why should I care about analytics? It sounds like a lot of extra work and I’m not sure it’s worth my time, or even that I have the time – all legitimate concerns for the busy homebrewer. But you should care, because analytics can help homebrewers solve problems and identify new ones.

Consistency

Sustained consistency in brewing continues to be an ability that is nearly monopolized by professional breweries. For the homebrewer, recreating that perfect batch over and over again comes with great difficulty and maybe a little luck. Using analytics to track fermentation patterns will open your eyes to the world of inconsistencies that are likely hindering consistent results.

On brew day, you spend several hours creating wort – a liquid that yeast cells transform into beer. Given that you have consistent brew day habits, recreating that wort should come easy. It’s during the fermentation that minor inconsistencies blossom into major changes for your final product. Let’s say you brew Batch A and like the results, so you recreate it by brewing Batch B with the same recipe and methods. By gathering fermentation data (gravity and temperatures) throughout the process, you can create fermentation curves – a visual representation of changes in gravity over time – for each batch. After trying B, you find that the results are different than A. You know that the wort composition and characteristics matched for each batch and look to the fermentation pattern for any inconsistencies. Matching the shape of the fermentation curve from one batch to the next increases the likelihood of consistent results. When batches deviate from the “normal” curve for that particular recipe, you may begin to notice inconsistencies in the final product as a result of that deviation.

Predicting Final Gravity, Transfer Timing

Rookie (and seasoned) brewers often struggle with determining when their beer is done fermenting. Has it reached final gravity? Can I transfer it to the secondary fermenter or keg it? Prematurely ending primary fermentation has many implications, mostly bad, that homebrewers often avoid by fermenting for a pre-determined amount of time, e.g., two weeks being a common timeframe. While this approach almost guarantees adequate attenuation, the beer likely sits on the yeast for longer than necessary, eating up time that could otherwise be occupied by a dedicated conditioning phase. Professional breweries, such as Deschutes, are using analytics to model their fermentation patterns to predict final gravity, making transfer timing and brewing schedules far more efficient. For the homebrewer with limited fermenter space, being able to ferment more batches in a shorter timeframe sounds ideal. The analytics of this plays out in a two-step process:

Step 1 – Compare past attenuation ranges of the yeast strain you are using

For strains that you have not brewed with before, look on the manufacturer’s website for the attenuation range, it will at least give you a place to start. For strains that you have brewed with before, compile a list of the attenuation values from any past batch that used the same yeast strain that you are using for your current batch.

Batches Using Strain X Attenuation Value

Batch C 80%

Batch D 75%

Batch E 70%

Batch F 73%

Using these attenuation values from past batches and the current batch’s original gravity (OG), calculate a list of final gravities (FG).

((OG – 1) x (1 – Atten.)) + 1 = FG

Convert the attenuation percentages to decimals (e.g. 80% -> 0.80)

Current Beer – Batch G

Original Gravity of G = 1.057

Atten. OG of Batch G FG

80% 1.057 1.011

75% 1.057 1.014

70% 1.057 1.017

73% 1.057 1.015

The highest and lowest final gravity values define the range the current beer should land in, giving you extreme upper and lower boundaries. Averaging all the final gravities suggests the most likely final gravity value for the current beer.

Upper FG Limit – 1.017

Lower FG Limit – 1.011

Average FG – 1.01425 –> round to 1.014

Put these values to work in a simple X-Y chart, graphing the upper, lower, and average values as horizontal lines.

And just like that, as early as brew day, you have a very clear and analytically sound prediction for this batch’s final gravity, and your chart is set up and ready to populate with fermentation data.

Step 2 – Confirm final gravity by analyzing the change in gravity on a fermentation curve

As your beer ferments and you add data, the fermentation curve begins to take shape, likely starting out with a gradual decline followed by a rapid drop during the yeast growth phase. As the yeast slow down and begin flocculating, the fermentation curve will begin to level out, with each day having a smaller change in gravity than the last. Visualizing the fermentation curve makes analyzing the change in gravity very easy. Simply put, when the gravity curve yields little to no change from one day to the next, primary fermentation is complete. The gravity curve should have leveled out within the final gravity range and should be close to the average final gravity value. If the gravity curve leveled out far beyond the final gravity range, then there may be an underlying issue. Given that the curve has leveled out at a reasonable value within the range, it’s safe to say that the beer is done fermenting and you are safe to take the next step in your process.

Gathering Data

You don’t need all the pieces of the puzzle to figure out what the picture is. But the more pieces that you fit together, the more detailed the image becomes. Brewing analytics works in a similar fashion – the more data a brewer gathers, the more insights they have into various aspects of the brewing process and its results.

Any homebrewer, veteran or rookie, knows all too well that this beloved pastime comes with an often lofty monetary demand. Equipment upgrades typically arise after an inner debate that inevitably starts with, “do I really need this?” and ends with “now I can’t pay my rent… but I think this will help.” The best part about getting started with data gathering? It’s free. Gathering a solid foundation of data requires very little, if any, extra equipment beyond what a novice brewer already has. The drawback comes with diligence on the part of the homebrewer to manually gather data throughout the fermentation and/or brewing process.

There are three fermentation metrics that yield the greatest analytical insights: Gravity, temperature, and timeline.

Gravity

Using a standard hydrometer, take a gravity reading once a day during fermentation. Homebrewers may be hesitant to do this daily, fearing oxidation and wasted beer. Oxidation, while possible (and likely negligible), is dependent on your set-up and handling practices. As for wasted beer – a standard graduated cylinder requires 150 mL of beer in order to take a gravity reading. Given a seven day primary fermentation and daily samples, that amounts to 2.2 pints, or 5.5% of a standard 5-gallon batch. Keep in mind that taking samples affords you the opportunity to also observe subjective aspects of your beer and assess for off flavors and aromas.

Temperature

The temperature of the beer in the fermenter strongly influences rate of fermentation and the resulting byproducts produced by the yeast. A word of caution – do not assume the temperature of the beer in the fermenter is equal to the temperature of a beer sample taken for a gravity. Ensure that you are tracking the fermenter beer temperature and not the sample temperature when you record your data. As you translate the data into a chart, be sure to define the upper and lower temperature limits of the yeast strain. Visualizing the temperature values provides a sense of how effectively you are maintaining consistent temperature and ensures that you are staying within the recommended temperature range for primary fermentation.

Timeline

Possibly the most unique metric, timeline data defines the relationship between results and events. Simply put, record what you did and when you did it. Timeline data becomes relevant when making correlations between your fermentation schedule and results.

Using the Numbers – Basic Analysis Methods

The data trio of gravity, temperature, and timeline affords homebrewers a solid foundation to work with. While entire volumes could be written on the subject of brewing data analysis, there are two methods worth mentioning. Both methods follow a simplistic approach to improving your beer through analytics and can yield results early on.

Batch Comparison

Brewers compare beer all the time, regularly carrying out side-by-side taste tests and blind taste tests – looking, tasting, and assessing one final product against another. The end goal, especially when comparing batches of the same recipe, usually revolves around defining improvement. Is Batch B better than Batch A? While this conclusion comes easily, the follow-up question every brewer should ask is, “Why is Batch B better/worse than Batch A?” As a brewer, your analytical endgame is to achieve sustained improvement, and you can do this by comparing batch data. Start by creating overlapping fermentation curves, as this provides a visual representation of any inconsistencies among batches. While the shape of the curve is important and easy to contrast against others, pay special attention to your timeline data. Make correlations between when you did something and what happened – for example, say you cold crashed both Batch A and Batch B after 5 days in primary. What was the gravity of each when you crashed? Could this have affected the final results?

Trend Recognition

Remember, analytics is all about finding patterns and assigning meaning to them. Trend recognition is simply finding commonalities between results – this is particularly applicable to yeast performance. By comparing fermentation curves, brewers have the opportunity to identify patterns with a given yeast strain and how it reacts to various actions that you take throughout fermentation. For example, Batches A, B, and C were brewed with the same recipe and yeast strain. After analyzing the fermentation curves, you notice that all three batches had similar fermentation patterns, but each batch had a slightly lower final gravity than the previous one. While you might not immediately have an answer as to why this pattern occurs, you have identified a pattern nonetheless, and you can either take corrective actions on future batches to remedy negative outcomes or continue actions that tend towards positive outcomes.

Conclusion

When applied to homebrewing, analytics allows boundless opportunities for improvement by using data to uncover hidden patterns. Transforming fermentation data into graphics greatly enhances our ability to extrapolate meaning from data and gives the brewer an analytically sound foundation from which to make adjustments. Insights gained through analysis may not always manifest in the form of answers, but rather questions, that help to steer you in the right direction. Homebrewers of any level should embrace analytics as an affordable and credible way of achieving sustained improvement in brewing.

| About The Author |

Patrick Murphy is a homebrewer and founder of Arithmech Analytics, a software company dedicated to helping brewers of any level improve their beer through analytics. When he’s not developing software or writing about the brewing community, you can find him blasting punk rock and brewing beer. Patrick lives with his wife and two dogs in New Orleans and has been homebrewing since 2013.

Sources

1 – https://www.dictionary.com/browse/analytics?s=t

2 – http://www.t-sciences.com/news/humans-process-visual-data-better

If you have any thoughts related to this article, please do not hesitate to share them in the comments section below!

Support Brülosophy In Style!

All designs are available in various colors and sizes on Amazon!

Follow Brülosophy on:

If you enjoy this stuff and feel compelled to support Brulosophy.com, please check out the Support page for details on how you can very easily do so. Thanks!

Advertisements

Share this: Facebook

Twitter

Pinterest

Tumblr

Email



Like this: Like Loading...