With the arrival of the new year, comes new penchants for learning, growth, and improvement. Much the same, the data industry must put behind outdated methods and look forward to bringing in new strategies and objectives. Just over the last decade, data science has drastically evolved to meet the growing demand for data-driven business outcomes.
By 2025, we will be looking at 463 exabytes of data generated by people each day. In the ever-expanding technological, data-driven world of today, it is crucial to place focus on data and the adaptation of how data is gathered, stored, and utilized to propel results. Trends emerging in 2022 have the potential to accelerate machine learning projects and accelerate analytics usefulness. Here’s what experts predict the future for data analytics and science holds.
Large pools of data are greatly needed for network security monitoring tools, enterprise resource planning software, and machine learning models, among other uses. Companies have always gathered the data necessary for these tools, but data quality management has not always been prioritized. In 2021, organizations finally began to show care for the quality of their data and it is expected that this pressing need will be on the rise and a major focus for 2022 and ongoing years. But, as of now, many organizations do not feel that the quality of their data is up to par – that is, that the data is clean or usable. In fact, many enterprises report difficulty with attaining data integrity at scale. The forecast is that organizations will draw from third-party data and get rid of data silos to enhance their usable data.
It feels like only yesterday that the big thing in artificial intelligence data was big data analytics and tools to handle data sets that are large and complex. Given this, it would be reasonable to assume and assert that companies would be primarily interested in big data. However, this isn’t the case. Rather, companies are concerned with the volume and format of a dataset. They’re interested in volumes that are manageable and formats that are accessible, instructive, and workable.
Data, big or small, is grounded in volume, velocity, and variety. Issues surrounding big data - skills, budget, design principles, sources - are at their peak and may drive big data to its plateau. In 2022, small data sets may emerge as the next phase in a data strategy that shifts towards automation.
As more and more companies offer work-from-home, the limited network security infrastructure and visibility on home networks and personal devices poses a risk to security. Luckily, there are a few technologies and techniques designed for threat detection that can aid companies as they attempt to strengthen their remote worker security and reduce the risk of a cybersecurity breach.
Cyber criminals and hackers are adapting to the transition from in-office work to at-home work to target the average employee using their home equipment. Among the several techniques to combat these issues, two are gaining momentum: XDR and SOAR.
XDR stands for extended detection and response. Broken down even further, this means that XDR can detect threats in any location through the application of advanced analytics to endpoint the network data.
Humans and their emotions are at the heart of almost everything in the world. In all walks of life, the sentiments evoked from interactions and the emotions underlying our every move are representative of our actions and outcomes. As such, diving in data science and analytics means successfully understanding the human psyche.
It doesn’t matter how you utilize data science, advanced analytics, machine learning, artificial intelligence, cognitive computing, or even natural language processing; if you can’t integrate the tools of data science with the context of human decision making, they will fail. Given the irrational, biased, unpredictable nature of humans, this can pose an issue. The solution is to go beyond the design of data science and delve into the mind of your audience. Get to know them. For a successful roadmap to data science implementation, people-centered design is surely the way to go in order to be met with success in the future.
Natural language processing (NLP) helps bridge the gap between natural spoken language for a human and data from documents, spreadsheets, audio recordings, emails, so on and so forth. Another way to think of is to first consider that to derive valuable meaning from data, you have to be able to communicate with the systems and computer you are using. This can happen in two ways: 1) you can learn your computers language or, 2) your computer can learn english. NLP is the latter - rather than learning programming language, the computer understands you.
Experts predict that NLP will grow across industries this year, as various businesses uncover valuable applications for artificial intelligence technology. As using a phone microphone to search and text becomes increasingly popular, the ability for your organization to process and analyze the natural quality of speech becomes that much for . NLP creates a better user experience by providing relevant answers and improving productivity gains. NLP has been used extensively in consumer use cases and is now making its way into enterprise products. Low-code and no-code applications are beginning to be developed through natural language interfaces and this is just the start of what’s to come.
The use of AI technology has increased dramatically across industries – process automation, cybersecurity, and customer service to name a few. Traditionally, AI has taken a supportive role for legacy solutions. Never before has AI been used to replace workflows or campaign dashboards. However, this all may be changing in the near future. Experts have predicted that 2022 may see AI take over standby technologies in network monitoring.
Under this prediction, monitoring and troubleshooting processes in networks will be replaced largely by AI-driven assistants. For the years to come, you will have the capability of typing in a question and getting an answer or having issues flagged for you and in some cases, fixed through a process known as self-driving. The days of manual labor - hunting and pecking and looking at charts - are well in the past.
With organizations in need of new staff, and job-seekers in a position to pick-and-choose between several companies, it’s possible for candidates to demand higher salaries and better benefits. In light of this, experts foresee organization’s needing to offer higher salaries and benefits that support the growth, development, and personal health of the employee. This is true of technology workers in particular.
A 2021 tech salary report asked tech workers what benefits they look for in a job and compared that to what they were receiving. They found numerous gaps. There were gaps in training and education, stock programs, and flexible schedule options to name a few. These are areas where organizations should and will need to look into improving in order to maintain a competitive advantage. Smaller companies in particular will need to improve and increase their benefits if they hope to attain talented individuals.
Businesses are increasingly investing in cloud migration to meet goals anywhere from improving time-to-market to enhancing customer experience. As the cloud computing industry expands into new lines of business, it also shifts its identity to meet new corporate challenges and initiatives. In view of this, some experts believe 2022 will bring change by way of consolidation to the cloud security vendor landscape. This consolidation trend comes as a direct response to the large number of cloud security tools on the market, a great portion of which are mergers and acquisitions. Beyond consolidation, there are many trends that cloud security experts are seeing in store for 2022, the most significant by and large being cloud’s increase in popularity. If you haven’t invested in cloud, here are some reasons you might consider in 2022:
If you hope to meet goals, see favorable outcomes, and stay competitive in the coming year, it is in your best interest to stay-up-date on data science trends. Yesterday’s trends are history. Keep your focus on the up-and-coming. Once you’ve trained yourself on data science changes, you can integrate this into your understanding of your consumer to see favorable outcomes.