The Forest
Analytics in Operations Part 5: Analytics and technology coordination for business operations
A High Level View
Are you developing an analytics strategy?
Is it difficult to get everyone on the same page?
Do you wonder if you should create your own analytics team or coordinate with other divisions/groups?
Improving data analytics capabilities requires multiple skill sets and departments working together. Every company is unique, and coordination will vary by company. There is no one right way or one-size-fits-all.
Let’s look at a couple of areas where challenges often arise, and where you’ll need to identify the best strategy for your company.
Where Does Analytics Belong?
Some companies create a centralized team that oversees analytics throughout the company. It’s expected that all analytics issues, questions and requests go through this centralized function.
Other companies have analytics resources integrated into several business teams. Sometimes this is the result of a deliberate strategy, in other cases it develops organically with business teams hiring analytics professionals or developing their own expertise to meet their individual business needs.
There are pros and cons to each of these structures.
Integrated
The advantage of analytics resources (one or more analysts) being embedded in the business team is that they are closer to the business process and problems and can devote time to the needs of the specific business team. They’ll have a better understanding of the process and easier and more frequent communication with the business team so they can develop more targeted solutions.
The Problem with IntegrateD Resources
The risk is that these analysts become detached from other analysts and teams and lose sight of the overall company direction. This can lead to a variety of tools and methods being used throughout the company, duplicated efforts across teams, and can compound data silos. An embedded analyst or analytics team is also more likely to get pulled into day to day operations tasks and ad hoc requests and away from analytics.
Centralized
The advantage of a centralized analytics team is that they can focus exclusively on analytics while working with multiple business teams. They are also more likely to be aligned with the overall company vision and maintain a coordinated strategy (systems, methods, and tools) across divisions.
The Problem with Centralized TEAMS
A centralized team is more removed from the individual business teams, so the risk is that they lack a complete understanding of the individual business process and problems. They will be more focused on common problems that affect multiple teams, rather than the unique challenges of specific teams. As a result, the specific needs of business teams may not be well met in this model leading to the development of rogue analytics teams. In addition, because they are more removed, these teams are more likely to be distracted by projects that are analytically interesting but don’t provide business value.
HYBRID
A third option is a hybrid model with a centralized analytics team as well as analysts embedded within some of the business teams to provide specialized attention while coordinating with the centralized team.
WHY CHOOSE A HYBRID MODEL?
A hybrid model may work well for a company that needs centralized coordination for systems, tools and processes, but also has business teams with specialized individual needs that won’t be well met in a centralized structure.
This model can help a company maintain overall coordination and consistency and minimize duplication of efforts across the company. At the same time, it provides a way for teams with unique analytics needs to address those within the business team, in coordination with the centralized team.
There isn’t one best way. You’ll have to decide what works best for your company and possibly experiment with different approaches.
Who Is Looking Out For Your Data?
Depending on the maturity of your company’s data infrastructure you’ll likely have significant data needs.
What is the state of your Data infrastructure?
Data needs will vary a lot by company based on its business needs and the maturity of its data infrastructure. In some cases, data warehouses have already been built and data is connected, centralized, accessible, and reliable (this is rare). In other cases, a lot of work must go into supervising the development of a data warehouse or somehow centralizing, cataloguing, and validating data sources (this is much more common).
Where are the resources?
A lot of early work in developing analytics is focused on the data itself. This includes identifying data sources, validating and cleaning data, setting up data correctly in a system, identifying and addressing the source of data issues, and extracting data from systems. This is generally the domain of data engineering. The technology team may or may not have the necessary skills and capacity or be receptive to working with the business teams. In some cases, dedicated resources will be needed.
How Will you coordinate?
Business intelligence work will require coordination and access to existing systems, data warehouses or other data resources. Consider in advance how the coordination with the technology team might work and start building a strong relationship early. Decisions will need to be made about whether the business side will have dedicated technology resources and, if so, how they’ll coordinate with the technology team.
What Works Best For Your Company?
Good coordination within and between teams is key to a successful analytics strategy. There are several ways to approach this, depending on your company characteristics. There isn’t one right way, but there are several things to consider when developing a coordination strategy.
Will you have a centralized analytics team, or will you integrate analytics resources into each of the business teams, or a combination of the both?
Will you have dedicated data engineering/ technology resources within the business team and how will you coordinate with the technology team?
The next post in this series will look at the [skills and roles] that are needed to improve analytics in operations.
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About the photos: Windham High Peak, Catskill Mountains, NY
Thoughts: The Catskill Mountains are just a few hours north of New York and provide good hiking opportunities, including the Windham High Peak area (a popular New York ski resort in the winter).
The new dog in town: You may have noticed a new dog in the photos. Meet Sebastian! He doesn’t have a company named after him – yet – but he’s shaping up to be a great hiking companion. Follow his outdoor adventures on Instagram @Sebastian_Times.
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