A key component of the transition to becoming a data driven organization is the
acceptance of a constant state of change, with a rigorous measurement compo-
nent. These constant changes involve the automation of formerly manual tasks,
requiring human approval, to a state where machines and algorithms make deci-
sions and execute them on their own. This constant state of change is managed
through common best practices (Figure 1) that assist an organization in ensuring
high quality decisions are made. These best practices are driven by data acces-
sible to a wide portion of the organization to greater facilitate collaboration.
Maturity Level People Skills Process Methods Example Technologies
Level 1 - Data Basic IT/Computer By memory Spreadsheets
Access skills Level 2 - ETL, DBS By experience RDBMS
Level Data Quality Documented & Enterprise Data Warehouse
3 - Reporting Statistics reproducible (Redshift, BigQuery, SQL Data
Consolidation
Development
Level 4 - Alerting
Advanced
Warehouse)
Automated
Statistics
Level 5 - Engaging
NLP, Predictive,
Big Data Platforms (EMR, Data-
pric, HDInsights, Sprak)
Learning & Evolving
Modeling, Math
Predictive Analytics Tools (R,
Python, AWS ML, Google ML,
Azure ML)
Figure 2: Organizational Data Maturity
Figure 2 illustrates the common maturity levels an organization will progress
through as they become a data driven organization. An organization will not
seamlessly move from one level to another, but rather mature each portion of the
organization at different rates, depending on the skill sets and outside influence.
Description of the data maturity levels:
1. Data Access – This is the first level of data maturity, and characterizes
organizations that are early in their data journey. These organizations
often store information for reference, but do not regularly use that infor-
mation to drive decision making or work to integrate the data into third
party systems for automation in use.
2. Consolidation – This stage in maturity characterizes organizations that
have taken initial steps to integrate their separate datasets and create
more formalized applications for the presentation and updating of the
information. Decisions are still manual and human driven.
SUMMER 2017 | THE DOPPLER | 9