Data can be found in every level of many organisations. It’s constantly being created, aggregated, collected, and analysed. To put things into perspective, the current output of data is at roughly 2.5 quintillion bytes a day. With all that data, it can be difficult to determine which bits really matter.
Focusing on the right data can help organisations evolve, transform, and be agile enough to keep in step with the demands of the times. It helps inform decision-making quickly, making it more effective and accurate. The concept of Big Data is about bringing together large data sets from different sources which can be analysed to uncover patterns, trends, and insights for improving systems, services, and organisations.
As its name implies, Big Data is enormous. However, you need not include every tiny piece in your strategy for it to be useful to your organisation.
You Don’t Need all that Data to Find Answers
The concept of knowing which data you need is akin to walking into a drugstore and trying to find the proper medication for a cold. There are a lot of options, and each one works in a certain way. However, you don’t need all that medicine, you just need the right one to free you from a runny nose, an itchy throat, and sneezing.
The same thing can be said about data. One persistent Big Data myth is that you need all the data you can get to get results. This is not true—not every piece of data you can access is going to be relevant to your objectives. For example, if your objective is to understand your target market’s online purchasing habits then data related to their online activity is relevant. However, data related to their offline activities may not be needed in this scenario.
Looking at the Right Data: How Wayfair Verified their Customer Experience Features Worked
The amount of data available for e-commerce companies is overwhelming. Data may also be hidden or in data silos, making the challenge of finding meaningful data even greater. Home decor and e-commerce giant Wayfair had all these issues to overcome. They believed, however, in the power of Big Data to provide their customers with a better way to shop.
Wayfair began to focus more on improving customer experience. Earlier this year, the company launched their “Search with Photo” feature which allowed shoppers to take a photo of an item that they see in a store or even in a friend’s house then find visually similar products on the Wayfair site. They learned that the data they must analyse to see if this feature was successful came from loyal repeat customers. Whether they had an exact match to the photo wasn’t really relevant. What mattered is what actions the buyer took after the search: looking at lots of other items and, of course, if they made a purchase. Overall, they found an upward trend in the number of return buyers compared to last year. This let them know that they were making the right decisions when it came to improving customer experience.
This is just one example of the many nuances of finding the right nuggets to gain a data-driven edge over the competition.
Knowing What You Need (and What You Don’t)
To determine which data is relevant and important to your business objectives, you need to ask some questions first.
For example, if you want to gain more production efficiency and reduce waste, asking questions related to the sources of waste in your business model is a good start. If you’re in an industry where waste material is a significant issue, there’s opportunity to be had in collecting data related to waste. For example, up to 40-percent of restaurant food in Australia is wasted. If restaurants thoroughly studied the data and trends around wasted food, they’ll be more informed and prepared in dealing and reducing it. By effectively analysing relevant data they could come up with better inventory patterns or to remove certain menu items that are contributing more to waste than to actual profits. The end result could be a more optimised production process and even increased earnings.
In certain industries, asking which tasks and decisions could be automated is a step in the right direction. Humans are naturally good at making decisions. However, we can be clouded by experiences and bias. As such, machines and digital tools actually have a higher potential than us in this department. This is especially true around building pricing strategies. Gathering data around historical pricing, competitive pricing, and demand for a product can work to basically automate pricing models and decisions for certain offerings. Algorithms are powerful tools for translating data patterns for better business. Most analysts believe this is how e-commerce and retail giants such as Amazon and Wayfair work, and it appears to be working quite well for them, as referenced above.
As these two scenarios show, asking the right questions in relation to your business goals will help you determine and categorise which data sets are to be used, and which ones you should give less importance to. That way, you’ll be able to focus more on collecting and analysing the data you need, saving you time and effort since you don’t have to waste energy gathering and using data which is not relevant as of the moment.
Being able to ascertain the right data then analysing it properly will lead to actionable insights that can improve operations, increase business efficiency, and even disrupt markets. This is exactly why you can’t just assume that all data is important and must be used.
Insights are Key
While the logic behind knowing what data you need seems simple enough, the analysis and interpretation can be tricky tasks to handle, especially without guidance and expertise. Here’s where Latize’s data management platform Ulysses comes into play. By harmonising internal and external data sets, Ulysses is able to form an intelligent network of data about various things—from people and patterns to organisations and events—which will yield insights you can then apply to enhance various business processes and solve problems, resulting in positives such as better decision-making and gaining a competitive advantage.