Top 10 Data Trends in 2022

In the last few years, we have seen an increase in the need for digital services due to global lockdowns and public health protocols. Organizations around the world are starting to realize that an online presence is an essential component of business and they can no longer remain competitive or efficient in their industries by merely using paper and spreadsheets. With digital services becoming more prevalent, organizations have easier access to data that they may not have had access to in the past, and the ones who embrace this new era can reap the utmost benefits. While having access to data is a great thing for decision makers, there are also critical and valid concerns for original data owners (i.e. regular people/customers), as well as concerns about data quality. So, as the data science industry continues to take off, what will be the important trends to look out for in 2022? In no particular order, this article will explore what I think will be the top 10 data trends this year.

#1. Improved Data Ethics and Privacy

In today’s world, data ethics and privacy are among our primary concerns as we navigate the Digital Age. Data ethics refers to the moral compass of right versus wrong as it relates to an organization’s conduct with personal data. Organizations rely on data to make insightful decisions, and if the data collected has been contaminated or subjected to personal biases and distortions, they are causing more harm to their customers than good. Modern consumers are more aware of certain biases that contribute to discrimination due to questionable data collection and interpretation. We have heard the stories of wrongful criminal accusations due to faulty facial recognition software and the stories of denial of opportunities due to the lack of diversity in data collection. With today’s cancel culture, businesses will be kept on their toes to ensure that they do the work to eliminate biases and work towards good data ethics practices. On the other hand, data privacy is the right to determine how one’s data is used or shared when it’s collected by an organization. More and more consumers are starting to have a stronger desire to control the use of their personal data and to protect it from falling into the wrong hands. Therefore, in order to protect our data and ensure the integrity of decision-making, specific laws, security recommendations and terms and conditions are typically put in place. These regulations dictate how companies should use personal data, and they also suggest best practices for avoiding the use of data and technology in ways that harm others. As consumers, we should be able to trust businesses that we patronize, so in recent times, companies have started to be more transparent with us about how they are using our data. We will see more companies updating their security policies and taking action to reassure consumers that their data will not be misused. Organizations must maintain compliance with regulations for two main reasons: to keep consumers safe from serious harm such as identity theft and to avoid paying fines or being sued for such mistakes. This year, we will see more businesses making a commitment to lead with integrity and transparency, and taking accountability in how they will use technology and data responsibly. As consumers continue to learn about data privacy and ethics, and as we become more tech savvy, businesses will fall in line so as to maintain customer relationships and create environments where ethics and privacy are at the top of their priority list.

#2. More Flexible Data Governance

As organizations engage in more digital activities, they have access to more data, but that doesn’t necessarily mean that they will automatically be successful. They are noticing that they must know how to use the data and extract value from it so it can truly be a tool for success. Data governance encompasses a company’s data control policies, which includes determining standards, roles, processes and security. As technology, the world and the organization continue to change, these data governance policies will have to adopt a more flexible model so that the organizations can maintain their competitive edge and stay in compliance. In 2022, organizations will have to create stronger governance plans to assist them with accomplishing their goals. They will be ready to adapt their policies to suit their changing environments and ensure that they can still curate data with the most up-to-date techniques, while maintaining integrity.

#3. DataOps

DataOps is a method of managing the way data flows in an organization. It enables improved communication and integration of data so that value can be delivered quickly across an organization. A key component of DataOps is the automation of the design, deployment and management of data, taking data governance into consideration. Because it helps to improve data flows within an organization, real-time analytics is facilitated, which in turn makes it quicker to extract valuable insights from data. Real-time analytics will be more prevalent this year as organizations strive to make better decisions as quickly as possible.

#4. New Data Management Strategies

One of the major trends in data management for this year will be the increased use of cloud technologies. Due to the ongoing pandemic and the rise in remote work, an increased number of organizations have started moving their data to the cloud and others will continue to follow suit as the volume, velocity and variety of data that they collect continue to increase. Additionally, there is another trend that may be on the rise in 2022: the emergence of the data mesh. A data mesh is an analytical data management platform that uses a decentralized strategy to enable users to access and query data without transporting it to a data lake or warehouse. It takes time to transport data to a data lake or warehouse, so a data mesh eliminates our current challenges of data availability and accessibility. Extracting value from data will be much faster with a data mesh, but in my opinion, it appears to be only a short term solution for data scientists and analysts to get a head start on gathering insights. As it becomes more popular or mainstream, it may start to replace more traditional and centralized data management approaches.

#5. The Use of Blockchain in Data Science

A blockchain is simply an immutable record of transactions on a peer-to-peer network. I like to think of it as a database that ensures transparency and trust among all related parties. Smart contracts are one component of blockchains that facilitate this trust, and can improve data quality by 50% (according to Gartner). If blockchains improve data quality, this indicates that they are likely to be a good foundation for analytics. Additionally, the decentralized nature of blockchains make it easier to manage big data as data scientists can run analytics from any machine. Blockchains also keep a complete record of transactions and provides transparency and data validation. One of the current challenges of data science is controlling dirty data, as it sometimes takes up approximately 80% of one’s time to clean data and prepare it for use in analytics. The fact that blockchain ensures data validation means we can rely on the fact that no one is manipulating that data so there may be less data cleaning involved. Blockchains are constantly being discussed in recent times, and it has revolutionized how we manage data. Although the technology has its own challenges, I think we will continue to see more conversations and case studies about blockchain being used to improve data science as it integrates with other advanced technologies such as AI tools and IoT.

#6. Increased Use of Artificial Intelligence

This year, we will see an increased use of Artificial Intelligence (AI) in decision-making. In simple terms, AI is the ability of computers to perform tasks that are normally performed by humans. So far, there are many practical applications of AI in several industries. For example, in the manufacturing industry, computer vision is already being used for data analytics. The cameras help to detect inconsistencies, ensure quality checks and improve safety procedures. Organizations are realizing the value in allowing computers to do some of the more repetitive tasks that humans used to do, and this always raises the concern of job security. However, by allowing the machines to do the mundane work, the humans can work on other things that add value to the business. This will continue to be a debate in 2022, but organizations will still continue to use AI as long as they see the benefits for the organization. Another prominent area of AI to look out for is Natural Language Processing (NLP). NLP is the ability for computers to understand natural language (human language) as it is written or spoken, and this has the ability to provide information and insights that cannot necessarily be obtained from traditional data collection methods like multiple choice surveys. When computer programs analyze text via sentiment analysis, organizations can start to understand how consumers feel about them, how they compare to their competitors and what they need to do to improve customer satisfaction. This is a growing field and as organizations continue to use speech and text as data inputs, they will refine the way they provide goods and services.

#7. Automation of Data Cleaning

An important point to note is that having lots of data is pointless if it’s not clean enough to do analytics and gain actual insights. When we say that data is dirty, we mean that it could be duplicated, manipulated, incorrect, redundant or lacking any type of structure. This could hinder data collection, but recently researchers started investigating how to make the data cleaning process less manual. If we can automate data cleaning which usually takes up so much time, we can speed up data analytics and gain accurate insights from clean data. Since AI is already on the rise, it will come in handy for this venture. There are existing software that enable data analysts to use AI models without actually building them, so if we could accomplish the automation of model training, why can’t we accomplish the automation of data cleaning?

#8. Data as a Service

Organizations are starting to offer data as a service (DaaS) to individuals and other organizations. For example, a health organization can provide COVID-19 data to a private organization who can publish charts on their website or use the data to facilitate some business process. In general, the buying and selling of data is a major concern as it relates to the data ethics and privacy issues discussed above. Organizations have to be careful so as to avoid breaching sensitive data. However, we will continue to see the rise of data exchanges in marketplaces for analytics. A data marketplace is essentially an online store that facilitates the sharing of data. It enables organizations to easily and affordably locate data sets and helps data providers to reach more of their target audience. We live in a world where data is valued and because both people and organizations want to access reliable data to make decisions, I think we will see more data exchanges and DaaS operations in 2022.

#9. Data Democratization

In 2022, data democratization will become more prevalent. Since data is a powerful resource that can enable change and solutions to systemic issues, it’s important for there to be equity with data. As people are becoming empowered, we are trying to hold governments and other organizations accountable in their approach to data-driven decision-making. In many cases, data has not been representative of the individuals it is meant to represent and this is where data equity comes in. Equity ensures that diversity is considered and that data is more representative, which makes it effective as a tool for solving social problems. Data democratization also entails the sharing of information so that no one is left behind or denied access in an organization or country. As we strive to build a more inclusive world, data democratization will continue to thrive.

#10. Data Skills Among the Working Class

Finally, the Digital Age has created a global demand for data skills which means that everyone has to understand and work with data in one way or another. It may seem like gibberish to some people now, but it will be necessary for all working individuals to have some level of data skills, no matter how basic. Although there are some barriers preventing every single person from being data literate, we will see an increased need for data literate employees or the continued outsourcing of data services from advanced professionals.

Sources:

https://www.tableau.com/reports/data-trends#trend5

https://technical.ly/2022/01/31/data-predictions-2022-metaverse/

https://www.starburst.io/learn/data-fundamentals/what-is-data-mesh/

https://www.gartner.com/en/information-technology/glossary/dataops

https://www.datatobiz.com/blog/top-data-science-trends/