Data Challenges

We can help businesses solve a wide variety of data issues. Below are some common scenarios that we run into.

Problem: Our critical business data is stored in a mainframe.
Solution: Data architecture modernization Without a doubt, the highest requested service is system modernization. If you are ready to move your data off of a mainframe, you have some options. The obvious approach is to transition to a relational system. With the recent advances in database technology, that may not be the best approach. We can help you survey the landscape and select the proper solution.

Problem: Executives need faster access to historical data.
Solution: Hybrid Transactional Analytic Processing (HTAP) In a world where data can be used to gain a competitive advantage, having fast access to historical data is critical for decision making. Advancements in database technology have now made it possible for your data warehouse to be updated in near real time. We can build a solution that will allow your executives to have access to data the moment it is generated.

Problem: Our warehouse ETL processes are breaking our SLAs.
Solution: Offload ETL processing into a distributed system Many organizations are processing so much data that they do not have the computing power to finish loading a data warehouse in a respectable amount of time. This becomes a challenge if something breaks and you have to rerun your load. While you can optimize your ETL load, the modern approach is to add more processing power by distributing the load over many machines. This can be done using distributed computing software. The specific software solution used will depend on your particular use case.

Problem: We have data located in many different data sources.
Solution: Virtualization The holy grail of historical analytics is a single system of record. This is difficult to achieve in mature environments when you have many different systems and data sources. With virtualization technology, it is possible to make data stored in various servers appear as one data source.

Problem: Our total cost of ownership for data is prohibitively high
Solution: Store data in Hadoop Storing data in a Hadoop cluster can significantly bring down the total cost of ownership of your data. Data that you otherwise would put in cold storage can now be made available for analysis in a cost effective manner. Managing a Hadoop cluster can be a challenging endeavor. It requires a team with a very specific skill set. The best strategy is to hire a knowledgeable resource, then place the cluster in the cloud. Outsource the administrative work while your resource focuses on extracting value from the cluster.