Choosing a tailor-made Analytics platform

The end-goal of an Analytics tool is to make decision-making smarter and more collaborative. What process should you use when choosing one? Follow this step-by-step guide

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Thibaut Collette

April 9, 2023 · 3 min read

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The end-goal of an Analytics tool is to make decision-making smarter and more collaborative: To achieve this, the platform must serve both future users: Data Analysts and Business Users. Many solutions exist on the market, but choosing the one that will suit you best requires you to dig deeper and take more accurate criteria into account.

A quick reminder: what’s a Data Analytics platform?

An Analytics platform combines various technologies into a unified solution. Its purpose is to meet business needs all through the Data Analytics Lifecycle.

What are the key features of a modern Analytics tool?

Analytics tools are designed to make business users more independent when making data-driven decisions

An Analytics platform enables business users to: 

  • access data easily;
  • discover insights that they can trust;
  • share data all across the company;
  • speed up decision-making.

Choosing an Analytics platform: the main criteria

When choosing an Analytics platform, you need to think about:

  • your Analytics team: will they be able to leverage this new platform? Is it suited to advanced Analytics projects?
  • your business users: will they actually be able to use it, quickly and efficiently?
Keep in mind that the best platform for your business users isn’t always going to be the best platform for your Data Analysts. As a consequence, you will need to find the right balance between these two teams and their expectations.

A solution that suits your business users

#1 Identify your business users (or end-users)

  • Who are they? Which department do they work in?
  • How many business users are there? Should the whole staff of the company get access to this platform?
  • What is the knowledge level and data literacy of these business users? Are they self-sufficient when it comes to data exploration?

For instance, a solution such as Metabase is more appropriate for technical teams, whereas Looker is designed for people who do not possess any data hard skills. 

#2 Define your business users’ needs

  • How much will they use reporting?
  • What kind of advanced Analytics will they need?
  • Do they want to share their reports? If so, to which business users?
  • Do they use multiple data sources (especially if you do not have a unified data warehouse)? 

#3 Determine your business users’ specific expectations 

  • Are they familiar with specific charts?
  • Can the platform handle this particular kind of chart?
  • Do they need to access data remotely?

#4 Assess the tool’s UI and accessibility

Self-service Analytics solutions should offer a user-friendly interface that can be handled by various user types. The solution needs to allow non-tech users to create and understand reports and dashboards on their own.

A solution tailored to your business 

You should see the choice of your Analytics platform just like any other IT investment: keep in mind that it should support both your current and future business requirements.

Here are some issues you should consider:

  • Pricing: what is your maximum budget? Do you need a flat rate or a price that varies depending on the number of business users? Do you need a Pay-as-you-go plan?
  • Type of solution: do you want an on-premise solution or a cloud solution?
  • Ability to connect with your existing system and third-party data sources: are you looking for an Analytics platform from the same provider as your data warehouse, in order to avoid egress charges? For instance, if all your data is already stored by Google, it can make sense to work with Looker or Google Data Studio.
  • Security is another essential criteria: make sure you check the compliance certification. Also, if you choose a cloud solution, find out where your data is stored: our recommendation is that you look for a European solution (such as Trevor and others) – and even if the solution is American, the data source should be stored in Europe.

What about modeling?

Modeling helps your business users work with data and create reports. However, it represents a huge workload for your Data team – especially regarding data preparation and maintenance, so as to keep the data up-to-date.

3 kinds of solutions are offered on the market:

  • with some solutions, all you need to do is connect to your database in order to create your own dashboards directly;
  • with others (such as Looker), modeling and specific data preparation are required before your team can start creating dashboards;
  • others (such as Metabase) are easier to use, although some quick modeling is needed.

Also remember that providing your Data teams with a simple SQL editor will make their work much easier! 

If you’re only looking for a basic tool that will allow you to display KPIs, a classic Analytics platform is more than enough. But when using a dashboard, you often need more context to understand the analysis: Business Analysts then have to copy/paste data from their BI tool to Notion or Google Docs, which takes up a lot of time and can damage the data’s quality. 

To counter that, choose notebooks. Notebooks enable you to gather your Analysts’ work and data documentation in order to help business users make informed decisions through Data Storytelling.
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