Data Ecosystem

Now, it’s time to explore the data ecosystem, find out where data analytics fits into that system, and go over some common misconceptions you might run into in the field of data analytics. To put it simply, an ecosystem is a group of elements that interact with one another. Ecosystems can be large, like the jungle in a tropical rainforest or the Australian outback. Or, tiny, like tadpoles in a puddle, or bacteria on your skin. And just like the kangaroos and koala bears in the Australian outback, data lives inside its own ecosystem too.

Data ecosystems are made up of various elements that interact with one another in order to produce, manage, store, organize, analyze, and share data.

These elements include hardware and software tools, and the people who use them. People like you. Data can also be found in something called the cloud. The cloud is a place to keep data online, rather than on a computer hard drive. So instead of storing data somewhere inside your organization’s network, that data is accessed over the internet. So the cloud is just a term we use to describe the virtual location. The cloud plays a big part in the data ecosystem, and as a data analyst, it’s your job to harness the power of that data ecosystem, find the right information, and provide the team with analysis that helps them make smart decisions. For example, you could tap into your retail store’s database, which is an ecosystem filled with customer names, addresses, previous purchases, and customer reviews.

As a data analyst, you could use this information to predict what these customers will buy in the future, and make sure the store has the products and stock when they’re needed. As another example, let’s think about a data ecosystem used by a human resources department. This ecosystem would include information like postings from job websites, stats on the current labor market, employment rates, and social media data on prospective employees. A data analyst could use this information to help their team recruit new workers and improve employee engagement and retention rates. But data ecosystems aren’t just for stores and offices. They work on farms, too. Agricultural companies regularly use data ecosystems that include information including geological patterns in weather movements. Data analysts can use this data to help farmers predict crop yields. Some data analysts are even using data ecosystems to save real environmental ecosystems. At the Scripps Institution of Oceanography, coral reefs all over the world are monitored digitally, so they can see how organisms change over time, track their growth, and measure any increases or declines in individual colonies. The possibilities are endless.

Okay, now let’s talk about some common misconceptions you might come across. First is the difference between data scientists and data analysts. It’s easy to confuse the two, but what they do is actually very different. Data science is defined as creating new ways of modeling and understanding the unknown by using raw data. Here’s a good way to think about it. Data scientists create new questions using data, while analysts find answers to existing questions by creating insights from data sources. There are also many words and phrases you’ll hear throughout this course, that are easy to get mixed up. For example, data analysis and data analytics sound the same, but they’re actually very different things.

Let’s start with analysis. You’ve already learned that data analysis is the collection, transformation, and organization of data in order to draw conclusions, make predictions, and drive informed decision-making. Data analytics in the simplest terms is the science of data. It’s a very broad concept that encompasses everything from the job of managing and using data to the tools and methods that data workers use each and every day. So when you think about data, data analysis and the data ecosystem, it’s important to understand that all of these things fit under the data analytics umbrella. All right, now that you know a little more about the data ecosystem and the differences between data analysis and data analytics, you’re ready to explore how data is used to make effective decisions. You’ll get to see data-driven decision-making, in action.

Data and gut instinct

Detectives and data analysts have a lot in common. Both depend on facts and clues to make decisions. Both collect and look at the evidence. Both talk to people who know part of the story. And both might even follow some footprints to see where they lead. Because whether you’re a detective or a data analyst, your job is all about following steps to collect and understand facts.

Analysts use data-driven decision-making and follow a step-by-step process. You have learned that there are six steps to this process:

1. Ask questions and define the problem.
2. Prepare data by collecting and storing the information.
3. Process data by cleaning and checking the information.
4. Analyze data to find patterns, relationships, and trends.
5. Share data with your audience.
6. Act on the data and use the analysis results.

Analyzing facts is a key part of data-driven decision making because facts lead to patterns that help guide the decisions we make — big and small. Data-driven decision-making is rooted in using facts to guide business strategy. As an analyst, you will be tasked with creating a verified story about the data and sharing it with stakeholders. These stakeholders use your story to make choices based on facts, and make sure that the company is focused on the right goals.

Gut instinct can be a problem

There are other factors influencing the decision making process, too, though. You may have read mysteries where the detective used their gut instinct, and followed a hunch that helped them solve the case. Gut instinct is an intuitive understanding of something with little or no explanation. This isn’t always something conscious; we often pick up on signals without even realizing. You just have a “feeling” it’s right.

But for data analysts, just trusting our gut instinct can be a problem. At the heart of data-driven decision making is data, so we always want to focus on the data to ensure that we’re making informed decisions. When we make decisions based on our gut instinct without any data to back it up, it can lead to mistakes. Or worse, when we ignore the data based on our own personal experiences, we can create bias in our analysis. Businesses that rely on gut instinct to make decisions often make bad choices because they aren’t considering the story their data is actually telling.

Instead of relying on gut instinct, you can build your business knowledge and experience over time. The more you know about how a business works, the easier it will be to figure out what that business needs. And that business knowledge and experience can also help you identify errors and gaps in your data and communicate your findings. For example, a detective might be able to crack open a case because they remember an old case just like the one they’re solving today. Their past experience could help them make a connection that no one else would notice. Maybe their unique background knowledge helps them discover someone is lying, or it could help them uncover new clues. Your business knowledge and experience may help you understand problems intuitively. But, unlike gut instinct, it will give you more than just a feeling to go on.

Data + business knowledge = mystery solved

Blending facts and data with your business knowledge will be a common part of your process. The key is figuring out the exact mix of data and business knowledge for each particular project. A lot of times it will depend on the goals of your analysis. That is why analysts often ask, “How do I define success for this project?”

Successful analysis needs to be accurate, and fast enough to help decision-makers. So try asking yourself these questions about a project:

• What kind of results are needed?
• Who will be informed?
• Am I answering the question being asked?
• How quickly does a decision need to be made?

For example, if you are working on a rush project, you might need to rely on your own knowledge and experience more than usual. There just isn’t enough time to thoroughly analyze all of the available data. But if you get a project that involves plenty of time and resources, then the best strategy would be to be more data-driven. It’s up to you, the data analyst, to think about the situation and make the best possible choice. You will probably blend facts and knowledge a million different ways over the course of your data analytics career. And the more you practice, the better you will get at finding that perfect blend.