Collecting, Transforming, and Analysing Data

Data Analytics

”Name”

Appcues

Your all-in-one platform for user engagement. Struggling to reach users and keep them around? Losing them before they even get started? You’re not alone. SaaS has a retention problem, and it starts on day one.
”Condens”

Condens

Create a single source of truth for research, making data accessible and shareable across teams. Accelerate your analysis and scale research maturity — minus the manual effort.
”

Glassbox

Optimize customer experiences, boost conversions and maintain top-tier security and compliance—all tailored for regulated industries.
”UXPressia”

UXPressia

Collaborate on customer experience insights. Keep all your customer journey maps and personas in one place. Unlock opportunities for business growth.
”

UXtweak

Recruit, conduct, analyse, and share UX research all-in-one place
Definition

Data Analytics

Data analytics is the process of examining raw data to uncover patterns, draw conclusions, and make informed decisions. It involves collecting, transforming, and analyzing data to gain insights that can be used to drive business intelligence, strategy, and decision-making.

What it is:

Data Collection and Preparation:
Gathering data from various sources and preparing it for analysis (cleaning, transforming, and organising).

Analysis and Interpretation:
Applying various techniques like statistical analysis and machine learning to identify patterns, trends, and relationships in the data. 

Insight Generation and Communication:
Interpreting the results of the analysis and communicating them in a clear and concise manner, often using data visualisations.

Why it's important:

Data-Driven Decision Making:
Provides insights to help organisations make informed decisions based on evidence rather than intuition.

Improved Business Performance:
Can lead to better marketing campaigns, more efficient operations, and increased customer satisfaction.

Competitive Advantage:
Helps organisations gain a competitive edge by understanding customer behaviour, market trends, and emerging opportunities. 

Types of Data Analytics:
  • Descriptive Analytics: Focuses on understanding what has happened in the past.
  • Diagnostic Analytics: Explores why something happened.
  • Predictive Analytics: Uses data to forecast what might happen in the future.
  • Prescriptive Analytics: Suggests what actions should be taken based on predictive insights. 
Stages of a user journey
  • Statistical Analysis: Using statistical methods to analyse data and identify trends. 
  • Machine Learning: Applying algorithms to learn from data and make predictions. 
  • Data Visualisation: Using tools like dashboards and charts to communicate insights visually. 
  • Programming Languages: Utilizing languages like Python and R for data manipulation and analysis. 
  • Software: Using platforms like Tableau, Power BI, and Qlik Sense for data visualization and analysis. 

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