The advent of Big Data brought about the enormous opportunity for businesses to capture a significant market that they compete in. Even startups were able to compete at the level of enterprise companies across different industries. For more than 10 years, all the tools created to manage large amounts of data breached the maturity curve and organizations had to deal with the following challenges.
- Proliferation of tooling across the data lifecycle
- Clear ownership of data domain
- Time to extract insight on data
- Data sharing
- Poor customer experience
- Complex operations management
Snowflake clearly solved the problem of data sharing and complex operations management. One can perform few clicks to create a Datawarehouse in the cloud and it doesn’t take a lot of resource to share data, internally or externally. Data Mesh established the standard for clear ownership of domain data across different business departments. The rest of the challenges still linger till date.
There’s another wave of opportunity for businesses in the next three years. It’s the digital economy. By 2028, the market opportunity will total to $16.5 T. Compare that to less than a fifth from last year. Most of this growth is going to be driven by Real-time Data and AI workload while Analytics workload will also represent a decent chunk. With this, a lot of organizations are not ready to capture a slice of the digital economy in the next three years. For those who have started the journey of capitalizing on real-time data, their revenue growth increased to 62% and generated a higher profit margin of 97%. The closer the business is to generating instant insights, and the more of their customers are able to buy their product or services at their whim, the more business they generate. It’s that simple.
For real-time data processing to be effective and efficient, it has to happen across all steps in the data lifecycle. When data changes, it takes a long time to reflect it from collection to consumption stage. It's also very expensive to apply the changes. Also, by the time net new data arrives to consumption stage, it’s already irrelevant to the customer.
Fig. 1 – Data Lifecycle
The integrity of the data must be kept intact from stage to stage so that business rules and metrics are correct, accurate and consistent. Over the years, the workloads have become more complex and current tooling can no longer support it. Which slows down business decision-making and impacts severely on customer experience. Instead of innovating to generate more revenue and profit, budgets are used for maintenance purposes.
Tacnode was created to alleviate these challenges. It provides a real-time data lakehouse solution where changes to data are reflected immediately without affecting performance and scalability. Organizations that require real-time data access and complex AI workloads will no longer have to use multiple products to support it. They are guaranteed to have fresh data at their fingertips, always. Users can trust that data they are querying is always consistent and correct. Tacnode is cloud-native and it's a self-tuning, self-healing SaaS solution thereby allowing organizations to innovate and move with the exponential growth of the digital economy in the next three years.
For more information about Tacnode, please visit www.tacnode.io. You can also book a demo here. Tacnode CEO, Xiaowei Jiang, will also be presenting at Postgres Global Conference on March 19, 2025, which you can see here. If you are attending this conference, we invite you to go to attend this session.