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One of the major challenges companies deal with when working with data is implementing a coherent data strategy. We all know that the problem is not with a lack of data, we know that we have a lot of that. The problem is how we take the data and transform it into actionable insights. 

However, sometimes there is too much data available, which makes it harder to make a clear decision. Funny how too much data has become a problem, right? This is why companies must understand how to approach a new data science problem. 

Let’s dive into how to do it. 

Crafting the Perfect Problem Statement

Before we get into the nitty-gritty, the first thing we must do is define the problem. You want to accurately define the problem that is being solved. This can be done by ensuring that the problem is clear, concise and measurable within your organization's limitations. 

You don’t want to be too vague because it opens the door to additional problems, but you also don’t want to overcomplicate it. Both make it difficult for data scientists to translate into machine code. 

Here are some tips:

• The problem is ACTUALLY a problem that needs to be further analyzed
• The solution to the problem has a high chance of having a positive impact 
• There is enough available data
• Stakeholders are engaged in applying data science to solve the problem

Choosing Your Direction

Now you need to decide on your approach, am I going this way or am I going that way? This can only be answered if you have a full understanding of your problem and you have defined it to the T. 

There are a range of algorithms that can be used for different cases, for example:

• Classification Algorithms: Useful for categorizing data into predefined classes.
• Regression Algorithms: Ideal for predicting numerical outcomes, such as sales forecasts.
• Clustering Algorithms: Great for segmenting data into groups based on similarities, like customer segmentation.
• Dimensionality Reduction: Helps in simplifying complex data structures.
• Reinforcement Learning: Ideal for scenarios where decisions lead to subsequent results, like game-playing or stock trading.

The Quest for Data Quality

As you can imagine, for a data science project you need data. With your problem clearly defined and you have chosen a suitable approach based on it, you need to go and collect the data to back it up. 

Data sourcing is important as you need to ensure that you gather data from relevant sources and all the data that you collect needs to be organized in a log with further information such as collection dates, source name, and other useful metadata. 

Keep something in mind. Just because you have collected the data, does not mean it is ready for analysis. As a data scientist, you will spend some time cleaning the data and getting it in analysis-ready format. 

Delving into Analytical Depths

So you’ve collected your data, you’ve cleaned it up so it’s looking sparkly clean, and we’re now ready to move on to analyzing the data. 

Your first phase when analyzing your data is exploratory data analysis. In this phase, you want to understand the nature of the data and be able to pick up and identify the different patterns, correlations and possible outliers. In this phase, you want to know your data inside and out so you don’t come across any shocking surprises later on. 

Once you have done this, a simple approach to your second phase of analyzing the data is to start with trying all the basic machine learning approaches as you will have to deal with fewer parameters. You can also use a variety of open-source data science libraries to analyze your data, such as scikit learn. 

Deciphering the Data Story

The crux of the entire process lies in interpretation. At this phase, you will start to see the light at the end of the tunnel and feel closer to the solution to your problem. 

You may see that your model is working perfectly fine, but the results do not reflect your problem at hand. A solution to this is to add more data and try again until you are satisfied that the results match your problem. 

Iterative refinement is a big part of data science and it helps ensure data scientists do not give up and start from scratch again, but continue to improve what they already have built. 

Conclusion

We are living in a data-saturated landscape, where companies are drawing in data. Data is being used to attain a competitive edge, and are continuing to innovate based on the data decision-making process. 

Going down the data science route when refining and improving your organisation is not a walk in the park, however, organisations are seeing the benefits of the investment.
 
 

Nisha Arya is a data scientist, freelance technical writer, and an editor and community manager for KDnuggets. She is particularly interested in providing data science career advice or tutorials and theory-based knowledge around data science. Nisha covers a wide range of topics and wishes to explore the different ways artificial intelligence can benefit the longevity of human life. A keen learner, Nisha seeks to broaden her tech knowledge and writing skills, while helping guide others.