Jefferson Kenji Takahashi

Jefferson Kenji Takahashi

Today’s businesses have more data than ever before, easily amassing terabytes–even petabytes–of information. It’s no wonder why data is hailed as the oil of today’s digital era.

But while most organizations understand that their data has the power to enable them to make better and faster business decisions and, consequently, boost their profitability, not all are able to derive true value from the overwhelming amount of data they gather on a daily basis. The fact is, businesses often have so much data that knowing where to begin is one of the greatest challenges of working with data.

To address this problem Primesphere offers an intelligent platform that accelerates company’s Digital Transformation journey through a series of automation services.

When I started the development of the Platform one of the things that I kept in mind was all the fuzz about big data, data analytics and others that were out there. I wanted to make sure to provide to the market a portfolio of products and services to automate usually inefficient, repetitive and error prone tasks but at same time provide valuable statistics information to the users.

Reduce application complexity in a sea of data turns out to be a big challenge. 

To the rescue the Platform combines machine learning with human intelligence to make it easy to explore disparate data.

A set of back-end services are triggered by workflow automation of connected systems based upon the data, or a human can be alerted to make a judgement call.

The platform radically reduces the need to spend valuable time doing data preparation. Instead, users can begin to ask ad-hoc questions within minutes of connecting to their data sources. Moreover, machine learning proactively reveals otherwise obscured patterns, anomalies, and relationships across all of the connected data.

To truly become a data driven enterprise, a business must do more than just collect data; they need to become a mathematical corporation. The business must also be able to explore, analyze, and react to high-velocity data changes in real time. But conventional methods of doing this are costly and time consuming.

As Internet of Things data becomes more prevalent, the ability to keep up with rapidly evolving data becomes critical.

Conventional methods of working with disparate data tend to require an investment in data pipelining, which is not inexpensive. Then there is the need to invest in ETL tools (Extract -> Transform -> Load) and processes to get all of the data to share a similar structure.

Beyond that, there is the costs of the tools for analytics and visualizations, and of course someone is going to have to do all of the work. Because working with data is complicated, that could mean hiring one or more Data Analysts or working with outside consultants.

Add all of that together, and you can understand why some businesses are unable or unwilling to allocate the budget needed to get started, or decide that it’s too much of a headache to deal with it.

To do what Primesphere does as a single comprehensive solution, you would likely have to invest in series of tools from different vendors.

Instead of using an ETL and a datawarehouse and conforming all of your data to a common format, Primesphere platform instead uses machine learning to examine connected data to understand what it means. It doesn’t force any schema on the user, so feel free to mix and match any group of data sources, including real-time data.