As we kick off 2023, it’s important to consider the trends that will shape the data analytics industry in the coming year. Here are my top 5 predictions for data professionals to keep an eye on:
1- Real-time data analysis will be key
In a fast-paced business environment, the ability to quickly analyze and act on data is crucial. Expect to see more organizations investing in technologies like streaming analytics platforms to enable real-time processing and analysis.
Below is a list of the leading Real-time data Organizations globally, in addition to the big-name players in the field (Google, Amazon, and Microsoft)
a. Datadog: Datadog is a cloud-based monitoring and analytics platform that provides real-time insights into the performance and availability of applications and infrastructure.
b. New Relic: New Relic is a cloud-based monitoring and analytics platform that provides real-time visibility into the performance and availability of applications, infrastructure, and services.
c. Elastic: Elastic is a company that offers a range of products and services for collecting, storing, and analyzing real-time data from a variety of sources. Its flagship product is Elasticsearch, a search and analytics engine.
d. Grafana: Grafana is an open-source platform for data visualization and monitoring. It allows users to create real-time dashboards and alerts based on data from a variety of sources.
2- Data lakes will continue to grow
Data lakes have become a popular way to store and manage large amounts of data, and that trend is set to continue in 2023. Look for a proliferation of tools and technologies to help manage and analyze data stored in these platforms.
Here are some popular data lakes that are currently available:
a. Amazon S3: Amazon Simple Storage Service (S3) is a cloud-based storage service that can be used as a data lake. It allows users to store and process large volumes of data, including structured and unstructured data.
b. Google Cloud Storage: Google Cloud Storage is a cloud-based data storage platform that can be used as a data lake. It allows users to store and analyze large volumes of data from a variety of sources.
c. Azure Data Lake: Azure Data Lake is a cloud-based data storage and analytics service provided by Microsoft. It allows users to store and analyze large volumes of data, including structured and unstructured data.
d. Snowflake: Snowflake is a cloud-based data storage and analytics platform that can be used as a data lake. It allows users to store and analyze large volumes of data, including structured and unstructured data.
e. Hadoop: Hadoop is an open-source framework for storing and processing large volumes of data. It includes the Hadoop Distributed File System (HDFS) which can be used as a data lake.
3- Data privacy and security will be top of mind
With data privacy laws like GDPR and CCPA becoming more prevalent, organizations will need to prioritize data privacy and security to protect their assets. Expect to see more regulation around data privacy as well.
4- Artificial intelligence and machine learning will rise
These technologies have already revolutionized data analysis, and that trend will only continue in 2023. More organizations will use machine learning algorithms to automate data analysis and make faster, more accurate decisions.
Here are some examples of newer artificial intelligence (AI) and machine learning (ML) tools:
a. Google Cloud AI Platform: A cloud-based platform that allows developers to build, deploy, and manage machine learning models.
b. Amazon SageMaker: A cloud-based platform that simplifies the process of building, training, and deploying machine learning models.
c. Azure Machine Learning: A cloud-based platform that enables developers to build, train, and deploy machine learning models.
d. TensorFlow: An open-source machine learning framework developed by Google that is widely used for building and training machine learning models.
e. PyTorch: An open-source machine learning framework developed by Facebook that is popular for building and training deep learning models.
f. RapidMiner: A data science platform that allows users to build and deploy machine learning models.
g. KNIME: An open-source data analytics platform that enables users to build and deploy machine learning models.
5- Data literacy will be in high demand:
As more organizations recognize the value of data, there will be a greater need for professionals who can help others understand and effectively use data.
There are many websites and online resources that can be helpful for improving data literacy skills. Here are a few data literacy resources we recommend to our clients:
DataCamp is an online platform that offers courses and tutorials on data literacy topics such as data visualization, data analysis, and machine learning.
Coursera is an online learning platform that offers a wide range of courses on data literacy topics, including data visualization, data analysis, and machine learning.
Khan Academy: https://www.khanacademy.org/
Khan Academy is a non-profit educational organization that offers a range of resources on data literacy topics, including lessons and exercises on data analysis and statistical analysis.
Datawrapper is a data visualization tool that offers a range of resources on data literacy topics, including tutorials, blog posts, and case studies.
Dataquest is an online platform that offers courses and tutorials on data literacy topics such as data visualization, data analysis, and machine learning.
As we move into the new year, it’s important to stay up to date on these trends to stay ahead in the data analytics field. If you have further questions, feel free to contact our team for a free consultation.