The final group of Big Problem Challenge winners are all focused on taming the sprawl of enterprise data.
In Challenge #2 of the 2023 Big50 Hot Startup Competition, 42 startups participated, and 20 passed this test. For the startups that didn’t move on in this round, the main issue was either 1) they never detailed the problem, instead jumping right ahead to the solution, or 2) their description of the problem was confusing, unbelievable, or both.
The 20 startups that passed the test all clearly detailed the problem they solve in terms that non-experts can understand. They illustrated the scope of the problem, and they gave evidence for why the problem they target will continue to get worse if it’s not addressed.
Business have trouble understanding the behavioral data behind users’ digital experiences and then translating that into data-driven decisions to drive business growth.
Data-driven transformation is a necessity in a rapidly changing digital world. Yet, as much as 57% of marketers misinterpret data, hence making wrong decisions. Modern analytics require a continuously iterative process: checking the results, drawing conclusions, and testing the implementation – a process businesses still struggle to master.
More than 23,000 SaaS apps are in use around the globe and, on average, more than 212 are deployed in the typical enterprise. With the proliferation of SaaS application usage today, enterprises need to figure out better ways to backup and restore SaaS application data in the event of a cyberattack, disaster, or outage.
With more than 52% of successful ransomware attacks coming through SaaS usage, SaaS application providers and enterprises must find ways to break down data silos and provide enterprise-class data protection for multi-cloud and SaaS IT environments.
Businesses struggle to capitalize on its most valuable asset: customer data. Data is often misaligned, with department-specific apps holding different versions of the truth. The effort required to create and maintain a shared view of customer metrics is an expensive drain on resources, and even if you can create it, data metrics and signals tend not to make it to the right people in time.
The skills required to execute an effective customer data strategy are technical skills that those closest to the meaning and value of the data (e.g. the sales, marketing, and customer success teams) do not have.
Organizations want more value from their data, but that data is growing rapidly and coming from increasingly diverse and dispersed sources. Thus, getting data to where it’s needed is often a slow, expensive, complex, and even impossible process.
Data locality becomes a challenge when organizations, global enterprises, and agencies have data located in geographically dispersed locations, such as data centers, remote offices, on a ship, or even far out in the field. That data needs to be moved great distances across edge, on-premises, and cloud environments to be consumed by users and applications. This often results in stale data.
