Data analytics is now a common aspect of business practice across an array of industries, from health care to finance to marketing and beyond. The practice of data analytics, in its most general sense, involves the collection of vast amounts of data that is then scrutinized to derive actionable insights about an organization. Analytical queries are used to collect and collate data that then becomes actionable intelligence that informs business decisions.
Much like technological innovation in general, the practice of data analytics continues to evolve and improve, particularly in accordance with the debut of new digital platforms. As we head into a new decade, there are several different data analytics trends to look out for.
1. Sharing data
Sharing data will become simpler and easier as society continues to connect more devices to the cloud. This is exemplified by the internet of things (IoT): the network of interconnected objects that can function in tandem and share data near instantaneously between one another. Both consumer and business use of the IoT has been growing exponentially in the past decade. Various organizations are making it their goal to expand the capability of their IoT devices, making information sharing a standardized data management practice across platforms and services.
The ability to share business-related and personal data between providers and services with ease will streamline the role of analytics. As artificial intelligence and machine learning come on to the scene, further avenues will be opened for the exploration of data and analytics solutions. For instance, technologies like natural language processing can be used to collate large amounts of verbal data and share it with relevant parties, in a process that is exponentially quicker than typical recording practices. Both new technologies and an increase in the number of devices and organizations sharing data points contribute to the building of these “sharing networks.”
2. The cloud will become king
The use of the cloud continues to rise in popularity: A growing number of organizations, from large corporations to small businesses, are moving more of their data solutions to the cloud, as opposed to using data centers featuring equipment at the office. The flexibility and accessibility of the cloud, along with the savings, is driving this shift in data analytics practice. The IoT is enabling data to be shared between many different architectures and organizations through the cloud.
Of course, this level of interconnectedness brings with it a number of risks. The cloud and IoT are a security specialist’s nightmare, as any number of devices can be connected to these open-source networks to share information back and forth, including cheap consumer products that have poor security architecture. The more ways to share information and the more data that is shared, the bigger the risk of security breaches become. Data analytics professionals will have new ways to do their jobs and a record amount of information at their disposal, but will need to be aware of the ever-present risk of hacking.
3. A push for immediate results
The practice of business analytics involves mining data to eventually reach actionable insights and ideas that can then be implemented to improve a company’s strategy. This process is often complicated by the fact that data needs to be collected, compiled into reports and then scrutinized for insight. As a result, there will likely be a growing demand for analytics platforms that can not only mine for data but also simultaneously deliver immediate results that can be acted upon.
The cloud and the IoT make this push for data-driven, fast results possible. The internet allows information to be shared and collated in near-real time at the push of a button, from multiple disparate sources of information, no matter the physical location of each. With industrial automation taking off, businesses will be looking to bring these same efficiency gains to their white-collar professionals. As each and every organization making use of the cloud reaps the benefits of this information sharing, companies will be seeking even the tiniest levels of advantage, and this will manifest itself through the ever-increasing urgency placed on big data analytics professionals to make sense of data and draft solutions to a problem in as short a time as possible.
4. More data
Perhaps one of the most obvious big-data trends that can be expected to continue, according to Forbes contributor Bernard Marr, is that the amount of available data across the world will continue to rise at a remarkable rate. This is because of the development and proliferation of new digital platforms and the ubiquity of the cloud and IoT in modern society. Both consumer and business devices share a wealth of data between each other, and analysts will be able to make use of this new treasure trove to gain actionable intelligence on various operations. The rise of smart devices has made this possible—the vast majority of the US population, for instance, has access to smartphones, and these can be installed with numerous applications and connected to the internet to share important information with analysts.
5. Historical data
Organizations will likely begin to incorporate what is known as “dark data” into their analytics practices. Dark data is generally older information that is stored in hard-copy format. It can take the form of paper, videos, photos and other documents. Every single data point in these collections will likely become useful to companies because they can be used to gain insight on past company performance, ideas and business practices. In contrast to the previous trends, this practice cannot benefit from the cloud—these stacks of old and obsolete data can be very useful, but they must be introduced into an organization’s modern systems manually. Additionally, this practice involves resolving various conflicts in how information is categorized and measured that occur between the old and current sets of information. While time consuming and tedious, the implementation of historical data will give analysts even greater insights into various aspects of their organization’s operations.
6. A push for privacy
Marr predicts that there will be a growing push from governments to regulate big-data privacy and punish any violations of IT privacy mandates. With the increasing amount of public concern and discussion regarding the security of personal data in the age of information, both companies and governmental organizations have taken steps to improve the privacy measures in place to prevent the unauthorized and malicious use of information. New devices are being made with security in mind, with more advanced architecture being a priority in the design process.
Meanwhile, certain governments, like the state of California and the EU with its GDPR standards, are implementing new legislation to define the measures that must be taken by technology manufacturers to protect the information that is entrusted to them. Data scientists should be cognizant of this trend, as not only will they need to jump through more hoops to access the information they need, but they could also run afoul of local laws in the event they misuse the data at their disposal.
7. More qualified analysts
As the field of data analysis continues to grow, the demand for qualified professionals will rise. The Bureau of Labor Statistics states that the job outlook for analysts until 2028 is expected to grow at a rate of 20%, which is much faster than the average business occupation. In response to the growing number of jobs, it is becoming easier to get trained in the practice of analytics, thanks to online tutorials. It is possible to take online classes in analytics programs such as R, Python and SAS. Furthermore, online programs at the master’s level now offer training and qualifications in this area. The use of information in the course of conducting business is reaching a point where it may soon result in a severe deficit in qualified analysts. Prospective data scientists might be interested in business intelligence and data analytics to secure a strong financial future.
8. The rise of personal platforms
Platforms that can offer digital personal assistance and other services using personal data will continue to grow in sophistication and proliferation, Marr predicts. He explained that robotic technology will continue to become more widely used, as will inventions that seemingly belong in the pages of sci-fi books, such as self-driving vehicles. These high-tech platforms present the opportunity for even more information to be shared, many of which will be novel and useful to the average analyst.
9. The need to demonstrate ROI
While data analytics continues to be used widely across companies and organizations worldwide, a growing number of CEOs, board members and company executives will likely begin pushing to see a tangible return on investment for their analytics programs and strategies. Therefore, it is important for data analytics professionals to develop ways of showing ROI to company executives to ensure the programs continue in earnest. Companies want to know they are getting their money’s worth from investing in analytics, and each minute increase in efficiency will be squeezed out of current manpower and technology to get a leg up on the competition. As big data becomes a more pervasive force in the marketplace, organizations will be looking for proof that it is as useful as most say it is.
Consider Villanova University
If you are interested in shifting your professional goals and beginning a career in data analytics, consider applying to Villanova University’s online Master of Science in Analytics program. Designed to equip you with the knowledge and skills to succeed in this emerging business practice, and help you study at a time and pace that suits you, the online program is an ideal way to balance your professional and personal commitments with study. Learn more about Villanova University’s Master of Science in Analytics program.