10 Big Data Trends to Watch in 2019

There are possibly endless potentials in the application of big data, especially for businesses. With big data, businesses can gain insights into developing trends and tailor their operations towards these trends and make maximum gains. Technological developments are thus underway to ensure that maximum benefits are attained from big data. Because of the ever-evolving nature of technological improvements as regards big data, trends in the sphere of big data develop continuously. Professionals and organizations are better poised towards reaping the benefits of big data when they are aware of happenings in the world of big data and how it applies to their operations.

Here are some of the developing big data trends of 2019.

IoT devices: The Internet of things, although not a new trend, has a lot of potential for growth, especially as regards big data. IoT devices are devices that automate a variety of activities, from turning bulbs on in private space to routine office activities.



IoT devices depend on the Internet and are run by platforms such as Google Assistant. IoT devices have been widely accepted across different settings. It is not uncommon to find private and workspaces where a lot of activities can be controlled from a smartphone.



The acceptance of IoT devices has encouraged organizations to invest in IoT devices for big data collection and analysis. When the application of big data was initially introduced, it seemed like it was out of the reach of smaller organizations because of the financial implications. Over time, the application of big data became more accessible to smaller organizations.



The development of IoT devices adapted for the application of big data trend will be in a bid to ensure that big data is applied across the different scales. These IoT devices will be aimed at collecting as well as the processing and analyzing big data, improving the applicability of big data.

Devices depend on the Internet and are run by platforms such as Google Assistant. IoT devices have been widely accepted across different settings. It is not uncommon to find private and workspaces where a lot of activities can be controlled from a smartphone.



The acceptance of IoT devices has encouraged organizations to invest in IoT devices for big data collection and analysis. When the application of big data was initially introduced, it seemed like it was out of the reach of smaller organizations because of the financial implications. Over time, the application of big data became more accessible to smaller organizations.



The development of IoT devices adapted for the application of big data trend will be in a bid to ensure that big data is applied across the different scales. These IoT devices will be aimed at collecting as well as the processing and analyzing big data, improving the applicability of big data.

Data management: Big data management is a critical step in its application. Before data can be collected from a host of sources and properly analyzed to recognize developing trends, methods, and skills for managing big data must be developed. The management of big data requires a specific skill set which, unfortunately, is not commonplace.



Thus, a trend that is bound to be seen in the big data sphere in 2019 is the difficulty in big data management. The different step involved in collecting, cleaning, processing, and analyzing big data requires particular skillset. Because these skillsets are not readily available, the application of big data is bound to be limited.



Apart from the fact that the required skill set for proper application of big data has not been acquired by a lot of professionals in the tech space, there is also the limitation of the time and finances needed for a full application of big data, especially at larger scales.



Thus, professionals in the big data space are tasked with tackling the difficulty in data management in 2019. This may require training of more professionals on the required skills as well as the development of cost-effective and sustainable ways of applying big data. There is also bound to more emphasized need for methods of scaling the applications of big data.

Artificial Intelligence: Artificial intelligence is especially needed for the maximization of the benefits of big data. AI systems provide the efficiency that is needed for the maximal application of big data. Thus, a step towards full maximization of big data will be the availability of more AI devices.



The availability of these devices will particularly improve the scalability of applications of big data- a factor that is critical in reaping the most benefits from big data. Big and small businesses run more efficiently with AI devices.



These learned machines perform activities that would take humans a lot more time to carry out, thus saving organizations money and time. With the deployment of AI devices, organizations stand to gain more benefits from the application of big data. These benefits stem from the fact that the machines will be trained to carry out activities required for the application of big data highly effective.



It has been established that activities involved in the application of big data take a lot of time, especially when carried out by humans. With AI systems, organizations will be better poised towards gaining insights from available data.

Data storage: An aspect of big data where trends are bound to develop is storage. Big data, just like the name spells out, involves a lot of data. The application of big data requires the collection then analysis of an enormous quantity of data.



After data are collected, it must be stored before analysis. Also, data could be stored after analysis. Data storage is thus essential in the application of big data. It has been proposed that a single storage system should be applied for proper compartmentalization of data.



However, because of the changing nature of data, a single storage system is not feasible. As a result, data silos for the storage of big data are bound to expand. In the large-scale application of big data, new silos will be created, and these silos will be maintained. With no alternative method for the storage of data insight, a big data trend for 2019 will be the expanding nature of data silos.



The storage of big data in silos is bound to continue until other appropriate methods are developed. The methods of storage of unstructured data currently available are eventually categorized as big data.

Predictive analysis: Predictive analysis is one of the major applications of big data for business. Big data is analyzed to get insight into future happenings. Changes happen regularly across different sectors. Organizations that are prepared for changes when they happen achieve maximum gains from the changes.



The changes that can affect the performance of organizations include consumer behavior changes. Big data analysis can provide insights on changes in consumer behavior and guide organizations accordingly. A lot of organizations have become aware of the insight they can gain from the analysis of big data and are implementing measures for the application of big data. In 2019, more organizations are bound to leverage the available insights from big data.



More organizations will apply tools for the analysis of big data for insights on possible market changes. Tools already exist for applying big data for predictive analysis. There is bound to be the development of more specific tools tailored towards the application of big data. The application of big data for predictive analysis gives organizations “inside” information about future happenings.



Big data analytics could also provide context on the predicted changes. The importance of predictive analysis for strategic planning will fuel its application across more organizations and sectors. There is bound to be an increase in big data in the predictive analysis because of the attached benefits.

Data regulations: The value of data has increased significantly over the years. As a result, data has become a highly sensitive material, and regulations have become necessary to ensure the protection and proper use of data. The EU has also already implemented regulations for the protection of data.



The GDPR enacted by the EU spells out important consequences for improper data governance as determined by the guidelines. Although such guidelines do not apply across the entirety of the US as the existing regulations are still limited, there is bound to be more data regulations in the coming times. It is noteworthy that the GDPR applies to organizations with relations with the EU.



Because of the sensitive nature of data, organizations are tasked with ensuring methods of securing data. Even though the regulations on data governance are still limited, there is bound to be the widespread application of heavy fines on improper data governance. This developing trend alerts organizations of the fact that they need to put their house in order as regards data governance. The earlier organizations implement measure towards the proper management of data, the better prepared they are for the changing climes.

Data migration: Even with the widespread application of big data, insights that can be gotten from big data is still quite limited to the data that has been digitized. A lot of data still exist that have not been stored on the cloud. Thus, a trend that is bound to develop in the big data sphere is the migration of data to the data.



When more data is migrated and uploaded to the cloud, the predictive analysis will be applied more effectively to businesses. It is, however, notable that the migration of more data will lead to natural consequences such as the expansion of data silos. Data migration is necessary for attaining the most benefits from big data. Thus, more attention will be paid to the migration of data to benefit from untapped data.

Big data professionals: Big data professionals are bound to expand their career opportunities because of the evolving role of big data in organizations. Because there is a need for skilled individuals to develop and run the systems that are necessary for maximizing big data, persons with the needed skills will find expanding career opportunities.

For AI systems, for example, the systems that have received a high level of acceptance are developed by persons with the necessary skills. As the application of big data expands, there will be a need for professionals with specific skill sets to develop and run the required tools and systems. Organizations will also implement more systems for ensuring proper data governance and will require professionals to run as well as develop these systems.



Big data language skills are thus bound to be in high demand.Within organizations, persons with positions related to data management will also play a more critical role in the success of the organization. The chief data operation officer, for example, will be more involved in strategic planning activities, for example. More positions would also become more available for persons in the big data sphere in areas such as data marketing.

Cybersecurity: Big data has applications in cybersecurity, and this application will be more evident in the coming times. Big data analytics can provide insight into cybersecurity. Trends can be detected based on the analysis of data on security threats and the development of possible threats and modes of operation.



Big data can be applied to ensuring the security of sensitive materials within organizations. More organizations are experiencing security threats and seeking ways of preventing attacks in the future. It has been established that big data can be applied to gain insight into the pattern of security threats as well as possible flag threats.

A big data trend for 2019 is thus the integration of big data in cybersecurity strategies. More organizations will rely on big data to prevent trends. Tools have been developed to achieve this integration of big data in cybersecuritystrategies. These tools which are already being applied in sectors such as the financial sector will support receiver applications in the year possibly in different scales. There is thus a high indication of improved collaboration between cybersecurity experts and big data experts.

Deep learning: Even with the available applications of big data, it could be said that experts are yet to scratch the surface, and there has been an exploration of deep learning frameworks to investigate the more enhanced application of big data.



Deep learning frameworks have bound to receive more attention in the coming times, include TensorFlow and MXnet. These frameworks are strategic to the more effective application of big data in areas such as fraud management. Experts are thus dedicated to understanding these frameworks better in the recent past. A trend that is bound to continue in the nearest future.

Finally

Organizations are seeking more effective approaches in applying big data. From all indications, experts are also committed to finding the new approaches towards a more effective application of big data as well as wider applications of deep learning.

The applications of big data are bound to expand in the nearest future. With this expansions, trend such as those highlighted above is bound to be established. These trends indicate the growth in specific aspects of the big data sphere that professionals, individuals, and organizations should be prepared to receive.