“Breaking down the data silos” was a hot topic at Strata in NYC this year. Our Simba team polled attendees and revealed that data access was by far the biggest frustration for many, especially when it comes to reporting.

Today, companies are seeking to access an ever-expanding volume of valuable data from more sources, which makes reporting more complex. It requires pulling raw data to get KPIs and metrics from a myriad of ERP and CRM systems and sales platforms. You also need access to both historical and real-time data for analytics and dashboards to derive meaningful insights.

Choosing the right solution to extract the contents of your data source in whatever form your application needs is essential for gaining a holistic view of data. Working with specialized data connectivity experts is your best bet for guidance in picking the right data connectivity option, whether it’s an off-the-shelf connector or a custom solution.

Here are three takeaways from my participation in Strata – heavily influenced by peer discussions:

Increasing adoption of Apache Kafka – With the growing popularity of Apache Kafka, data scientists and engineers are dealing with more and more streaming data. Kafka is growing in popularity for use in building distributed applications and powering web-scale Internet companies. Due to its high-performance characteristics and its scalability, we’re seeing more companies use Kafka as a way to ingest and move large amounts of data very quickly. As such, having a standardized way for accessing streaming data with JDBC and ODBC drivers should be a consideration.

Driving AI and Machine Learning value – Most companies are still trying to figure out how to get value from their AI and machine learning (ML) applications and tools. It all starts with data. Data access, collection and quality are fundamental elements in driving the success of any AI and ML system. This affects the development, performance and accuracy of results. Regardless of the source, data preparation is key for input into AI and ML training models and systems. Data preparation can be an extensive process that involves aggregations, deduplications, reformatting and feature extraction. Having standards-based connectivity solutions is the foundation for managing access to live data to achieve process automation.

Keeping pace with Big Data access – Big Data is changing quickly. Today, companies need to access and analyze data coming from social media to understand how their customers and prospects are thinking about their brands. Simba is helping companies keep up with these trends and, for example, has built SQL capable connectors for data sources like LinkedIn, Facebook and Twitter to help brands make sense of digital advertising and marketing campaigns, and for analyzing user engagement. This is a progression of Simba’s partnerships with major Hadoop vendors as well as other large data providers like Google BigQuery, Amazon Redshift, Snowflake, enabling SQL capable ODBC and JDBC access, so users can use tools like Qlik, Power BI and Tableau to easily analyze their data.

For a deeper dive on our Simba data connectivity and analytics solutions, check out our resource library.