Are new New and Simpler AI Powered Video Editing Tools Foreshadowing a Parallel World to Come for Analytic Tools?
Capcut, Descript, Filmora, Canva and so many more new AI powered video and photo editing tools are challenging industry stalwarts like Adobe Premiere, Apple Final Cut Pro, and DaVinci Resolve. New video editing tools optimized for a mobile experience and infused with AI are radically simplifying use cases, making video and photo editing fun, and massively growing the user base. Users who feel Adobe and Apple's products are too complex are now adopting new tools in droves to drive their TikTok, YouTube, Instagram, Facebook, and other social media cravings and engagements.
Can this same democratization of video editing tools bleed over into the world of analytics?
Organizations who lean heavily on data teams, data scientists, and engineers for reports, dashboards, and insights will continue relying on those with deeper technical skills until they change how they see and evaluate analytics requirements.
There is so much inertia in the data pipeline and analytics tools market where IT teams stack requests for proposals (RPFs) against traditional buying criteria that includes a variety of tools from ETL, to MDM, to Governance, Security, Databases, Predictive, and Reporting Tools. Each requiring a deep set of skills, often coding knowledge in R, Python, SQL, and other scripts. No wonder, business users feel they are forever beholden to IT and data engineering teams.
But just as the video editing market has gone through a fundamental and innovative change, the analytics market is poised to be disrupted with simple and fun tools infused with A.I. that shake up the traditional IT buying agendas.
Enter eyko
At eyko, our vision is to unlock the power of analytics for all users by simplifying the entire data acquisition, transformation, and analysis steps into an intuitive, AI powered, and single integrated product.
We believe we are ushering in a new era of analytics that will drive a massive adoption of new users who can now get insights from all their data in minutes, without having deep technical skills or needing their IT teams to hold their hands.
In many sales cycles we come across IT leaders who are following all the other hoards of IT teams and landing all their data into a Data Lake. While this has been fantastic for Snowflake, DataBricks, and other data lake software revenue growth, it has just moved the analytics problem from operational systems to a sea of unnavigable data.
Users want insights and context, and with the proliferation of more data sources like never before, users can no longer wait for IT teams to merge, clean, and enrich their data. The pace of business is moving too fast and they need insights and answers to make the right decisions now – not when IT gets to it.
"Best of Breed" has led to fragmented data tools
eyko is challenging the current buying criteria in the data analytics market. Frankly, the criteria are out of date and don't acknowledge the massive shift happening right now. We fundamentally believe users can stop buying discrete tools for each step in the data journey and consolidate the entire process into a single solution that is great at delivering insights from all data sources.
How is eyko changing the game?
We've written about eyko in other articles but simply put, eyko makes it easy for you to acquired data from to multiple systems, merge the data, enrich it, and start answering questions and getting insights in minutes, at scale, without having deep IT and data skills.
Simplicity will win!
With the rise of cloud, in-memory, AI, and better design, eyko feels we can disrupt the dominance of traditional tools. But, that is not our objective. We want to delight business users and enable them to get insights from all their data in minutes. By focusing on simplicity, we will win the users.
You can learn more about eyko's disruptive approach to data analytics in these articles:
- eyko's disruptive capabilities.
- Virtual Data Scientist at your fingertips.
- Virtual Data Warehouse to avoid traditional data warehouse failures.
