At TSIA, we stress that in order for businesses to create differentiating capabilities, they must focus on people, process, and technology. In this short 3-part interactive blog series, we plan to apply this logic to the question "How do we optimize an Analytics team?" In this post, we will focus on technology.
How Are Data Analytics Teams Spending Their Time?
Analytics teams often report spending "too much" time on getting and preparing data instead of analyzing it. According to an often-cited Forbes article, data scientists spend nearly 80% of their time collecting, cleaning, and organizing data. When we surveyed our member companies at the Analytics team level, on average, 68% of a team's time is spent on data reshaping and basic analysis.
In terms of analytic activities where software tools can be leveraged, we will focus on three:
- Data Reshaping: Querying, cleaning, joining, pivoting data for analysis, and visualization
- Data Visualization: Basic reports, dashboards, summary statistics to describe data
- Advanced Analysis: Multi-variate statistical analysis, data mining, supervised and unsupervised machine learning to uncover patterns in data for insight
Data Reshaping
Deloitte's 2020 Technology Outlook again highlights how changes in technology are leading to more and more data, with this year's report highlighting edge computing. Instrumentation provides teams with a massive data challenge along the original three V's of big data: volume, velocity, and especially variety. Even if you are not in the industrial equipment and healthcare technology verticals, dealing with edge computing and the emerging data pressures on your company are demanding innovation from your employees that handle data. Your team needs to efficiently extract, transform, and load (ETL) this data into analysis environments for reliable insights.
According to our earlier cited survey, teams that cited data access as a challenge spend 29% on data reshaping versus 22% that do not cite this issue.
One option is to use off-the-shelf technology to reduce the time spent on data reshaping. Providers of this type of technology include, but are not limited to, Informatica, Alteryx, Pentaho, Talend, Xplenty, and MuleSoft.
Please answer the below poll question to let us know if you are leveraging off-the-shelf tools for ETL processes and to reveal the current adoption rate by the industry.
Data Visualization
Leveraging new and emerging technologies helps Analytics teams tell clear, visual stories essential for driving organizations in the right direction. Making data-informed decisions can only happen if the consumers of the data can see and interact with a clear representation of the data.
There are an abundance of off-the-shelf data visualization (DV) and business intelligence (BI) software providers including, but not limited to, PowerBI, Tableau, Qlik, Looker, Domo, and Birst. These tools help analytic professionals tell beautiful stories in a very short amount of time, provided that the data is in the right shape.
Answer the below poll question below to let us know if you use off-the-shelf technology for data visualization and to see how many of your peers do.
Advanced Analysis
Analysts of all backgrounds and levels live to spend time uncovering patterns in data. And while the title of this blog emphasizes carving out more time for us to do this type of analysis, we also must make sure that this time is as efficient as possible.
The good news is that similar to the data visualization and business intelligence space, there are dozens of tools that make advanced analysis and machine learning efficient. Analysts still need a strong background in statistics and data analysis to make the right choices and interpret model feedback correctly, but new tools can accelerate our ability to build hundreds of models with a few key strokes.
Off-the-shelf providers of advanced analysis tools include, but are not limited to, RapidMiner, SAS, Azure ML Studio, IBM Watson, and DataRobot. Answer the poll question below to let us know if you use such tools for advanced analysis.
Next Steps for Optimizing Your Analytics Team
In order for Analytics teams to create value, they must focus on setting up the right people, processes, and technologies. Regarding technology, being able to leverage off-the-shelf solutions for your analytics work will undoubtedly create efficiencies. Regardless of your current adoption, consider these three key practices:
1. Create a roadmap to minimize data reshaping cycles
- ETL (extract, transform, load) tools
- Establish data cleanliness initiatives
2. Prioritize efficient data visualization
3. Study your team's time spent
Regarding people and processes necessary for optimizing your Analytics team, stay tuned for our next two posts.
We Want Your Feedback!
Thank you for reading through this post! We’d love to hear directly from you about what you’ll find helpful on this topic. Please use the below survey to tell us your thoughts on answering short survey questions in the context of an article.