If you liked 2015 with its many changes in machine data analytics, you’ll love 2016. Many of the trends we follow can be tallied on the business side of data analytics. But this time, we cast our gaze at technical changes, especially event processing, data and information integration and new forms of analytics. Here’s our lineup of significant big data analytics tools and tech changes to look for this year:
Interest in Spark Continues to Grow
- In 2015, hundreds of companies and scores of vendors, like MapR and Hortonworks embraced the Apache Spark framework. Given Spark’s versatility, this trend will continue throughout 2016. Making sense of Big Data requires a lot more than SQL. Spark has proven itself a leader by combining in-memory performance with SQL, machine learning as well as graph, streaming, and R-based analysis capabilities.
Analysts attribute the growing interest in Spark to the growth of online business activities and use of many different data sources, which must be found, cleaned and analyzed before decisions ca be made. The combined predictive capabilities of R and Spark especially interest organization that want to make the right decisions and anticipate business outcomes with reasonable certainty.
Data Governance: Settings Customized for the Right Uses and Workgroups
The traditional method of data governance is no longer efficient. The secure, strict and highly centralized governance model is being replaced by a decentralized process with settings customized to different data uses and workgroups. So, look for many business intelligence platforms to provide different levels of settings and permissions that provide teams within a company with access to high-quality data.
Connections, Integration and Standardization
Although connector software doesn’t get much press, it’s the glue that ensures that everything works. In 2016, Look for:
- More API dev tools. As more and more software extends or replaces enterprise applications, the need for APIs that enable cloud connectivity will grow. Look for many, better developer tools.
- Firmer IoT standards. Although IoT has been around for years, standards around connected devices and event processing capabilities are beginning to solidify. Look for more apps and best practices this year.
- Event processing and analytics tools that are used together. The integration between event processing and analytics technologies and tools gets tighter than ever. Look for apps that bundle these capabilities.
Familiar Analytics Methods, Expanded and Refined
Very little analytics software is really new this year. Instead, developments are expanded, updated or refined approaches to existing apps. These include:
- Mature DevOps methods and tools. DevOps practitioners will use a new breed of next-generation log and machine data analytics services. These services run at cloud scale, use predictive algorithms and are integrated with DevOps applications. Look for dramatic improvements in continuous integration and deployment processes.
- Refined analytics methods. Look for continually improving methods designed to collect the most data from the best sources and transform it in to clean, useable information.
- More hard-core analytics apps. More predictive customer analytics and industrial-grade, real-time analytics apps will go commercial.
- Visual data discovery. Forward-looking data specialists often begin their analysis with visual data discovery. They use data visualization tools, such as Spotfire and Tableau, to discover unexpected relationships between data elements across many data sets for later data analysis.
- Exploration visual analytics These apps enable you to dig into Big Data. The tools: visualizations and best practices in visual perception exploration. This visualization method, which is based on experimentation, creativity and predefined questions, is often used ad hoc to check different alternatives.
- Data virtualization. Providing the right person with the right data at the right time is challenging and expensive. So, in 2016 companies will invest more in data integration. Data virtualization is a real-time method, which integrates any type of data from disparate, structured or unstructured data sources. Look for new tools that will connect to each dataset and combine, blend, or join with more flexible tools and methods.
- Graph databases and graph analytics. In 2016 more and more people will decide to represent and store data in the form of graph databases, and process it via graph analytics. This approach helps us analyze unstructured data and look for trends rather than for an answer to a specific question.
- Bootstrapping is the process of building a complex analysis from a simple starting point. The method takes samples from the same dataset over and over again. The goal: estimate how accurate your estimates of the entire data set are. The analysis is gradual and becomes more refined with every step as the model compares samples and ‘learns’ from the earlier data samples.
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Shikha Kashyap
Chief Technology Officer
About Shikha: Shikha is a tech leader with deep expertise in emerging technologies such as Big Data analytics using MapR, Hortonworks, Tableau, and Spotfire. Her experience includes working with Fortune 500 companies, implementing solution design, architecting, and project managing. Shikha leads Technology for Syntelli and is passionate about non-profit causes and giving back to the community.
Connect with Shikha on LinkedIn: [social_list linkedin_url=”https://www.linkedin.com/in/shikhabkashyap”]
