The role of the data analyst
First we examine the role of data analysts: what they need to deliver their work every day, how they
deal with data sources and what their deliverables look like.
The work of analyzing data is not limited to data analysts. In most organizations, many users take data
at rest in one format, then prepare and present it with context that makes sense to a given audience.
Those users are analysts, even if “analyst” is not in their job title. They work in all areas, from the
loading dock to the C-suite.
MAKING THE CASE FOR REPORTING TOOLS
Data preparation has existed for decades. Spreadsheets and PCs kicked off the data analysis
revolution by giving ordinary users a set of basic tools. But analysts found that spreadsheet
programs fell short in handling deep data analysis, performing SQL extractions and working with
data sets that required manipulation like multiple JOINs.
Brio offered easy query capabilities against relational databases, but through
ODBC or a meta-connection against an ODBC source.
Along came a wave of tools like Brio for easy, ad hoc data analysis for business users. Brio offered
easy query capabilities against relational databases, but through Open Database Connectivity
(ODBC) or a meta-connection against an ODBC source.
Now, a data generation later, expectations have changed for the tools, the type of data going
through them and the speed at which users want insights.
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MAIN PAIN POINTS FOR DATA ANALYSTS
1. Data source proliferation — With data spread across silos, analysts
must collect data from spreadsheets, structured databases,
unstructured databases and everything in between.
2. Skill set gap — Writing SQL queries is not the same from one data
Data source
proliferation
Skill set gap
Tool
proliferation
source to another. That puts analysts in perpetual catch-up mode,
focused on new coding practices instead of on business insights.
3. Tool proliferation — The variety of data sources breeds a variety
Spreadsheet
sprawl
of tools to work with them. To keep up with the data, analysts must
keep up with native tools, custom APIs, business intelligence (BI)
platforms and analytics apps.
4. Spreadsheet sprawl — Pulling together data from disparate sources
means using some other application to integrate them. The most
Data delivery
engines and
roadmaps
Data lineage
and data quality
issues
common tool for that purpose is the spreadsheet, which does not
allow for traceability or repeatability.
5. Data delivery engines and roadmaps — Analysts who depend
Manual
processes
on IT to deliver data subject themselves to another external set of
schedules and priorities.
6. Data lineage and data quality issues — Where has the data come
from? How accurate is it? Is it consistent with data from other
sources? Here, analysts contend with traceability, transparency
and standardization.
7. Manual processes — With greater variety comes the need for more
massaging and manipulation to make the data useful. Working with
data manually slows reporting and hampers productivity.
That brings us to Brio.
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Figure 1: Main pain points for data analysts
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