Data, data everywhere - how to use the most valuable bits
FYI, this story is more than a year old
Data, data everywhere but too hard to use it all. The complex nature of data and the sheer volume of it can overwhelm organisations, whether they know what they are looking for, or whether they don't.
It’s inevitable that some valuable data will be discarded as part of the process of filtering out what fits the most important criteria at any one time.
Ideally, all data and its value should be driving all product creation and business opportunities. But that requires quality data and the means to monetise it, as well as the data strategy to execute it.
A solid, foundational data strategy can incorporate data science platforms, which Gartner defines as the following: “a cohesive software application that offers a mixture of basic building blocks essential for creating all kinds of data science solution, and for incorporating those solutions into business processes, surrounding infrastructure and products.”*
Gartner goes on to add that, “a data science and machine learning platform supports various skilled data scientists in multiple tasks across the data and analytics pipeline. These range from data ingestion, data preparation, interactive exploration and visualisation and feature engineering to advanced modeling, testing and deployment.”
Data holds the secrets for new product opportunities and consumer trends; however the data strategy for any product creation requires reliable infrastructure software that supports data science and the insights that lie within.
Gartner analysed 17 of the most popular platforms as part of the 2019 Magic Quadrant for Data Science and Machine Learning Platforms. TIBCO Software, an independent provider of infrastructure software, improved its position on both the ability to execute and completeness of vision axes compared to the previous report.
"We believe that our success is thanks to the company’s acquisition of enterprise reporting and modern BI platform vendors (Jaspersoft and Spotfire), descriptive and predictive analytics platform vendors (Statistica and Alpine Data), and a streaming analytics vendor (StreamBase Systems)."
TIBCO has also kept its interoperability with open source environments – allowing open source code to be developed within the platform or in an outside environment and then integrated.
If you want to bring together powerful visualisation capabilities, strong descriptive analytics, and visionary predictive analytics in one platform, this evaluative report is a must read.
* Gartner Magic Quadrant for Data Science and Machine Learning Platforms, Carlie Idoine, et al, 28 January 2019
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