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Oracle’s Big Data Predictions for 2016
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Oracle’s Big Data Predictions for 2016 

For those of you who don’t know, big data, as its name suggests, are extremely large data sets which can be analyzed computationally to reveal patterns, trends and associations, especially related to human behavior and interactions. Convenient, isn’t it? So without further ado, here are Oracle’s top 10 predictions on what we should look for next year:

1.       Data civilians operate more and more like data scientists. Although complex statistics might still be limited to data scientists, data-driven decision making won’t. Next year, the discovery of simpler big data tools will let business analysts shop for data sets in enterprise Hadoop cluster, a unique type of computational cluster specifically designed for storing and analyzing unstructured data. Shoppers will be given the opportunity to reshape these clusters into new mashup combinations, and even analyze them with exploratory machine learning techniques. Extending this type of exploration to a broader audience will improve self-service access to big data and provide richer hypotheses and experiments that’ll take innovation to the next level. 

2.       Experimental data labs take off. With more hypotheses to investigate, professional data scientists will see increasing demand for their skills from established companies. For example, banks, insurers, and credit-rating firms will turn to algorithms to price risk and guard against fraud more effectively. However, it’s hard to shift many of these decisions from clever judgements to clear rulers. As firms try to identify hotspots for algorithmic advantage faster than competition, we will see a rapid increase in policy underwriting, fraud detection, and experiments where companies are unable to meet the required payments on their debt obligations. 

3.       DIY gives way to solutions. Early big data adapters had no choice but to build their own big data clusters and environments. But building, managing and maintaining these unique systems built on Hadoop, Spark, and other emerging technologies is costly and time-consuming. In fact, the average building time is six months. Who can wait that long? In 2016, we’ll see technologies mature and become more mainstream thanks to cloud services and appliances with pre-configured automation and standardization.

4.       Data virtualization becomes a reality. Companies won’t just capture a greater variety of data, but use it in a greater variety of algorithms, analytics, and apps. But developers and analysts shouldn’t have to know which data is where or settle with just the access methods that a central location for data storage and management can support. In 2016, we can expect a shifting focus from using any single technology, such as NoSQL, Hadoop, relational, spatial or graph, to increasing reliance on data virtualization. Users and applications connect to virtualized data, via SQL, REST and scripting languages. Successful data virtualization technology will offer performance equal to that of native methods with complete backward compatibility and security.

5.       Dataflow programming opens the floodgates. Initial waves of big data adoption focused on hand coded data processing, but those days will be gone. New management tools will separate and protect the big data foundation technologies from higher level data processing needs. Furthermore, we’ll see the emergence of dataflow programming which takes advantage of extreme similarity, provides simpler reusability of functional operators, and gives pluggable support for statistical and machine learning functions.

6.       Big data gives AI something to think about. 2016 will be the year where Artificial Intelligence (AI) technologies, such as Machine Learning (ML), Natural Language Processing (NLP) and Property Graphs (PG) are applied to ordinary data processing challenges.  While ML, NLP and PG have already been accessible as API (Application Programming Interface) libraries in big data, the new shift will include widespread applications of these technologies in IT tools that support applications, real-time analytics and data science.

7.       Data swamps try provenance to clear things up. Data lineage used to be a nice-to-have capability because so much of the data feeding corporate dashboards came from trusted data warehouses. But in the big data era, knowing your data’s birthplace is a must-have because customers are mashing up company data with third-party data sets. While some of these new combinations will incorporate high-quality, vendor-verified data, others will use data that’s not completely perfect, but acceptable for prototyping. When surprisingly valuable findings come from these opportunistic explorations, managers will look to the lineage to know how much work is required to raise it to production-quality levels.

8.       IoT + Cloud = Big Data Killer App. Big data cloud services are the behind-the-scenes magic of the internet of things (IoT). Expanding cloud services will catch sensor data and feed it into big data analytics and algorithms for further use. Highly secure IoT cloud services will also help manufacturers create new products that safely take action on the analyzed data without human intervention.

9.       Data politics drives hybrid cloud. It’ll be easier for governments to enforce national data policies if they know where data comes from, not just from what sensor or system, but from within which nation’s borders. In an integrated environment which uses a mixture of private and public cloud, Multinational corporations moving to it will be caught between competing interests. There will be an increasing number of global companies moving to hybrid cloud deployments with machines in regional data centers. Those machines will act like a local wisp of a larger cloud service to kill two birds with one stone, the birds being cost reduction and following the regulations.

10.   New security classification systems balance protection with access. Increasing consumer awareness of the ways data can be collected, shared, stored—and stolen—will heighten the calls for regulatory protections of personal information. Expect to see politicians, academics and columnists grappling with boundaries and ethics. Companies will increase use of classification systems that categorize documents and data into groups with pre-defined policies for access, redaction and masking. The continuous threat of ever more sophisticated hackers will prompt companies to both tighten security, as well as audit access and use of data.

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