For decades, managing data has meant collecting and storing it, and being able to access it periodically. The approach has changed in recent years. Businesses now need to make important decisions based on massive amounts of information stored in a variety of ways, from corporate data centers to clouds.
As a result, data analytics powered by AI and ML has become critical. In 2022, this trend will continue, and the range of tools will expand. Here are some upcoming trends.
Data Structuring Innovations
Enterprise data analytics uses two approaches.
- The first is to collect data from business applications (CRM and ERP) and import it into a warehouse to then use it in BI systems. Now cloud technologies such as Snowflake are gaining more and more popularity. In this approach, the data is organized sequentially.
- The second is to collect any raw data and import it directly into the lake without any pre-processing. Any type of data is suitable for this, which is why online storages like Amazon S3 are turning into massive data lakes.
The problem is that some data can be processed better than others. For example, logs, genomic data, audio, video, images, and others are not well suited for storage. The reason is the lack of a consistent structure, which complicates the search. Because of this, data lakes eventually turn into “swamps” in which it is quite difficult to find something.
As a result, a data lake house (data lake + data warehouse) was developed, designed to create lakes with semi-structured data that have some semantic consistency. The concept became popular thanks to DataBricks and will continue to evolve in 2022.
The data lake house format is suitable for .csv and Parquet files, as well as other semi-structured data. However, it does not solve the problem of inconsistency, as it does not offer a general structure. Today, almost 80% of the world’s data is unstructured. Optimizing them for analytics is a big area for innovation.
Distribution of citizen science
Citizen science is scientific research done voluntarily by anyone in partnership with professional scientists.
To democratize data science, cloud solution providers will develop and release more ML-based tools. Ultimately, this trend will lead to a reduction in the amount of code that will need to be written. ML systems will become available to a wider range of professionals in both IT and business.
Amazon Sagemaker Canvas is one example of low-code/no-code tools that will see even more adoption in 2022. Citizen science is still in its infancy, but the market is already moving in that direction. Platforms and solutions that make it easy to work with data will take a more prominent place.
Analytics of the “right data”
Big data creates swamps that are difficult to work with. Finding the right data, no matter where it was created, and using it in analytics will save you a lot of time, automate your work, and get more relevant analysis. Therefore, next year, instead of big data (big data), analytics of “right data” (right data) will begin to develop.
Dominance of on-site analytics
According to forecasts, cloud lakes will become the main place for collecting and processing data for research activities. While cloud solutions are gaining momentum, data is also accumulating in other storage formats: at the edge, in the cloud, and in local storage.
Sometimes data needs to be processed and analyzed where it resides, instead of being moved to a central repository. This is a cheaper and faster way. Cloud-based analytical tools will help to implement it. In 2022, there will be more “edge clouds”, in which computing takes place at the periphery of the data center.
Data Driven Governance
A data factory is an architecture that provides data visibility, the ability to move, copy, and access it in hybrid cloud storage.
Near-real-time analytics gives you control over where your data resides in clouds and storage. This helps make sure they get to the right place at the right time. Data factories will become more popular and provide data-centric, not storage-centric governance.
Instead of storing all medical images on one NAS server, you can use analytics and user feedback to segment them. For example, copy it to make it available to ML tools for clinical research. Or move critical data to immutable cloud storage to protect it from ransomware.
Many organizations now use hybrid cloud environments, where most of the data is stored in private data centers on systems from multiple vendors. Unstructured data grows exponentially, so the cloud is used as a secondary or tertiary storage layer.
In such an environment, it can be difficult to manage costs and risks, as well as maintain productivity. IT leaders are realizing that extracting value from data distributed across cloud and on-premise environments is a difficult task. Multi-cloud strategies work best when you use multiple clouds for different use cases and datasets.
However, another problem arises – the transfer of data from one cloud to another is very expensive. The new concept proposes to direct calculations to data that is stored in one place. Such a central place can be a server located in a data center and directly connected to cloud providers.
The multi-cloud will evolve through various strategies: sometimes computing will move to data, and sometimes data will reside in multiple clouds.