The Trees
Analytics in Operations Part 6: Analytics knowledge, roles, and skills needed in operations
Analytics Capabilities
Are you trying to improve the analytics capabilities of your team?
Is it a challenge to find time for staff development while maintaining core operations responsibilities?
Do you wonder what roles you need and if you should hire new people or train your existing staff?
Let’s look at the knowledge, skills and roles needed to improve analytics in operations.
Raising The Bar
Business operations is a unique environment with a particular set of analytics challenges. Solving these analytics challenges requires changes and improvements in systems, tools, methods, business process, and communication and better coordination between teams. This in turn requires improving the overall level of technical and analytics knowledge among the business team.
Not just for analysts
It’s not just the analysts that need analytics knowledge; analytics is a team sport. Throughout operations, people need to increase general knowledge and awareness of technology and analytics. While everyone does not need to become a data analytics expert, understanding more about data and analytics will make it easier to work effectively with technology and data professionals. There are several areas where more knowledge and understanding is helpful.
Systems
A better understanding of existing systems and their limitations, as well as how data quality issues arise, will help in interactions with the technology team and analytics professionals. This will also help with understanding problems and advocating for solutions.
Tools
Upgrading existing tools requires understanding which tools are no longer effective, or efficient, and being able to evaluate alternative options.
Methods
Since nearly everyone must deal with data in some capacity, improving knowledge of basic data analysis methods will allow them to work more productively (e.g. learning to use existing tools more effectively and efficiently). Business professionals should learn enough about data and analytics to know when it might be useful to enlist the help of data professionals and how to direct and inform this work.
Business Process and Communication
As business experts, operations professionals will need to help analysts, analytics teams, and the technology team understand business needs and problems. Increasing knowledge and awareness of the technical side will make it easier for business operations professionals to communicate more effectively with technical teams leading to better project outcomes.
Analytics Roles
Defining analytics roles can be challenging. We’ve talked about analytics roles before but let’s get into more detail about the roles and skills that will be needed to improve analytics capabilities in business operations.
Busines Process Expert/Project Manager
A business expert is critical to determine business priorities. Business knowledge is the key input to any analytics project, followed closely by effective coordination. A team with a combination of business and technical skills will be most effective in solving business problems.
The primary contribution of the business expert is an understanding of the business. However, in order to inform the analytics process they will need some understanding of the current business systems and enough knowledge of analytics tools and methods that they can coordinate effectively with the analysts and the technology team. They’ll need to provide business direction and ask the right questions to keep the project on track.
While they don’t need the detailed technical knowledge of a data analytics expert, they will need a high-level (basic) understanding of data issues and when analytics is helpful.
Data ENGINEER
Data quality and availability is often the biggest roadblock in analytics efforts. Data engineers are central to ensuring good data quality and accessibility.
Data Engineers can help with setting up data correctly in a system, identifying and addressing the source of data issues, extracting data from systems, and getting the data into reporting and analytics tools. You may hear the term data pipeline in connection with this set of processes.
Data engineers typically have database and/or data warehousing expertise, including computer programming skills.
Data analyst
The goal of analytics is to derive useful business information from data. In the early stages of building out an analytics functions, a data scientist is usually not necessary, but data analysts will be critical.
The role of a data analyst is to analyze large datasets and derive insights from multiple data sources. They need to be able to identify the data they need and interface with the technology team as necessary. They should have a good understanding of basic analytics methods and tools as well as some advanced analytics knowledge.
They’ll need to learn and understand the business processes and business problems.
Business intelligence Analyst
Reporting is a critical area of need for operations and often occupies far too much employee time, from creating reports to accessing and using them in productive ways.
A business intelligence analyst focuses on report automation, efficiency, and the effective display of information to design and create interactive dashboards.
They need skills in data cleaning, data transformation, data visualization, and a basic understanding of databases and accessing data from various sources including data warehouses. They’ll typically have experience with one or more enterprise business intelligence tools (Power BI, Tableau, etc). They will also have some computer programming capabilities including SQL to query databases, and R or python (programming languages commonly used in data analytics).
Data scientist
Once you’ve established basic analytics capabilities you may find that you’re ready for and need more advanced analytics. At this point you’ll want to consider adding a data scientist to your team.
A data scientist has an advanced level of analysis, modeling, and computer programming skills. They can handle very large/real time datasets and can develop complex models. A data scientist also needs some business understanding and the ability to work closely with a business expert who can provide business knowledge context to guide the technical work.
There are many types of data scientists
A data scientist isn’t a monolithic role. There are a wide variety of specialized technical skills among data scientists, and the specific skills needed will vary by industry, company, and division.
A Word On data science job descriptions
There are far too many poorly worded and confusing data science job descriptions out there. Make sure that yours isn’t one of them. These often present as a laundry list of every data science skill and tool out there. If you need a specific set of skills, be sure to define the role appropriately and specifically. Don’t list skills or tools that you don’t need now or in the near future.
DEVELOP OR HIRE?
Business operations functions that are just getting started with analytics will need to add employees to many of these roles. In some cases, you may have people with the expertise to step directly into these roles or the ability to develop into the role with the right guidance. In other cases, you may need an external hire.
Improving data analytics capabilities will likely require a combination of training existing staff and hiring new people. Each comes with its own challenges.
Looking Within
Roles that require more business knowledge and are less specialized/technical will be easier for existing employees to step into. These roles include business knowledge experts, data analysts, and business intelligence analysts.
Every company has talented employees who want to learn more about analytics and/or have more advanced capabilities that are underutilized. Consider which of your analysts are interested in and capable of augmenting their knowledge and skills. It’s likely that you have people who have the foundational skills for these roles but need some training.
Why look within?
Internal resources may need to improve their analytics knowledge but they are already familiar with the nuances of your business processes and how to work within your culture to get things done.
making TIME for Talent dEvelopment
One of the key challenges in business operations is lack of time. Learning new skills takes time and requires the opportunity to apply that knowledge.
For an existing employee to grow into a role they’ll need mentorship and guidance. They’ll also need time away from day-to-day responsibilities for training as well as the opportunity to experiment and apply new knowledge and skills to real business problems.
This may be difficult at first, but it will pay off in the long run as manual data and reporting tasks are automated freeing up time for more valuable analysis work. In addition, you’ll likely end up with a more dedicated and engaged employee as you’ve given them the chance to develop and learn new skills.
Broadening The Search
When it comes to advanced analytics/technical resources like data scientists and data engineers, it’s less likely that you’ll have this level of skill or knowledge unless you’ve explicitly hired for or developed this skill set.
WHen To hire
New hires may be necessary if the skill set for an analytics role doesn’t already exist and would be difficult to develop in the appropriate time frame.
Talent acquisition Challenges
While new hires bring immediate access to advanced technical and analytics skills, the hiring process for data and analytics talent can be time and resource intensive.
There is also a learning curve for new employees to become familiar with your business, systems, processes, and company/department culture. In addition, they’ll still need time to learn new technical and analytics concepts. Technology and analytics is constantly evolving and your company’s needs may be a bit different from the company they came from.
When an advanced skill set is needed immediately and doesn’t already exist in house, hiring can provide a faster solution, but may not be quite as fast as you’d expect.
A Team Approach
Multiple people, skills, and roles are needed to develop analytics capabilities. These roles include data engineer, business expert/project manager, business intelligence analyst, data analyst, and data scientist. Each brings a specialized skill set. The strategy for filling these new roles will likely be a combination of developing internal talent as well as hiring new talent.
We’ve covered many aspects of analytics in operations. The next and final post in this series will consider how to get started with analytics and continue to build these capabilities in operations functions.
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About the photos: Joshua Tree National Park, California
Thoughts: I enjoyed Joshua Tree much more than I would have expected. In addition to the trees themselves, there are a lot of interesting shrubs and rock formations. The Mojave section of the park has a lot of short trails which are great for less adventurous hikers. It also attracts a lot of rock climbers, although I didn’t personally do any climbing. Finally, drive the road through the park to see the fascinating transition from the Mojave to the Colorado desert.
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