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There is a growing amount of data generated in all areas of life, and a data scientist's job is to extract meaning from that raw data. But not all businesses have a data scientist on their team, let alone the ability to be data-driven.

The good news is you don't need to be a data scientist to think like one. Data science is more of a mindset than a technical aptitude. While both are important, what separates good data scientists from great ones is a way of approaching data. And when you combine that mindset with the right tools, you level up your decision-making skills.

If you’re new to data science, consider the following five easy ways you can start thinking like a data scientist.

1. Clean your data

Data scientists know the value of having clean data. Cleaning data, or wrangling data, is the process of finding errors in your dataset and fixing them in preparation of analysis. This includes:

  • Typos

  • Mislabeled items

  • Incomplete information

  • Inaccuracies

  • Irrelevancies

Like a good data scientist, it’s important to be vigilant with your data. By putting the time and effort into ensuring your data quality is reliable, you will garner the best results. Your output is only as good as the data that's going into it.


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2. Think critically

Ever hear of the spurious case of divorce and margarine? Weird correlations exist and good data scientists will be able to recognize the difference between correlation and causation. Are most of your customers really from the 90210 area code? A good data scientist would be able to get these answers by thinking critically and ignoring these types of faulty data.

When analyzing data, the goal is to turn information into insights. To create insights about the right things, data scientists need to be able to ask the right questions. This is where critical thinking comes in. Critical thinking is a process of questioning information to make better decisions and understand things better. When we think critically, we are consciously using our intellectual tools to reach a well-founded conclusion than our brain naturally would.

When analyzing data, keep the following questions in mind when trying to solve a problem:

  • What do you already know?

  • How do you know that?

  • What seems to be the reason this is happening?

  • Are there other variables that account for this outcome?

  • What was the size of the sample used in this dataset?

  • Is this a correlation or a causation?

Keep coming back to thee questions when you're trying to solve your problem. They urge you to get to the root of the issue.


3. Avoid analysis paralysis

It’s easy to get overwhelmed with data and then do nothing with it. You need to start somewhere, right?

To prevent inaction, start with the low-hanging fruit. Decide on at least three insights you want to capture from your data and use and enterprise analytics solution, like SAP Analytics Cloud, to create a story.

A good mindset to have is to be curious. Data scientists love to follow hunches, discover insights, and answer questions. Once you have your story visualized, it’ll be easier to take the appropriate action.

As a business owner, here are five common visual ways your data can come to life with SAP Analytics Cloud:

Profit/Loss — Is your business profitable this month? If so, how much? And how do profits compare with previous months?

Sales revenue — Where are yours sales coming from? Who is your largest customer segment? What are the key influencers driving sales? Start with a chart that combines your sales revenue as a measure and time as a dimension. You can also establish parent-child hierarchies to drilldown deeper in your data.

Expenditures — What are your top five expenses this quarter? How do they relate to previous quarters? Are there ways to cutback on certain costs to maximize profits? Create a chart that shows months as a time dimension and expenditures as a measure.

Customer acquisition — How much does it cost to acquire a customer? Create a chart that shows your total marketing spend over time and the total number of customers acquired over that same period. You can then add a calculation as a new measure showing your customer acquisition cost (i.e. total marketing spend / the number of customers acquired in that period).

Churn — Is your customer base growing or shrinking? And by how much? To calculate churn rate, take the number of customers at the beginning of a given time period, subtract by the number of customers at the end of that period, then divide by the total number of customers from the beginning of that period.

4. Blend datasets

We get data from multiple sources, every hour of the day. In an enterprise setting, that data is always growing. Data blending can speed up the consumption of that data without involving data scientists or other specialists.

Data blending is combining multiple data sources to create a single, new dataset, which can be presented visually in a dashboard, like the ones above, and can then be processed or analyzed. By blending datasets from different sources, you can establish a broader understanding of your business and find valuable insights.

Start by collecting all your different data sources across your business into a platform, like SAP Analytics Cloud. When all of your data is gathered, it is then accessible for everyone who needs to look at it.

From there, you can create a series of charts with the KPIs for each business function. By seeing your organization as a whole, you may discover insights that may have gone unnoticed before.


5. Design your dashboard

Data science is more than just analysis, it’s about presenting your findings in clear and compelling stories. After all, no one wants to see a slew up numbers and stats; they want to see clear takeaways.

The goal is to use data visualization to present insights that are digestible and help explain trends and stats more easily. Think:

  • Line chart

  • Bar chart

  • Maps

  • Graphs

  • Tables

When designing your dashboard, choose the best charts and graphs that will tell your story. Visual communication is a great way to convey complex information to an audience, and when people can interpret your visualization by asking more questions instead of how or what is displayed, then you know you've successfully presented your findings.

To think and act like a data scientists requires the right tools, and there are a number of tools that help you take data science to the next level. But there's only one that provides you with everything all in one. SAP Analytics Cloud makes it easy to visualize your data and uncover actionable insights.