Top analytics tools in 2016
Data analysis is not cut and dried, providing results in absolute terms. Rather, many tools, techniques and processes can help dissect data, structuring it into actionable insights. As we look toward the future of data analytics, we can expect certain trends in tools and technologies to dominate the analytics space:
- Data analysis frameworks
- Visualization frameworks
- Model deployment frameworks
Data analysis frameworks
Open-source frameworks such as R, with its increasingly mature ecosystem, and Python, with its pandas and scikit-learn libraries, seem poised to continue their dominance of the analytics space. In particular, certain projects in the Python ecosystem seem ripe for quick adoption:
Modern data scientists work with myriad data sources, ranging from CSV files and SQL databases to Apache Hadoop clusters. The blaze expression engine helps data scientists use a consistent API to work with a full range of data sources, lightening the cognitive load required by use of varied frameworks.
By providing the ability to do processing on disk rather than in memory, this interesting projects aims to find a middle ground between using Hadoop for cluster processing and using local machines for in-memory computations, thereby providing a ready solution when data size is too small to require a Hadoop cluster but not so small as to be handled within memory.
Building on a general trend of using open-source ecosystems, we can also expect to see a move toward distribution-based approaches. Anaconda, for example, offers distributions for both Python and R, and Canopy offers a Python distribution geared toward data science. And no one will be surprised if we see the integration of analytics software such as R or Python in a standard database.
Beyond open-source frameworks, a growing body of tools is helping business users interact directly with data while helping them produce guided data analysis. Tools such as IBM Watson, for example, attempt to abstract the data science process away from the user. Although such an approach is still in its infancy, it offers what appears to be a very promising framework for data analysis.
Offering APIs in Python, R and Matlab, this data visualization tool has been making a name for itself and seems on track for increasingly broad adoption.
This library may be exclusive to Python, but it also offers a strong potential for rapid future adoption.
Model deployment frameworks
Many service providers are willing to replicate the SaaS model on premises, notably the following:
- Domino Data Labs
What’s more, in addition to needing to deploy models, we’re also seeing a growing need to document code. Accordingly, we might expect to see a version control system similar to Github but that is geared toward data science, offering the ability to track different versions of data sets.
Going forward, we anticipate that data and analytics tools will see increased implementation in mainstream business processes, and we expect such use to guide organizations toward a data-driven approach to decision making. For now, keep your eye on the foregoing tools—you won’t want to miss seeing how they reshape the world of data.
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