Improving data analysis methods to generate sharper and better insights

Even small changes in data analysis methods make a huge difference for businesses, the basics are key - here are reasons why!

Small changes in data analysis methods make a huge difference for businesses. I learnt this after a conversation with one of my clients. He was the IT manager of a large firm in Fintech and was praising my team for the work they had done.

“You guys have made our job much easier,” he explained. “The value we have extracted from our data will double our revenue in the long run. Your services played a huge part in that.”
Ever since that conversation, I have worked hard to make sure our data analysis methods are better, sharper and more efficient than before so that other clients would yield similar benefits. In the process, I have learnt much about data analysis and how to deliver high-quality insights to clients.

A huge part of improving data analysis methods is making sure that processes, like data preparation, are well optimised for generating timely insights.

Going beyond curiosity – Why improving analysis is important

As SAS experts, we are driven by curiosity to discover the best analysis methods to tackle a particular problem. You only need to look at the wonderful articles shared on the SAS blog to discover just how much passion and initiative go into data analysis.

But improving data analysis methods should go beyond curiosity because it is something that makes a genuine difference in your client’s bottom line. Here’s how.

Better data quality

When we improve our analytical methods, it improves data quality by a significant margin. During data analysis, we address the problematic elements that compromise data quality, like replacing missing values, handling different data quantities and deleting duplicate data. High-quality data leads to better and more accurate insights.

After all, organisations could be using the most sophisticated analytics platform in the world, but if they are using poor quality data, then the results will be poor.

The process becomes easier

When data analysis methods are sharp and refined, it makes the client’s work much easier. Data analysis comes with several advantages, like increased risk awareness, the ability to make decisions faster, better insight into performance and deeper understanding of customer requirements.

By contrast, when data analysis is unrefined, it clogs up the entire process, leading to frustrating delays and poor insight from data. We should always look for ways to improve data analysis methods because as the volume of data grows, the methods that used to work will not be as effective as before.

So, it’s important to always improve and refine data analysis methods.

Improving data analysis methods

There are several technologies, like NLP and data mining, that can break down and analyse data. But are they the key to improving data analysis methods? No. While NLP and data mining are vital for analysis, it takes more than just the right technology to refine data analysis methods.

Instead, the key to improving data analysis is to optimise the processes surrounding it, like data preparation.

Refining data preparation

It is important to develop a method to make data preparation a more refined and timely process. With most big data being unstructured data, refining the processes for collecting, cleaning and preparing it is crucial for improving data analysis.

There are many methods used in data preparation that can optimise this process, like data lakes. However, it’s important to recognise that no method is fool-proof and that each one comes with its disadvantages.

Automation is crucial for shortening data preparation, like integrating new data. By refining data preparation, organisations also improve data analysis because it takes less time to get the insights that your clients need.

Identify repeatable areas

One of the main issues we encounter in data preparation and analysis is one-off processes. On the surface, it seems like a necessity because every case is unique. However, I have come to discover that most processes in data preparation and analysis can be easily duplicated across multiple projects.

Why not make the process easier?

The key to improving data analysis methods lies in optimising the processes surrounding the analysis stage. When the processes are as timely and efficient as possible, clients gain the insights they need faster.
Ultimately, it is important to seek out technologies that can make preparation and analysis easier because that ends up being the key to refining data analysis methods.


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