When we analyze data, we are usually looking for insights. In the business world, these insights enable us to make changes that improve performance or cut costs. In the world of research, these may be insights that allow us to better understand the implications of our data. However, without the ability to visualize our insights, analytics falls flat. We all know that charts and tables make insights pop out in an almost magical way, so it’s not surprising that the data visualization market is fast moving and competitive.
In general, as a domain matures, stratification occurs. Analytics and data visualization is no exception. Where we used to see monolithic applications such as Cognos and Microstrategy that provided “the whole shebang” — analytics, data visualization, ad-hoc exploration, dashboards, reports, notifications, and collaboration, now we see a plethora of products.
A quick look at the 2021 Gartner report shows 20 products competing in the analytics space. Tableau and PowerBI lead the pack but Qlik, Domo, ThoughtSpot, and Looker are on their heels. Each product has its own special sauce. Qlik’s strong product vision for ML and AI makes it a leading solution for businesses that want insight suggestions. Domo’s ease of use and fast deployment makes it popular among marketing and sales departments. ThoughtSpot’s search-driven user experience attracts non-technical business users that want to search for insights without needing to really understand analytics. And Looker, the new kid on the block, provides a new and powerful paradigm that enables data engineers and developers to customize data applications for their analysts and business users.
Like most tech, the analytics domain is a moving target. 10 years ago data visualization was all the rage, but as of late, the concerns are more about collaboration.
All of these concerns, and more, go into the decision as to what analytics product or products a team will use.
The key takeaway here is that as an industry matures, it naturally stratifies. Monolithic tools make way for a multitude of new, more focused tools. New concerns emerge and new products evolve to meet the demand. The upside is that new and powerful features emerge. The downside is that the product you love today, may not be the product you use tomorrow!
Gartner’s Top 10 data and analytics technology trends for 2021 lists Trend 2 as “Composable data and analytics”, noting that composable analytics provides “a more agile way to build analytics applications enabled by cloud marketplace and low-code and no-code solutions”.
If you are designing an analytics stack, it’s important to stay flexible. When it comes to analytics, you may choose a data warehouse such as BigQuery or Snowflake for periodic reporting on historical data, and you may choose an operational database such as Apache Druid or Pinot for decision making on your real time data. When it comes to data visualization, you’ll want similar flexibility to choose the right visualization tool for the task. You’ll need to consider the dashboarding features you need, the type of collaboration you want, whether you want to do reporting or ad-hoc analysis and the expertise level of your users. The product you choose may be one of today’s known leaders, or it may be tomorrow’s new contender.
At Rill, when it comes to your choice of analytics, we believe that Apache Druid is a great option for your operational analytics needs. We provide a fully managed cloud solution that allows you to perform fast analytics with no DevOps overhead.
But what about data visualization? The reality is, we are agnostic about data visualization providers so we enable easy integration with your existing tools. You may like a product because it is the best tool on the block for your use case, or simply because you’ve already paid for it and it’s at your fingertips. Whatever the reason, we recognize that interoperability is key and we strive to support your team with whatever data visualization tool you like best.
Rill currently integrates seamlessly with industry standard business intelligence tools such as Tableau, Looker, Superset, Jupyter, and custom applications. Our team has experience with the pros and cons of all of these tools and we invite you to reach out for more information or advice if you are in the process of creating an operational analytics stack.