Analytics In The Background
Analytics in Operations Part 3: Analytics challenges in business operations
Falling Behind
Is it difficult to get the information that you need for critical projects?
Does every data request set off a data scavenger hunt?
Do you feel like your reporting is stuck in the 90s?
Good data quality and the ability to easily extract useful information from that data is critical to business operations and company success.
Unfortunately, common analytics challenges often get in the way. These challenges affect all companies and all divisions to some degree. However, the nature of business operations functions intensifies these issues.
Let’s take a closer look at the data and analytics challenges in these functions and where companies tend to lag in analytics.
Data Quality
One of the biggest impediments to analytics is data quality. Poor data quality leads to poor analytics results and wasted time and effort.
There are several areas where data quality issues arise in business operations and where companies may struggle.
LEGACY SYSTEMS
Data is at the core of any analytics effort and being able to easily access this data in a user-friendly format is critical. Newer systems are designed with this idea in mind, but older systems can present significant data extraction challenges.
Any company that has been around for more than a few years likely has older systems. System migrations are complicated and expensive so older systems tend to stick around for longer than they should.
Since operations is responsible for internal data tracking, they tend to have the bulk of these legacy systems, such as employee management, talent acquisition, finance/accounting, procurement, resource planning, etc. These systems are less noticeable to those outside of operations and they are more likely to be outdated than client-facing and other high visibility platforms.
MANUAL TRACKING
Another key piece of the analytics process is accurate and consistent data. There are numerous best practices to ensure good data quality when data is stored in a system.
Unfortunately, it is common in operations functions for data to be tracked manually by individuals in Excel spreadsheets or, even worse, on paper. This compromises data integrity and consistency resulting in unreliable or inaccessible data.
DATA SILOS AND DATA DUPLICATION
Data quality and consistency is critical to producing reliable data insights and reporting. This requires communication and coordination between departments as well as between business operations and the technology/systems teams.
Unfortunately, operations functions can be disconnected from each other leading to process and data silos. This results in data inconsistencies and data duplication where each function is tracking their own set of data but may be measuring the same process. For example, HR and Finance might each be tracking their own set of headcount data.
DATA QUALITY AND CONSISTENCY
All of these issues lead to data quality, consistency, and reliability issues, that make it difficult to produce reliable data insights, reporting, and analytics.
Data Insights
While data quality is the greatest challenge in analytics, the next challenge is transforming this data into useful information. Business operations functions generally struggle in one of more of the following areas.
MANUAL REPORTING
Reliable data insights and key performance indicators are critical to understanding, managing, and planning operations.
Reporting processes are typically built up as needed over a long period of time. These processes are repeated quarterly, monthly, and even daily. They often rely on a compilation of Excel spreadsheets or Access databases and require a series of manual steps to extract and transform data.
This tedious and time-consuming work takes analysts away from the more valuable and engaging analysis that they should be doing and increases the chance of errors and inconsistencies.
This is especially true in operations due to its cyclical nature, and because the focus is on keeping everything running smoothly rather than analytics innovation. As a result, there is rarely a comprehensive analytics strategy behind report development. Organically grown, manual reporting processes are common.
OVER-RELIANCE ON EXCEL AND LEGACY TOOLS
Being able to efficiently extract, transform and analyze large amounts of data has become critical as the amount and complexity of data has increased.
Newer tools and platforms can make these tasks easier and help with automation. However, because operations functions tend to be risk averse and have limited time to experiment, they are slow to adopt new methods and tools. They may still rely heavily on Excel or older propriety tools for analytics and reporting. While there is still a role for Excel, it’s not a great tool for automated company reporting and it’s not up to the task of more advanced analytics.
Relying too much on older tools and methods holds companies back and prevents them from realizing the full potential of their data and their people.
ANALYTICS EXPERIENCE IS LIMITED
In addition to modern systems and tools, operations functions also need people who are trained in analytics methods and how to use analytics tools. Those in operations haven’t typically hired for these skills, and due to the nature of these functions it can be challenging to find the time and support for people to develop these skills on-the-job.
A Different Perspective
Business operations functions by their nature are mostly out of sight and often forgotten unless something goes wrong. If your operations functions rely on outdated tools, systems, methods, and processes, then your company will be less efficient and less productive. Despite the many obstacles, improving analytics in business operations functions is critical.
The next post in this series will focus on ways to address these analytics challenges.
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About the photos: a few more from Grand Teton National Park, Wyoming
Thoughts: For a spectacular sunrise get up early and check out the summit of Signal Mountain. There’s a road to the top, so no need to hike up in the dark, unless you want to. If you’re lucky you might even see a bear or two.
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