What Does it Take to Convert Data into Knowledge?

January 15, 2022

Today’s buzzword is that data is the new oil. That may well be true; without data, businesses are adrift in a sea of rapidly changing markets, customer expectations, and economic conditions, with no way of powering their course through it all.

But data alone isn’t enough. There are all too many organizations that are drowning in data, with more appearing every minute, but aren’t succeeding in making any use of it. Some brands are flooded by customer behavior data, but can’t deliver excellent customer experience; overwhelmed by supply chain data, but are plagued by unexpected inventory shortages; bombarded by social media data, but unable to predict the next big trend before they’re staring at its headlights.

The million-dollar question is why, at a time when data is at an all-time high, so many companies are still feeling their way. And the answer is usually because they misunderstand the difference between data and knowledge.

Data science is such a sexy term that it’s easy for an enterprise to race ahead to detailed questions like choosing between Athena vs. Redshift for their data infrastructure, before checking they understand the differences between data and knowledge.

What is data vs knowledge?

Data is essentially a collection of facts. It could be that someone bought a pair of red socks; a car pulled into a parking lot; or the vibrations in a rotating drum increased by 3%. Each fact alone is a data point, but it’s also meaningless.

Data can be structured, like a list of train arrival times, or unstructured, like the review someone leaves about their new socks, but either way, it’s what we call raw data. Rather like crude oil, it’s not useful until you’ve refined it. No one is going to change anything about their business decisions, marketing campaigns, or pricing choices based on isolated data.

Once data is gathered, processed, and analyzed, then it is converted into knowledge, or insights, and it gains value and meaning. Insights guide how often you order red socks, how much you should charge per hour for parking, and how often you run maintenance on your manufacturing equipment, for example.

Data needs to be collected

Although raw data isn’t useful on its own, there’s no other way to get meaningful insights. You need to collect it in a single location, and plenty of it too. It’s never enough to just have a few pieces of information; it takes several to reveal patterns and enable you to draw conclusions.

For example, the fact that vibrations increased in a rotating drum doesn’t tell you very much,but if they increase by another 5% the next, and another 5% the day after that, you’ll know that something isn’t working the way it should be. The more data you gather, the more patterns you’ll be able to spot, and the clearer those patterns will be.

This in turn requires powerful, flexible storage like a data lake, which can hold plenty of data, link all your data sources together, scale up quickly to support more sources and analytics engines, and is easy to refresh with new information.

The richer the data, the better

Big data repositories are good, but reliable, trustworthy data that crosses many fields is even better. You need to know not just that a lot of people bought socks, but what payment method they used, whether it was online or instore, which items they bought at the same time, the demographics of your customers, what the weather was like, etc.

With this kind of information, you can generate a multifaceted picture of your audience’s preferences, the resources you have available, market patterns, seasonal changes, and more. You can identify which customers are more likely to be interested in a discount offer for socks, who would buy more if they could use Apple iPay, and what weather conditions mean you should double your order.

When you use cloud-based data storage, like a cloud data warehouse, all your data sources can come together to make a richer data tapestry that you can access from anywhere, at any time.

Crunching data into insights

Even when you’ve turned data into knowledge, it needs to be applied. Seeing the patterns in your data is great, but using them to determine how much parking space you need at any given time, how many socks to order, or how often to inspect your equipment is truly getting value from it.

When data becomes actionable insights, it supports better business decision making. Rich data insights enable smarter market segmentation, better customer predictions, advanced personalization, more relevant allocation of resources, more accurate and relevant marketing, and more.

To ease the process of business decision making across all your departments, you want stakeholders to use versatile visualizations and customizable dashboards that let them try out different ways to view and organize the data so they can spot more meaningful insights more quickly.

Data is good, but insights are better

Data is more than just a cool business buzzword; it’s the real fuel that powers sales and profits. When you understand the process that converts raw data into meaningful insights, you’ll be able to gather the data you need, cut the time to insights, and ensure that every department can tap into the guidance they need to make better business decisions.

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