What Is Data Analytics?
Data analytics is the process of exploring, transforming, and analyzing data to identify trends and patterns that
reveal meaningful insights and building efficiencies that support decision-making. A modern data analytics strategy
empowers systems and organizations to act based on real-time, automated analytics, ensuring impactful, immediate
The Data Analytics Process
The data analytics process is built on multiple steps and phases. Learnings from a later phase might require moving
backwards and re-working an earlier phase, making it a more cyclical rather than linear journey. Most importantly,
successful data analytics processes are dependent on repeatability and automation between each of these steps.
The analytics process is best broken down into the following steps and phases:
surveying and investigating a large dataset through sampling, statistical analysis, pattern identification, visual profiling, and more. The methods are not necessarily scientific or conclusive, but rather, they serve to build understanding that leads to more informed data transformation.
Types of Data Analytics
There are several different types of data analytics. They are:
- Descriptive analytics: Answers the question “What happened?” (What were our sales
- Diagnostic analytics: Answers the question “Why did this happen?” (Why did our
increase from the previous week?)
- Predictive analytics: Answers the question “What will happen?” (What do we think
store sales will be during the holiday season?)
- Prescriptive analytics: Answers the question “What should I do?” (Based on our
we recommend shipping more of a certain product to prevent a stockout.)
Descriptive and diagnostic analytics allow data analysts and leaders to level set. These processes are building
blocks that pave the way for more sophisticated insights that result from predictive and prescriptive analytics.
Building a Mature Data Analytics Foundation
Data is omnipresent within any system or organization that exists today. Many systems or
organizations use analytics to improve their processes or experience impactful outcomes. There’s no
questioning if data analytics is important. The focus of modern organizations is on building a
mature data analytics strategy — one that ensures real-time insights and future-looking
A Modern Analytics Solution Depends on Automation
Within the data analytics practice there are a myriad of point solutions which align to each step or
phase mentioned previously in the data analytics process. A core issue of the point solution
approach, however, is the inability to easily automate the end-to-end data science and analytic
process. Analytic automation enables true
real-time analysis as it’s built on a
foundation of automation throughout the entire analytics journey in a single analytics solution.
With the introduction of data analytics automation, analytic teams and organizations can automate any
and every part of their analytics process — from initial input of data, through data cleansing,
enrichment, data science and machine learning, to writing the data into relevant apps, cloud
databases, BI platforms, etc. — all contained in one analytics solution.
A Modern Organization Depends on an Analytics Center of
Additionally, a company’s ability to compete in the emerging digital economy requires faster-paced,
forward-looking decisions. Thus, modern systems and organizations looking to digitally transform
must consider a modern data analytics strategy a “key accelerant” of their efforts.
Building a Data Analytics Center of Excellence
A center of excellence is a centralized analytics function built to effectively evangelize and
implement a data-analytics-first culture throughout the organization, with the goal of improved
operational efficiencies and processes, resulting in impactful, organization-wide decision-making
and real-time business outcomes. With an effective center of excellence, organizations are enabled
with internally led training, consultation, guidance, and support; can promote best practices;
implement analytics modelling methodology framework; and maximize ROI on analytics investments.
A successful center of excellence will also be the vehicle to connect data, analytics, processes, and
people. The convergence of these four pillars ensures data is democratized throughout the org,
empowers analysts to become citizen data scientists, automates the analytic process throughout the
analytics journey, and eases the upskilling of the workforce.
The data backs up the investment in a strong center of excellence: A recent survey notes the catalyst effect of a single,
common set of tools and methods across the enterprise for accessing and analyzing data, noting that
of the 26% who are doing this right, 80% exceeded their business goals. And in companies where all
personnel were educated about how to leverage data, 88% exceeded business goals compared to just 61%
of those with few trained employees.
Modern Data Analytics Use Cases
The use cases for data analytics in a digital first world are almost endless, from predicting customer behavior based
on omnichannel interactions to anticipating changes within a supply chain due to natural disasters. Let’s dig into a
few of the most common examples we experience across industries.
- Enabling efficiency through reporting: Alteryx + Daimler Trucks North America
- Safety stock optimization: Customer video: Amway
- Omni-channel logistics: Seko Omni-Channel Logistics
- Promotional insights: 7-Eleven
- Predicting customer sentiment: Mayborn
- AB testing for product placement: Barnes
- COVID-19 medical research to saves lives: Castor + The Information Lab,
- Mitigate risks: Kaiser Permanente
- Self-service membership and claims processing: Blue Cross Blue Shield North Carolina (BCBS NC)
- Critical demand and resource scenarios: Integratis
- Predict the extent of structural damage: FEMA
- Contact tracing: Public Sector Infobrief
- Global financial crimes compliance: MUFG
- Centralized analytics strategy: UBS
- Personalized tax advice: Brookson
Office of Finance
Next TermCloud Data Warehouse (CDW)
Saving Over 75 Hours Day with Automated Forecasting
- Data Prep and Analytics
- Data Science and Machine Learning
- Process Automation
Nippon Caterpillar Japan Streamlines Analysis Operations
- Data Prep and Analytics
- Asia Pacific