Getting Started
3 min read
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This short guide for conducting successful big data analysis will take you through a couple of the most essential tips to help you learn how to analyse data & become a better data analyst by avoiding rookie mistakes and embracing the correct mindset to get the most from your data.
We’ll also cover some of ways that conducting effective data analysis can help your organisation.
Contents
Identify All Relevant Data Sources
Our first technical specialist Lior Shamir, Associate Professor of Computer Science at Kansas State University stresses the importance of knowing your data and it’s sources in his response;
“Unlike what many people happen to believe, the most important thing for successful data science is not the algorithms or tools, but the data.” “Many new data scientists often forget that, and rush to apply all possible analytics tools they know on the data that they have, or can easily access.”
“For successful data science, the data scientist needs to identify all relevant sources of data, and use them.”
“They might come in different forms, different formats, and very often not all of them are even digital.”
“Often the data scientist does not even know these databases or data sources exist. That requires a detective work inside the organization, interviewing people, and conducting an investigation until the relevant data sources are revealed.”
“Eventually, data analytics is the easiest part of the job of a data scientist. Identifying the right data and defining the data science problems takes far more effort and knowledge than just the analytics.”
Beyond Predictive Analytics
Lior also cited in his comments that despite the focus on predictive analytics being common across many tools, there's a lot more than initially meets the eye to conducting effective analysis;
“As for the analytics, today there is a lot of focus on predictive analytics, and that is due to the increasing popularity of machine learning in data science.”
“But data science is obviously far broader than merely predictive analytics.”
“Descriptive analytics and prescriptive analytics are just as important, often more, and tools such as correlations and dimensionality reduction are essential.”
“Tools such as feature selection are important for identifying links in the data.”
“Traditionally feature selection was used to improve the classification accuracy, but in the data science era they are used for profiling and detecting links in the data that can lead to discoveries.”
“Also with supervised machine learning, which is very common today in data science. The accuracy of a prediction is just one piece of the puzzle.”
“The ability to profile the relationships between the different classes in the data is often just as important, but that type of analysis can be different for each problem, so they require specific work, often with tools such as phylogenies or multidimensional scaling.”
Benefits Of Data Analysis
Our next specialist, Josh Adler, CEO at ConvrtX takes us through some of the actionable benefits that he saw from data analysis when their technical team used the ELK Stack for this task;
“ELK is one of the tools that our team uses; it is an open-source set of tools that helps to monitor the noisy environment—preferred by the IT organization because it is more likely to avoid vendor lock-in.”
Also, ELK provides a huge platform which gathers and processes multiple data sources and stores them in a centralized data store.”
As a result of using ELK they saw the following benefits;
1. Efficient Decision Making
“Data analysis provides insight and also a guide in helping us make decisions based on gathered data.”
2. Enhanced Communication
“By obtaining relevant ideas, which are key to assisting in how communication functions between employees and encourages the openness of information across the business.
3. Improved Productivity
“With verified collected data and visualisations, employees are more confident and have a guide to know what needs to be achieved—thus making a promising result relevant to the overall business goals”
Mediaworks, Head of Insights, Rachel McGuigan also contributed two of her insights on the benefits of analysing data;
4. Identifying Gaps In The Market
“Data can give you insights into every aspect of your business if you understand how to pull and visualise it in a usable way.”
“By visualising the data, you can spot trends and gaps in your market that weren’t sufficiently met in your marketing, sales or advertising campaigns.”
“This also helps you to gain a better understanding of who your customers are, when they shop and potentially why they shop”
5. React & Prepare For The Next Big Consumer Trend
“Data analysis enables you to be reactive as well as predict consumer trends – by using data analysis tools you can understand when consumers are likely to land on your website and predict your business’s needs and react to real-world events.”
Travis Fell, Product Manager at Hypori; added two valuable benefits of embedding data analytics within technology platforms in his response;
6. Increased Competitiveness
“Embedding analytics in customer-facing products influences customer behavior and moves them to the next stage of the customer journey.”
7. Improved Revenue:
“Embedded analytics can help you win more sales, retain customers and expand product offerings through increased win rate, decreased churn, greater product licensing and more monetization opportunities.”
If you need to automate your data analysis collection with an automated log management system then Logit.io provides this service as well as hosted ELK & log analysis for teams to improve how they work, compete and develop new features.