Data analytics strategies are becoming more accessible to businesses of every size and industry.
According to a recent study, 59% of organizations globally use data analytics. It makes sense: done well, data analytics amplify a business’ aptitude for defining and achieving strategic objectives. Manufacturers have been leveraging big data to help solve problems in the supply chain, for cost reduction, quality control and more. As with any toolkit, it can be implemented well or poorly. Here, we’ll touch on the great potential for those just launching their digitization journey while including some common traps to watch out for.
Benefit No. 1: It’s Easier Than You Think
It’s no longer necessary to know how to code to work with big data, thanks to a recent explosion of analytics products that aim to be as user-friendly as possible. Orange, for example, is a free open-source software package created by a European university with an interface based on toolbars and widgets instead of lines of code. Increasingly, platforms like Orange are readily available online, oriented toward an intuitive user experience and offering the tools to analyze data for free without prior experience. For businesses that take advantage, the costs and expertise needed to make data analysis practical have gone down considerably.
Benefit No. 2: There’s More Data Available Than Ever Before
From 2010 to 2020, the amount of data created in the world increased by almost 5,000%. This growth trend is showing up in manufacturing, as well. It is now possible to get more data on user requirements, supply chain logistics and quality control. Additionally, with the boom in sensor technology, data is more detailed and in real-time. Paired with the availability of easier-to-use data analytics tools, capturing and translating volumes of information can be practically applied toward business improvement.
Benefit No. 3: Data Analysis Can Find Patterns That Would Otherwise Go Unnoticed
If a supplier misses an important shipment, it’s a significant event for everyone involved. If, however, a supplier provides parts that are slightly more likely to be defective over time, it may go unnoticed. With the power of big data, subtle trends and nuance gathered over significant periods can be flagged and monitored where they might be missed by individuals. Longevity, objectivity and a great tolerance for repetitive tasks provide opportunities to reduce errors with greater precision.
Pitfall No. 1: Garbage In, Garbage Out
A universal truth of data analysis is that the quality of results is driven by the quality of the data. Just because you have a lot of information available does not mean that it is accurate. Without diligence in data gathering to ensure accuracy, there is little reason to put confidence in the results.
Once the data is accurate, it must be formatted so that it can be analyzed. Cleaning data can involve investigating outliers, correcting measurement errors, or simply fixing typos. Harvard Business Review found that data scientists can spend up to 80% of their time cleaning data.
Pitfall No. 2: Correlation Does Not Equal Causation
In manufacturing and throughout the supply chain, there are many relationships to be found. Some of these are expected. Pressure will correlate with temperature; volume and on-time delivery might fluctuate depending on the time of year; profit will correlate with sales volume. It’s important to remember that just because one variable correlated with another does not mean that one variable caused the other or that the relationship is relevant. Did you know that there is a very strong correlation between the per capita consumption of cheese and the total revenue generated by golf courses? It’s possible to see many relationships between variables, whether they are meaningful or not.
Pitfall No. 3: Big Data Isn’t A Substitute for Statistical Rigor
Data analytics can offer unique insights into complex systems. However, it’s important to keep in mind that the results need to be interpreted reasonably. The numbers and patterns that come out of data analytics tools come from statistical formulas. Foundational rules still apply. If there was a lot of data available from Warehouse A, and data analytics revealed some things about Warehouse A, those findings would not necessarily apply to Warehouse B.
Data analytics are more entrenched in the world every day. More businesses, large and small, have access to substantial quantities of information and, increasingly, the tools to interpret it. Used correctly, it can take a business to the cutting edge of its industry. While hurdles remain, the decision is no longer one of “if,” but rather, “Why not today?”
Ian Watson-Hemphill is a contributing analyst with Firefly Consulting.