Go back to Blog's hub Blog   |   tags:  

4 Key Team Members for Analytics in SaaS

Written by Zdenek Svoboda, Cassie Lee  | 

Share
4 Key Team Members for Analytics in SaaS

Each year, we kick the tires of quite a few analytical projects at GoodData. Fortunately, the GoodData platform already streamlines processes of the embedded analytics delivery process, like allocating hardware, installing operating systems, or implementing software components. With these bases covered, we’ve found that the key difference between success and failure comes down to having the right people onboard. Here is my perspective on what skill sets you’ll need on your team if you want to successfully launch analytics with your Software as a Service (SaaS) product.

UX Designer

Creating a successful SaaS product requires having a skilled UX designer that can encourage supportive and interactive user behavior. Now, you probably already have an experienced UX designer on your team, however, they’ll need to learn to take a few additional considerations into account when designing analytical workflows. One such example is understanding how to best utilize specific data visualizations. Typically, when we partner, we give your UX designers a crash-course in the thinking and design elements they’ll come across over the course of the project, so they’re ready to completely take over the embedded analytics design. This part is important in making sure the UX designers have a solid understanding of the platform interface so they can bridge the gap between the end users and development team, making your product easier to learn and adapt to.

Data Engineer

You’ll need a skilled data engineer for setting up a data pipeline to schedule background processes that:

  • Collect all data from your product and additional data sources to a data warehouse (either GoodData ADS, Snowflake, or Redshift).
  • Transform individual data sources to a semantic model.
  • Partition data to individual customer’s workspaces.
  • Set up the logical data model and base MAQL metrics that can later be reused by business analysts.

The data engineer is especially important for the initial phases of your project, when they’ll be setting up the baseline of your data pipeline. They will then take the data collected from their system and turn them into blueprints that allow data analysts to interpret and apply to business operations.

Why not try our 30-day free trial?

Fully managed, API-first analytics platform. Get instant access — no installation or credit card required.

Get started

Data Analyst

GoodData platform tools (e.g. Analytical Designer) provide unmatched ease of use and productivity when it comes to data analysis. Because these capabilities, logical data model, and important metrics have already been created by the data engineer, your data analysts are free to focus on completely understanding your product entities' life cycle and workflows. To help your analysts develop that understanding even further, product managers and designers can deliver relevant reports. As a result, even less experienced analysts can be successful, because there’s no need to build components from scratch.

A member we’d like to highlight specifically is a chief analytics officer, who manages data and strategically applies it across the business. Heightened pressures from competition within the industry requires managers to understand, monitor, and make strategic business decisions based on their analytics. Chief analytics officers do just that  — provide intuitive analytics tools to members of the company and create a unique strategy driven by their own data.

Product Manager

Lastly, a key team member you’ll absolutely need on your team is a product manager. This role is often under-appreciated in the success of a product, but is a valuable asset in implementation and user adoption rates for your SaaS product.

Product managers act as a bridge between technical and non-technical teams within an organization, which is beneficial in understanding the strengths and weaknesses from both sides. They ensure that the internal communication between different departments of the company are on the same page about the products.

Did you know that data analytic projects that fail are 70% due to low adoption rates from the users? Successfully implementing your product in the market and having high user adoption rates is not possible without both of those skills: knowing your product and knowing specifically who your product is for. Product Managers know everything about a product — why it needs to be built, what it should look like, when it is best used, etc. With their expertise in the product, they are able to provide insight on who the best target audience should be as well. Their firm grasp of target personas, market conditions, and buying behavior make them essential in the marketing of the product.

How GoodData Can Help

GoodData can support you from the very beginning, moving fast and efficiently . Our services team experts can cover all aspects of your analytical project delivery, and we work closely with your team so they can shadow, quickly learn, and take over in a few weeks. From there, you can decide whether you want to own or outsource your data analytics.

Why not try our 30-day free trial?

Fully managed, API-first analytics platform. Get instant access — no installation or credit card required.

Get started

Written by Zdenek Svoboda, Cassie Lee  | 

Share
Go back to Blog's hub Blog   |   tags:  

Subscribe to our newsletter

Get your dose of interesting facts on analytics in your inbox every month.

Subscribe