The global Big Data market size is growing year by year. Data-driven decisions have become standard for numerous industries around the world.
More than 60% of organizations are planning to increase data-related spending relative to 2021 levels in 2022 and beyond, according to the recent findings obtained by TechTarget's Enterprise Strategy Group division. The reasons are clear: Big Data helps to understand and anticipate customer needs, scan the hottest trends in social media, and analyze all kinds of external and internal threats to your company.
| Store Data→ | Process Data→ | Extract value for strong decision-making |
Following industry leaders, many companies seek Big Data and Analytics expertise to embrace the opportunities awaiting there. Everyone wants to know what is coming for the industry as well. Let’s disclose the trends in Big Data and look at the challenges that may dominate in the coming years.
The data volume is growing fast, and it looks like it won’t slow down in the coming years. Half of any technology company’s effort is dedicated to storing and managing Big Data.
It is no wonder that it can become a challenge for companies to maximize the potential of raw and unstructured data. It is hard to look for patterns and deduce meaning from large and voluminous data that keeps piling up.
It is no wonder that one of the first and foremost Big Data industry trends is the growing use of Machine Learning algorithms. Big Data and Machine Learning are forecast to expand their reach to new arenas. Experts from Fortune Business Insights predict that the ML market will grow from $15.3B in 2021 to $210B by 2029.
When combined, Machine Learning and Big Data enable machines or systems to learn from experience and use data received from Big Data and predict accurate results. Thanks to ML, data analytics teams can leverage large volumes of high-quality data needed to build accurate and robust models successfully.
Machine Learning and AI rapidly extend their abilities with more advanced algorithms and intelligent services. Their elements integrate into the company's processes to extract insights and assure fast and robust problem-solving without manual assistance. Together, they are going to provide more opportunities to analyze the data flow coming from numerous sources, be it sensors, applications, or Google search queries.
Ultimate processing abilities make Natural Language Processing one of the most recognized Big Data technology trends. NLP requires enormous amounts of data to derive insights from natural languages. It is no wonder that merging NLP and Big Data is a fruitful scenario for companies.
According to Facts & Factors, the NLP market will steadily grow from 2022 and reach $13,277 million by 2028. It means that despite the pandemic and its significant blow to IT budgets, tech leaders are will be in demand for sophisticated text analytics fuelled by the rising voice assistant usage.
Companies plan to focus on systems that can capture a customer’s voice and translate it into machines’ language. With the technology, it will be possible to dig deeper into customer attitudes and preferences and achieve better outcomes for other crucial initiatives.
It, in turn, will lay the groundwork for developing virtual assistants or chatbots with “conversational” capabilities. Using Big Data analytics tools, further advancements are likely to bring the concept closer in the coming years.
A promising Big Data market forecast has already fueled the specialization of roles within IT departments. It urges companies to search for talent with industry-specific expertise rather than an MBA degree.
The Chief Data Officer (CDO) position has become one of the hottest Big Data market trends in 2022, especially in the North American region. Companies appoint such a specialist to address data availability, integrity, and security challenges. The most recent study conducted by Strategy& indicated that 80% of North American companies have a CDO in place.
The same survey, however, indicates an uneven spread of CDO positions worldwide. Around half of the total number of CDO careers have landed a job in the US. At the same time, there is only a 7% ratio of companies with an executive-level data management specialist appointment. So there remains much room for improvement in defining their responsibilities. It looks like the definition of the role will be revised in the coming years.
Our experts will help you handle Big Data and data-related tasks to create meaningful connections between unstructured information and business value.
Almost every company will recognize the benefits of as-a-service platforms in the next couple of years. DaaS delivers data in streams that are tailored to subscribers’ needs with a SaaS-like experience. As-a-service data solutions are one of the Big Data current trends that will move Big Data from the hands of data scientists and engineers to employees throughout the company.
Only a few businesses now have in-house resources to access data streams and manipulate that data efficiently. Through various DaaS options on the market, non-technical staff will have access to user-friendly applications that can gain insights and work more efficiently with real-time data.
Therefore, DaaS can eliminate the need for internal data management teams as it offers a robust means to quickly and easily access external data sources. DaaS solutions will give you more control over company-wide data and fill the gap between departments. In addition, data lake proliferation and secure cloud-based solutions can make relevant data available in an easy-to-use and safe format.
In addition to innovations in cloud storage and the rise of DaaS, companies are turning to new architecture types to store raw data in its native format.
We talk about data lakes, which allow organizations of all sizes to store unstructured and structured data sets. Big Data doesn’t have to be transformed and prepared for end-user needs when moved to a data lake. Instead, companies can upload data sets without any management and governance efforts. Besides, data lake platforms can improve security and accelerate data insights.
Valuable features of data lakes fuel the demand for these platforms. For example, a recent survey conducted by ChaosSearch found that most respondents plan to maintain their data architecture in data lakes. 21% of companies, mostly technology companies with up to 10,000 employees, stated they plan to grow their data lake expenditures by 10%. Besides, 35% of respondents intend to increase their investments by 9%.
Vast amounts of historical data located in different repositories across an Enterprise-level company can affect the data processing activities. This, in turn, makes it challenging to find patterns and make predictions about future and near-future events.
As companies become more challenged to gain valuable insights, forecast malfunctions, and discover new patterns, adopting predictive modeling is one of the latest trends in Big Data analytics. Especially for industries that store voluminous data, the urge for predictive analysis is likely to become one of the top priorities in the coming years.
Predictive methods are one of the major Big Data trends in Healthcare. Healthcare facilities rely more on technology to predict allergic reactions, prevent unnecessary visits, and craft better-targeted therapies.
The true potential of predictive analysis is in disclosing pressing healthcare problems on a global scale. Quick and accurate predictions can add value to traditional historical approaches by pinpointing new outbreaks and revealing where they may head next. As the world has learned lessons from the Covid-19 pandemic, the chances for faster responses and better coherency will improve with predictive mechanisms.
Another example of using predictive algorithms is in the financial services sector. As one of the most data-intensive industries, it can rely on predictive modeling to transform its processes, support dynamic market changes, and minimize risks. Big Data trends in financial services also include an enhanced focus on ongoing improvements and revisions of predictive algorithms.
Regardless of your industry, predictive analytics can provide the understanding needed to make the right move. Whether making financial decisions or working to save lives, keeping an eye on new trends in Big Data analytics can help you head in the right direction.
We prepared a copy about Big Data issues that you can face on the way to the digital transformation of your business. Our Data experts are ready to help you to deal with them properly.
The need for reliable Internet connectivity and an ever-growing number of IoT sensors have triggered the demand for active 5G deployments. The technology is likely to provide a high-throughput data pipeline that can handle huge data volumes to integrate the data coming from various sources.
The technology will improve user experience by connecting household-level wearable tech and VR assistants. At the industrial level, 5G will be powered by the Big Data technology stack to provide faster and safer ways to collect and process data.
Telemedicine has become one of the most widespread territories that have leveraged the 5G technology. China is now in the vanguard of the global 5G revolution as it has started to roll out the infrastructure and facilities to apply the new network to hospitals across the country. The recent Wunderman Thompson report describes the phenomenon as an innovative way for Chinese patients to access doctor’s help anytime, anywhere, without latency and connectivity issues.
What is edge computing? The technology refers to shifting the data processing infrastructure closer to the data generation source. Sending business-critical data to the cloud or centralized data systems is no longer sufficient. The urge for fast, near real-time processing of data streams, goes hand in hand with Big Data growth.
Local data processing and real-time data analysis are likely to grow exponentially in the coming years. Due to the growing number of IoT, AR/VR, ML, Robotics, and other use cases that require intensive workloads.
One of the trends in Big Data analytics trends is related to the increased focus on faster data processing and hunting for insights. When examined and appropriately organized, Big Data can create meaningful connections between unstructured information and business value. In this way, it can facilitate informed business decisions and streamline operations.
The need for improvements in data collection and processing will continue to put to the fore Hadoop, Spark, and other analytics tools. As a result, their value is forecast to grow exponentially in forthcoming years as more and more companies embrace the hunt for actionable data.
Cloud computing enables organizations to store and process large volumes of data irrespective of their location. Previously, they would have to physically expand their premises to scale operations and analyze complex data sets.
Within a few years, Big Data Cloud-based solutions are likely to become mainstream but with certain limitations.
The big question is whether achieving the right data protection level in the short term is possible. Most cloud storage adopters face the issues of data security and privacy issues, growing cloud spending, and a lack of expertise in cloud management. These are the top challenges for Enterprise-grade and SMBs in 2022, according to the novel Flexera’s report.
With 85% of organizations mentioning security as a top roadblock affecting their operations, the demand for cloud security specialists is rising. However, almost 34% of respondents participating in Foundry’s cloud computing study say that their lack of expertise in that field is one of the major challenges for their organizations.
The rapid development of Cloud computing, Big Data analytics tools, AI, ML, and content streaming has already caused urgent environmental implications. As the volume of information stored in data centers grows exponentially, it urges companies to work towards renewable energy solutions.
Data sustainability is one of the top Big Data new trends in the previously mentioned Wunderman Thompson Intelligence report. As of 2022, the infrastructure around digital technologies consumed 2% of global electricity. It is also responsible for a growing percentage of greenhouse gas emissions. The growing carbon footprint is fuelled by the spread of the Metaverse and an accelerating amount of online users.
To reduce technology's environmental impact, governments will embrace regulations concerning energy consumption and nurture ecological awareness. In this regard, the turn to green data centers is anticipated to bring about tangible results. According to Research and Markets, the carbon-neutral data centers market is forecast to grow by a CAGR of 22.19% by 2027 and reach $16.53 billion in market value.
Along with its potential, Big Data issues and challenges can’t be ignored. This happens because more data than ever has been created in recent years.
The advent of wearable devices and other IoT technology-based gadgets has generated massive data influx from ordinary users. Only a small amount of this data is accessible for further analysis with Apache Hadoop, Spark, or other frameworks.
Organizations that process personal data have to comply with data protection regulations introduced by GDPR. The California Consumer Privacy Act had also come into action in 2020. Businesses will be under pressure to keep up with the ongoing changes as failure to comply will result in hefty penalties. Considering those regulations, GDPR-compliant software development is likely to become one of the most important offerings in the industry.
In a recent couple of years, Internet users have already created more data than ever before. Big Data is transforming how businesses operate, and there is no sign of slowing down. We believe 2023 will be another crucial year with trends in Big Data and analytics staying on top of the agenda.
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