Data and smart use of it have the potential to provide limitless commercial and social value. Yet many organisations - despite more than adequate resource and time invested - are set back by data quality issues, technical debt, and labour-intensive reports that give out irrelevant, contextless or conflicting numbers.
So how should a business engage with its data? The UCOVI methodology distills it into a sequence of five steps:
Understand the data you have, the data you don't have, and the data you need.
Collect the data you need in the most efficient way available.
Organise the data you have collected reliably, cleanly and securely.
Visualise the data you have organised impactfully to show the key trends and explore further.
Interpret the trends you discover in a data-savvy way.
UCOVI as the paradigm for the complete data professional
In data, you face two sources of confusion and complexity when building either your own career or a cohesive team under your leadership. First, a wide range of job descriptions used lazily and interchangeably, and second, a frankly bewildering choice of competing technologies, programming languages and frameworks that may or may not be what you should be learning.
The five elements of UCOVI help with both: they each act as discipline buckets into which technologies and specific skills can be grouped, while also simultaneously being the building blocks on which the various job roles in data - in their precise senses - are built on.
UCOVI is a clean and approachable point of reference to show exactly you where you fit into the ecosystem of data, and sign-post the learning path you should be taking to fulfill your career ambitions.
Understand: Skills | Experiences | Technologies | Programming Languages ◆ Exploratory Data Analysis ◆ Gathering Report Requirements ◆ Awareness of Relational Databases ◆ KPIs and Business Assumptions ◆ Technical Documentation ◆ Gap Analysis
Collect: Skills | Experiences | Technologies | Programming Languages ◆ APIs ◆ User Form & Survey Design ◆ Web Scraping ◆ JSON ◆ SQL: Awareness of appropriate column data-types ◆ GDPR & Compliance Awareness ◆ Stakeholder Management and relationship building with IT and data owners
Interpret: Skills | Experiences | Technologies | Programming Languages ◆ Data Translation - communicating insights to stakeholders ◆ Statistics ◆ Business decision or further analysis work? ◆ Correlation vs Causation ◆ Trend vs Seasonality ◆ Margin of Error ◆ Storytelling