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Big Data: 2017 Major Trends
Over the past year, we’ve seen more and more organizations store, process and exploit their data. By 2017, systems that support a large amount of structured and unstructured data will continue to grow. The devices should enable data managers to ensure the governance and security of Big Data while giving end-users the possibility to self-analyze these data.
Here below the hot predictions for 2017.
The year of the Data Analyst – According to forecasts, the Data Analyst role is expected to grow by 20% this year. Job offers for this occupation have never been more numerous before. Similarly, the number of people qualified for these jobs is also higher than ever. In addition, more and more universities and other training organizations offer specialized courses and deliver diplomas and certifications.
Big Data becomes transparent and fast – It is obviously possible to implement machine learning and perform sentiment analysis on Hadoop, but what will be the performance of interactive SQL? After all SQL is one of powerful approach to access, analyze, and manipulate data in Hadoop. In 2017, the possibilities to accelerate Hadoop will multiply. This change has already begun, as evidenced by the adoption of high performance databases such as Exasol or MemSQL, storage technology such as Kudu, or other products enabling faster query execution.
The Big Data is no longer confined to Hadoop – In recent years, we have seen several technologies developing with the arrival of Big Data to cover the need to do analysis on Hadoop. But for companies with complex and heterogeneous environments, the answers to their questions are distributed across multiple sources ranging from simple file to data warehouses in the cloud, structured data stored in Hadoop or other systems. In 2017, customers will ask to analyze all their data. Platforms for data analytics will develop, while those specifically designed for Hadoop will not be deployable for all use cases and will be soon forgotten.
An asset for companies: The exploitation of data lakes – A data lake is similar to a huge tank, it means one needs to build a cluster to fill up the tank with data in order to use it for different purpose such as predictive analysis, machine learning, cyber security, etc. Until now only the filling of the lake mattered for organizations but in 2017 companies will be finding ways to use data gathered in their reservoirs to be more productive.
Internet of Objects + Cloud = the ideal application of Big Data – The magic of the Internet of Objects relies on Big Data cloud services. The expansion of these cloud services will allow to collect all the data from sensors but also to feed the analyzes and the algorithms that will exploit them. The highly secure IOT’s cloud services will also help manufacturers create new products that can safely act on the gathered data without human intervention.
The concentration of IoT, Cloud and Big Data generates new opportunities for self-service analysis – It seems that by 2017 all objects will be equipped with sensors that will send information back to the “mother server”. Data gathered from IoT is often heterogeneous and stored in multiple relational or non-relational systems, from Hadoop cluster to NoSQL databases. While innovations in storage and integrated services have accelerated the process of capturing information, accessing and understanding the data itself remains the final challenge. We’ll see a huge demand for analytical tools that connect natively and combine large varieties of data sources hosted in the cloud.
Data Variety is more important than Velocity or Volume – For Gartner Big Data is made of 3 V: Large Volume, Large Velocity, Large Variety of Data. Although these three Vs evolve, the Variety is the main driver of investment in Big Data. In 2017, analysis platforms will be evaluated based on their ability to provide a direct connection to the most valuable data from the data lake.
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Spark and Machine Learning makes Big Data undeniable – In a survey for Data Architect, IT managers and analysts, almost 70% of respondents favored Apache Spark compared to MapReduce, which is batch-oriented and does not lend itself to interactive applications or real time processing. These large processing capabilities on Big Data environments have evolved these platforms to intensive computational uses for Machine Learning, AI, and graph algorithms. Self-service software vendor’s capabilities will be judged on the way they will enable the data accessible to users, since opening the ML to the largest number will lead to the creation of more models and applications that will generate petabytes of data.
Self-service data preparation is becoming increasingly widespread as the end user begins to work in a Big Data framework – The rise of self-service analytical platforms has improved the accessibility of Hadoop to business users. But they still want to reduce the time and complexity of data preparation for analysis. Agile self-service data preparation tools not only enable Hadoop data to be prepared at source, but also make it accessible for faster and easier exploration. Companies specialized in data preparation tool for Big Data end-user, such as, Alteryx, Trifacta and Paxata are innovating and consistently reducing entry barriers for those who have not yet adopted Hadoop and will continue to gain ground in 2017.
Data management policies in hybrid cloud’s favor – Knowing where the data come from (not just which sensor or system, but from which country) will enable governments to implement more easily national data management policies. Multinationals using the cloud will face divergent interests. Increasingly, international companies will deploy hybrid clouds with servers located in regional datacenters as the local component of a wider cloud service to meet both cost reduction objectives and regulatory constraints.
New safety classification systems ensures a balance between protection and ease of access- Consumers are increasingly sensitive to the way data is collected, shared, stored – and sometimes stolen. An evolution that will push to more regulatory protection of personal information. Organizations will increasingly use classification systems that organize documents and data in different groups, each with predefined rules for access, drafting and masking. The constant threat posed by increasingly offensive hackers will encourage companies to increase security but also to monitor access and use of data.
With Big Data, artificial intelligence finds a new field of application – 2017 will be the year in which Artificial Intelligence (AI) technologies such as automatic learning, natural language recognition and property graphs will be used routinely to process data. If they were already accessible for Big Data via API libraries, we will gradually see the multiplication of these technologies in the IT tools that support applications, real-time analyzes and the scientific exploitation of data.
Big Data and big privacy – The Big Data will have to face immense challenges in the private sphere, in particular with the new regulations introduced by the European Union. Companies will be required to strengthen their confidentiality control procedures. Gartner predicts for 2018 that 50% of violations of a company’s ethical rules will be data-related.
Sources:
Top 10 Big Data Trends 2017 – Tableau
Big Data Industry Predictions for 2017 – Inside Bigdata