Every day, companies are making incredibly significant decisions based on data. These decisions improve products, alter prices, enhance customer experiences, keep people safe and even change lives. But as game-changing as those daily outcomes are, we know that the most critical decision a company makes about data is choosing its technology stack to support the data analysis workflow. 

The initial selection of a platform to store, track and analyze your data lays a foundation for all the important questions that come afterward. Companies are understandably meticulous about their choice of data analytics vendors because they know a misstep early on could restrict the powerful outcomes they get from data down the line. 

Often the biggest questions we hear during this evaluation process center around three key themes: flexibility, choice, and speed. 

Businesses want flexibility in the methods and languages that drive their data analytics workflow, because they never want to miss an opportunity to make a valuable data-driven decision. Today’s modern data teams use SQL in conjunction with more expressive languages like R and Python to do predictive analysis or join predictive results with other data. Finding the right data platform with these languages can provide a huge advantage because everyone else is also using those languages to do analysis. That means more open source libraries, more tools and more resources to learn from. Compared to analytics platforms that require you to learn a proprietary markup language, using standard languages is the obvious right choice. 

These businesses also want freedom of choice to use partners and integrators that best match their priorities. With the array of data warehousing options on the market, customers know that each one provides its own advantages, depending on their use case and level of data maturity. The last thing a data team wants to see is limitations on the choices they can make with their warehouse – they should be free to pick what’s best for the business, whether that’s Snowflake, Redshift, BigQuery, a combination of those or other options. That becomes a major concern with platforms whose exclusive partnerships and business relationships limit the ability for customers to build their ideal data workflow.

And critically, today’s modern businesses want to find the quickest time to get insights out of their data. They know that they will regularly face new, unanticipated business questions — the kind that usually have major impacts on a company — and that they shouldn’t have to wait hours or weeks to get the answers to those challenging questions. These data teams shouldn’t have to pre-define metrics before every analysis – that is incompatible with a culture of experimentation. With a well-built data workflow, there needn’t be any modeling layers that create roadblocks between data sources and the final analysis. 

Sisense has hundreds of integrations and partners that make connecting data from multiple sources as simple as logging in. And we also built Data Engine to help clients get faster query performance and data ingestion at scale for any kind of workload regardless of concurrency, size or complexity. Having SQL as our foundation has made the platform far more accessible to all users, and adding R and Python functionality built directly into our platforms enables much more powerful analysis. Our approach to data leverages the best aspects of models — groomed, pre-aggregated data using SQL Snippets, Views and templating — as well as the flexibility of joining that modeled data instantly to other tables.

Data is only valuable if you can access information and find answers quickly. Choosing the right BI platform that supports your preferred workflows can make the difference between a data-driven culture and one that completely loses confidence in the value of data. We recognize the wide array of ways that our clients explore and analyze data today, and we’re proud to be building a platform that can deliver deep insights in real time for all of them. 

If you’d like to see how this line of thinking applies to our industry as a whole, check out this post from Sisense Chief Strategy Officer Guy Levy-Yurista outlining major trends for the future of data analytics.