Behind The Scenes
Analytics in Operations Part 1: The nature of business operations and why it leads to analytics challenges
Getting Clarity On Analytics In Operations
This is the first of a seven-part series about analytics in operations.
Operations refers to business support functions including finance, human resources, business development, sales, and technology: the functions behind the scenes that keep a company running.
What this series will cover:
Understanding the nature of operations and why this makes it more difficult to improve analytics capabilities in operations functions.
An analytics framework for understanding the analytics process better, particularly if you’re new to analytics.
The analytics challenges in operations and ways to improve data quality and get better data insights.
The coordination and the analytics knowledge, skills and roles needed to improve analytics in operations.
How to get started with analytics and continue to build these capabilities in operations functions.
Out Of Sight
Do you lead an operations function, or a part of a function, like finance/FP&A, HR, IT or business development?
Is your function falling behind in data and analytics?
Have you tried to make changes but haven’t been as successful as you’d like?
Operations functions have unique challenges that make it difficult to improve data and analytics capabilities. This results in data and analytics weaknesses that can undermine company performance, and because these functions are mostly out of view these problems don’t get addressed.
Let’s start with understanding the nature of these functions and how it leads to analytics challenges.
The Nature Of Operations
Flashy headlines around AI and machine learning can make it seem like analytics is all virtual assistants and complex algorithms, advising us on what to buy next or predicting the next market downturn (or claiming to).
People working in areas of advanced analytics generally work in environments with newer systems and cutting edge tools, where innovation is expected and risk is likely to be rewarded.
OPERATIONS IS A DIFFERENT WORLD
This is not the reality in most business operations functions. Systems and tools are older and often outdated, while the environment itself can be a challenging place to implement an analytics strategy.
A better understanding of the nature of operations will help when considering improvements in the data and analytics capabilities of these areas.
MAINTAINING CORE OPERATIONS IS KEY
The primary focus of operations is on keeping everything operating smoothly. If you think about a railroad, this literally means keeping the trains running on time.
MOST WORK IS CYCLICAL
Daily/monthly/quarterly deadlines and responsibilities are the norm, with a lot of reporting, planning, and scheduling.
LESS DATA ORIENTED
While this is beginning to change, many operations functions, such as human resources, have traditionally been less data driven and informed.
INNOVATION IS RISKY
If nothing is broken, trying new methods and tools is often discouraged while risk taking is rarely rewarded.
OPTIMIZATION with OLDER TOOLS
While optimizing company operations may be a core responsibility this is often accomplished using outdated tools, systems, and methods.
LIMITED TIME TO EXPERIMENT
The nature of operations leaves limited time to experiment with new methods and tools. Since developing analytics expertise often happens through experimentation this hinders the development of analytics skills.
EVEN TECHNOLOGY LEADERS ARE NOT IMMUNE
Surprisingly, this can affect companies that are otherwise technology leaders. Your company can be an industry leader in AI, marketing analytics or software engineering, while your support functions are lost in the data wilderness.
Losing Ground
Business operations functions face a variety of analytics challenges. The nature of these functions compounds the difficulty of making improvements in this area.
These are the functions that keep a company running and operating smoothly. Allowing them to fall behind in analytics will make it difficult for a company to get timely business insights and ultimately affect company profitability.
The next post in this series will provide more context for understanding and addressing analytics challenges. Sign up for our email list to be the first to know when the next blog is published!
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About the photos: Grand Teton National Park, Wyoming
Thoughts: Grand Teton is just south of Yellowstone. It doesn’t have the geothermal attractions of Yellowstone, but since it’s a bit less trafficked you have a better chance of seeing more wildlife.
Have a data or analytics question that you’d like to see answered here? Email your questions to stacey@arielanalytics.com.
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Stacey Schwarcz is the Founder and CEO of Ariel Analytics. She specializes in analytics for business operations, helping these functions improve their analytics capabilities. She is also the creator of The Data Wilderness ® Blog, which provides practical introductory analytics content for business professionals who are not analytics experts and want to learn more. LinkedIn