The Top Data Trends to Follow in 2022
Here’s an uncontroversial thought: 2020 was a year no one could’ve predicted.
A global pandemic disrupted virtually every industry across the world, and the tech world was certainly affected. Information science, AI development and other core data science concentrations became largely virtual fields, as scientists and engineers worked remotely to solve new data challenges.
In many ways, COVID-19 expedited many of the changes the data science world endured in 2021. Now in 2022, the tech world — and specifically, the data science landscape — is experiencing new changes. Algorithm shifts, updates to data analysis best practices, cloud architecture renovations and other updates rank among the largest data science trends worth following in 2022.
Generation of intelligent features
If your organization is looking to save time, money or energy at scale, there’s a good chance you’ve considered automation. Automation requires the use of “intelligent features”, or components that use data, and often machine learning, to automatically complete repetitive, mundane or complex processes.
For example, a digital marketing agency might deploy automation to automatically sort through large amounts of customer data, in order to identify the best-performing channels.
Similarly, a data scientist might depend on intelligent features to simplify equations that help them draw new conclusions from a company’s datasets.
Data scientists might write basic queries in order to extract information from a spreadsheet, dataset or other data type. This method vastly simplifies schema that could include a large amount of data, potentially sourced from consumers, patients or goods. Simplifying queries helps to expedite automation.
Conversely, data without the necessary entity event templates will often require a complex query, for even basic functions.
The problem of explanation, which is linked to interpretability, model bias and appropriate AI, can undermine market benefits from statistical AI deployments. Nevertheless, companies can conquer this challenge by coupling mathematical and informational AI.
In the coming year, many data scientists will focus on increasing machine learning through the expertise of AI, typified by rules-based learning.
This would extend the types of data available to virtually anyone responsible for its analysis. For companies around the world, explainability could represent new breakthroughs in symbolic logic, semantic inferencing and other dataset rules.
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Accurate feature recognition hinges on noise reduction for dynamically evolving data (such as the transactions of e-commerce, suggestions or applications of the Internet of Things). To reduce the variables of training models, data scientists use unsupervised learning strategies, equivalent to clustering.Potential benefits in this knowledge graphs application include:
For use in machine learning models, data needs to be vectorized. Graphs with appropriate matrix support can help enable organizations to transition information from graph to matrix form.
Subsequently, organizations can perform functions like PCA, allowing data scientists to clearly identify data trends and correlations.
Reduced data preparation
In contrast to processing data, elaborate pipelines of graph embedding can compromise a data scientist’s available time. Fortunately, data science can help cut down the time a data scientist will otherwise commit to the process of data preparation.
For this machine learning task, transferring data into software like Python is time-consuming and intensive programming. Graph databases can perform the same task at a greatly accelerated rate.
Granular feature engineering
For optimizing functionality and other elements of training models, graphs are also suitable for inputting the effects of machine learning analytics, such as clustering.
In comparison to methods such as Random Forest or assembly strategies such as gradient boosting, the effects of vastly multi-layered neural networks have become the most difficult to describe. Specifically with the computation and deep learning, these frameworks and others can be standardized to optimize implementation:
By opting for algorithms with limited tuning parameters and incorporated objective functions, data scientists can accelerate the cumbersome process of tuning parameters for machine learning techniques.
Open Neural Network Exchange (ONNX)
ONNX is an architectural model for the distribution of deep learning models," as per SAS Chief Data Scientist Wayne Thompson. The scope of use of ONNX is expansive; one could devise strategies in a proprietary system, then "someone else will put it into open source and then use my framework as an initial target and train it more for their environment,” noted Thompson.
Convolutional Neural Networks (CNNs)
Computer vision is one major application of CNNs. "Today, they are looking cleverer than humans," said Thompson. "So yes, they are very helpful for the recognition of an object, and there are a plethora of use cases for that."
Recurrent Neural Networks (RNNs)
For forecasts and text analytics, RNNs function well. That's how they look at a data point set," Thompson added." "A discussion is a token of sentences spoken that have a chain to it."
Information is kept between NAND flash memory and dynamic random-access memory in a new memory tier. This offers much faster memory recall for advanced data analytics in industries that can accommodate high-performance workloads.
Data-as-a-service incorporates cloud infrastructure that offers on-demand access to data for users and apps, without relying on users or applications.
Data as a service can provide a variety of useful functions, including hardware and software synthesis, supplementary technology integrations and platform support.
This cloud computing model adapts data storage and computation closer to its necessary position.
To boost data processing, augmented analytics employs both machine learning and artificial intelligence by seeking a new way to generate, develop, and exchange data analytics.
This method of data analysis reduces human error and bias, which is why many companies prefer increased over conventional analytical processing. This offers a boost to data streaming without latency, including real-time data processing and streaming.
Dark data is the data from any and every analytics method that an organization does not need. The information is collected from many network activities that are not used to evaluate insights or project results.
Core data science capabilities
This year, new analytics skills combine with traditional data science responsibilities, to create a newly required skillset.
Data scientists still use R on a few occasions, though Python remains one of the most useful programming languages in the world.
After Python 2 was withdrawn by most libraries on January 1, 2020, Python 3 (the new edition) has now officially become the standard language version for most apps.
If you are now studying Python for data science, it’s important that you find coursework that accommodates this version.
SQL and NoSQL
SQL has existed since the 1970s, and it still ranks among the most relevant skills for data scientists. Organizations around the world use relational databases as their analytical data stores, and SQL is the platform that can provide you with this knowledge as a data scientist.
NoSQL does not collect data as relational tables, but instead stores data as wide-columns, key-value pairs or graphs. If you regularly use Google Cloud Bigtable or Amazon DynamoDB, you’re likely familiar with NoSQL databases.
Organizations commonly shift from a conventional data warehouse to NoSQL databases if the amount of gathered data and unstructured information grows over time.
For data manipulation, analysis and processing, Pandas is still the preferred Python library. Pandas is the instrument that can help you to extract, process, clean and derive insights from virtually any dataset. Pandas DataFrames are still commonly adapted by most machine learning libraries as a normal input.
A vast majority of companies currently use a form of cloud technology, and that trend is expected to further expand this year.
Leading cloud vendors, including Amazon Web Services, Google Cloud Platform and Microsoft Azure, are increasingly evolving tools for machine learning models. As we approach 2023, these and other cloud platforms — and their associated skillsets — are expected to remain in high demand.
Traditionally, data science coding is a messy practice, one that sometimes is not well checked. Software engineering can remedy this trend, and provide a data scientist with an improved understanding of operational machine learning.
Many organizations are increasingly implementing Apache Airflow, an open-source workflow management platform, for the management of machine learning pipelines and ETL processes.
Major tech corporations such as Slack and Google are already using it; Google even developed their own cloud composer tool. Data scientists at virtually any level can learn this still-developing platform, for a clear perspective into the emerging direction of machine learning.
What's next in data science?
2022 represents an exciting year for data science. Smart devices, hybrid cloud, increased NLP adoption and AI highlight some of the year’s major evolutions.
Pragmatic AI, algorithmic separation, containerization of analytics and AI, quantum analytics, differential privacy and augmented data management could also affect the data science landscape before 2023 arrives.
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