Gaining Perspective
Analytics in Operations Part 4: Addressing analytics challenges in operations
Getting Started
Do you wish you had better data?
Would you like to do more with that data?
Have you tried to make improvements, but it hasn’t been as successful as you’d like?
Operations has unique challenges that make it difficult to improve data and analytics capabilities but catching up is critical to improving overall company results.
Let’s consider ways to address analytics challenges in operations.
The Base – Better Data
The ability to access, analyze, present, and display reliable information depends on good data quality. The analysis becomes difficult or even impossible when there are underlying data problems.
Improving Data Quality
Ensuring data quality, consistency and accessibility require addressing some of the key analytics challenges in operations, including legacy systems, manual tracking, data silos, and duplication. These challenges are tied to systems, business process, and communication issues.
working with LEGACY SYSTEMS
We know that older systems often pose data extraction challenges. Depending on the system, there may be some workarounds. It may be possible to have a systems administrator set up custom data extracts for reporting and analytics that refresh on a scheduled basis. Alternatively, it may be possible to change the process for how data is entered or add new data fields to make it easier to retrieve key information.
Moving Past LEGACY SYSTEMS
However, there may be no quick or easy fix to this problem. You’ll need good communication with the technology team to identify potential short- and long-term solutions. In some cases, the only effective solution may be to upgrade or replace the system despite the complexity and expense.
The PROBLEM WITH MANUAL TRACKING
Manual tracking occurs when users enter and store data in spreadsheets and local tools, or on paper. This is a major source of data inconsistencies, missing data, and poor data quality. All data should be stored in a system.
Why it Occurs
This issue generally occurs for one of the following reasons, each with its own potential solution:
There is no existing system that is appropriate for the type of data that needs to be stored (e.g. no customer relationship management (CRM) system to track clients).
In this case, you’ll need to consider whether to invest in a new enterprise system or tool.
The appropriate system exists but isn’t configured in a way that allows easy/intuitive data entry (e.g. entering and tracking clients is overly complicated and/or lacks customization).
Resolving this issue requires coordination between the business and technology teams to understand business process requirements and identify technical solutions. For example, adding or adjusting fields or tables, or making changes to the user interface (where possible) can improve how data is entered and stored.
People don’t understand how to use the system effectively or don’t know that it exists, so they choose to enter and store data locally. The result is that the system isn’t used or is used inconsistently, leading to manual tracking and inconsistent data.
This can be addressed by providing clear business process direction on when/how/what data to enter as well as technical training on how to use the system.
This requires patience as it may take several iterations until there is widespread consistent use. However, if the system was worth the investment then it’s worth the additional marginal investment to ensure it’s being used effectively. Companies often invest in expensive platforms and then get frugal about the training and communication needed to ensure that the system is used optimally.
Eliminating manual tracking through system, business process, and communication changes will go a long way toward improving your data quality.
Connecting DATA SILOS
Data silos result in poor data quality and duplication of efforts. Data silos are generally the result of departmental silos related to the company culture.
This is a business process and communication issue. The best way to address this is by improving communication between departments/divisions and between more and less technical teams. This is much easier said than done but it will go a long way toward improving all aspects of data analytics in business operations.
The Climb - Using Data Better
While business process, communication, and systems challenges are at the core of data quality issues, knowledge of analytics methods and tools are necessary to turn this data into useful information.
tools alone are not enough
There are a wide variety of reporting and analytics tools, many of which have become increasingly user-friendly and require less technical knowledge. However, analysts still need proficiency in data and analytics methods to use these tools effectively.
Analytics Knowledge is key
Knowledge of analytics methods and how to use analytics tools is necessary to derive insights from data and develop better and more automated reporting. However, these skills are generally not as common as they need to be in operations.
Developing ANALYTICS EXPERTise
There are two primary ways to acquire these skills: train (upskill) or hire. There are several considerations when deciding whether to improve your in-house talent vs looking to external resources or a combination of the two. There are advantages and disadvantages as well as costs associated with each of these options. Later in this series, we’ll take a closer look at what roles are needed and the types of skills that need to be developed.
when tools matter
Once you’ve addressed business process, systems, and communications issues, and you have people with the necessary analytics skills and knowledge, then the right tools will improve how efficiently and effectively you can extract, transform and analyze large amounts of data.
time to upgrade?
There are a wide variety of analytics tools available, both general and specialized, but operations functions often rely on Excel and older proprietary tools which limit their analytics capabilities.
The specific tools are often less important than all of the other pieces. Consider which tools will be most effective and easiest to use based on your current capabilities. Determining which tools are right for your company will vary based on your business problems, your data infrastructure, and your analytics skills and knowledge. There isn’t a one-size-fits-all solution.
manual reporting wastes resources
Reporting is another area that is challenging for operations and often occupies far too much employee time, from creating reports to accessing and using them in productive ways. Manual processes are common and require employees to repeatedly run through tedious, time-consuming, and error-prone tasks. This leaves little time for more valuable analysis and developing more advanced skills.
Automating and Improving reporting
Report automation, as well as data visualization through interactive dashboards, is critical to get to a functional level of basic analytics. This will provide critical on-demand and up-to-date information and allow for more informed business decisions. It will also reduce the manual time spent on tedious reporting tasks, freeing up time for more complex analysis.
Improving data quality is the first step, followed by developing reporting and business intelligence skills and knowledge. With these in place acquiring a business intelligence tool will make it easier to develop and maintain automated interactive reports.
The Summit – Better Information
High-quality data is the key to reliable data insights, reporting, and analytics. With a solid data and reporting foundation, it’s possible to move to the next level and begin to take advantage of advanced analytics.
Data analytics challenges, including poor data quality, typically arise from a combination of systems, tools, methods, and business process issues, as well as weaknesses in communication/coordination between operations divisions.
Many data analytics challenges are really process and communication issues: they are cultural rather than technical.
Improvements in these areas will improve data analytics capabilities and effectiveness in business operations functions which in turn will make the company more effective with better business results.
The next post in this series will consider the coordination needed between various divisions and teams.
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About the photos: Harding Icefield Trail, Kenai Fjords, Alaska
Thoughts: The Harding Icefield is one of my all-time favorite hikes. It’s an all-day challenging hike, but if you’re in good hiking shape I highly recommend it. The views of the icefield are spectacular. You’re also likely to see some wildlife up-close. The first time I hiked the trail I came within 6 feet of an enormous black bear and later encountered a moose at a similar distance.
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