Kevin Bartley

2021 marks an inflection point in data management. Data teams will make decisions in 2021 about data management that will determine the trajectory of their businesses for the rest of the decade. New advancements, and trends that have been growing for years, will empower teams to seize more market opportunities.

But in order to capitalize in 2021, teams will need to understand the new landscape.

That’s why we developed our latest eBook: Data Management 2021: Trends, Technologies, Teams, and Organizations.

Here’s an excerpt from the eBook about the key trends and technologies that will drive data management in 2021.


1. Artificial Intelligence/Machine Learning

In 2021, big data will only get bigger. An unprecedented volume of data, along with a relentless drive toward efficiency, will push companies to minimize human input in data operations. Just as with so many other fields, AI/machine learning will drive many of these advancements. AI/ML will automate core data management tasks, from data identification, to data classification, to semantic meaning, and much more.

Throughout 2021, data management will harness machine learning to facilitate automation, optimization, and capacity management. Machine learning will power a diverse array of data management capabilities, including data cataloging, metadata management, data mappings, anomaly detection, and other key processes. Meanwhile, AI will contribute to suggesting recommended actions, auto-discovery of metadata, and auto-monitoring of governance controls.

The benefits of AI/ML in data management are manifold. AI/ML enables companies to process more data, and at a faster rate. These technologies prepare and transform data for use in downstream BI and analytics platforms, improving query quality, system performance, and data virtualization. Increased automation eliminates a significant amount of labor-intensive tasks. And AI/machine learning enables non-experts to access and harness data without involved preparation.

As 2021 progresses, AI/machine learning will move from automating data management tasks into more intelligent, learning-based processes, such as search, discovery, and capacity planning. Data management technologies will adopt more sophisticated AI/machine learning to keep pace with market demands. This tracks with the broader, long-term tech goals of many companies: by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI.


2. Augmented Data Management

Here’s a jaw-dropping statistic: data scientists spend 80% of their time cleaning data rather than creating insights. This creates a big opportunity cost. Data scientists are some of the most sought after employees, with a 2018 LinkedIn Workforce Report claiming the demand is “off the charts”. On online job boards, there are three times as many data scientist job listings as there are searches for those jobs.

Employers aren’t bending over backwards to hire data professionals so they can clean data. Data professionals possess unique, hybrid skill sets that give data-driven businesses immense competitive advantages. Companies are starting to recognize that to get the most out of these cherished hires, they need to adopt technologies that limit grunt work. That’s where augmented data management comes in.

Augmented data management performs the key functions of data management, including ingesting, storing, organizing, and maintaining data. But ADM uses machine learning and AI to automatically refine data. Augmented data management performs low-level tasks, such as data preparation and data cleansing, eliminating the need for human input. By nixing these manual tasks, ADM enables data teams to focus on more important priorities, boosting productivity and efficiency.

In 2021, the importance of augment data management will become even more pronounced. As data volumes grow exponentially, and the relative supply of data professionals continues to shrink, companies will turn to augmented data management. Gartner predicts that by 2022, augmented data management could reduce manual data management tasks by 45%.


3. DataOps

A successful data operation is not simply about the technology an organization deploys. Technology alone cannot sustain success. In order to ensure that the right data gets into the right hands quickly, an organization must also develop the right processes, protocols, and other operational components. Currently, many companies do not emphasize this integrative method. But as data grows in volume and complexity, scaling a data operation without this holistic approach becomes much harder.

That’s why DataOps will reach the mainstream in 2021. DataOps applies the principles of DevOps to data management. DataOps combines agile development, technologies, processes, and practices such as statistical process control to deliver data and analytics across a company. Instead of barricading data management between different teams, tools, and processes, DataOps aims to break down these barriers and institute a company-wide data operation whose constituent parts are integrated and optimized. It plays a crucial role in data democratization across an organization.

DataOps isn’t a product or a service. It’s an agile methodology executed from the top-down. Organizations that implement DataOps will accrue a number of benefits, including improved insights, cost reduction, higher efficiency, and most importantly for the long-term, rapid scalability.

Simple Solutions for Complex Data Pipelines

Rivery's SaaS ELT platform provides a unified solution for data pipelines, workflow orchestration, and data operations. Some of Rivery's features and capabilities:
  • Completely Automated SaaS Platform: Get setup and start connecting data in the Rivery platform in just a few minutes with little to no maintenance required.
  • 200+ Native Connectors: Instantly connect to applications, databases, file storage options, and data warehouses with our fully-managed and always up-to-date connectors, including BigQuery, Redshift, Shopify, Snowflake, Amazon S3, Firebolt, Databricks, Salesforce, MySQL, PostgreSQL, and Rest API to name just a few.
  • Python Support: Have a data source that requires custom code? With Rivery’s native Python support, you can pull data from any system, no matter how complex the need.
  • 1-Click Data Apps: With Rivery Kits, deploy complete, production-level workflow templates in minutes with data models, pipelines, transformations, table schemas, and orchestration logic already defined for you based on best practices.
  • Data Development Lifecycle Support: Separate walled-off environments for each stage of your development, from dev and staging to production, making it easier to move fast without breaking things. Get version control, API, & CLI included.
  • Solution-Led Support: Consistently rated the best support by G2, receive engineering-led assistance from Rivery to facilitate all your data needs.

4. Data Governance

With huge volumes of data, regulations such as the GDPR, and the ever-complexifying relationship between internal and external data, governing data has never been more difficult. Data quality, data security, data auditing, and many other data issues are not only becoming more complicated, but also more interwoven. That’s why, in 2021, companies will focus on developing more comprehensive data governance strategies.

The Data Governance Institute (DGI) defines data governance as a practical and actionable framework to help a variety of data stakeholders across any organization identify and meet their information needs. In practice, this means developing the systems, system of rules, processes, and procedures to deliver data throughout an organization with consistency, security, and uniformity.

Data governance offers a range of benefits across an organization, including regulatory compliance, high data quality, lineage and auditing, consistency and accuracy, increased efficiency, and more. Taken as a whole, data governance can improve every aspect of a company that involves data, from insights and analysis, to scalability, to legal certitude, and beyond. A superior data governance architecture can improve business outcomes across an organization.

In 2021, companies still have significant work to do in the realm of data governance. Today, just 3% of data in enterprise businesses meets quality standards, and 60-73% of data is never used for any strategic purpose. And through 2022, only 20% of organizations investing in information governance will succeed. But companies that are willing to put in this hard work will be rewarded in 2021.


5. Automation of Augmented Data Cataloging and Lineage

In the past several years, two key trends have emerged in data management. Data warehouses moved to SaaS, and now data pipeline and ELT tools are transitioning to SaaS as well. Alongside this development, BI tools are moving closer to “augmented analytics,” or the use of new technologies (including AI/ML) to expand how people explore and analyze data. The next step is “augmented data cataloging and lineage.” This is the metadata’s metadata.

By conducting all data processes in a single “vessel,” a SaaS data management platform, data operations can achieve new levels of scalability and automation. Data operations moving to SaaS allows for auto-generation of data cataloging and lineage as data pipelines are being built. Inputs such as data pipeline metadata and the analysis of that metadata are created on the fly and will become a completely automated and ‘complimentary’ piece of building data workflows.

In 2021, teams that can automate can automate augmented data cataloging and lineage will leapfrog the rest of the market. This eliminates manual work and maintenance, and opens up data operations to personnel who are not comfortable with managing technical infrastructure.


6. Data Fabric

A data fabric manages the collection, governance, integration, and sharing of data across a single unified architecture. Data fabrics aim to offer frictionless access and sharing of data in a distributed network environment. In a world of fast-changing and fast-growing data, companies build data fabrics to “weave” together all data and data operations into a singular framework.

Data fabrics offer a number of benefits, including eliminating data silos, simplifying data management, enabling hybrid cloud and on-premise infrastructures, and supercharging scalability. To construct data fabrics, companies are using capabilities such as graph technologies and semantic standards, along with solutions such as ETL, ELT, and augmented data management.

In 2021, data fabrics will grow even more essential for companies undergoing digital transformation. As companies continue to migrate to the cloud, as data volumes and data types explode, and as data consumption turns toward on-demand channels, the need to seamlessly “weave” together data ecosystems will grow.

Download the Full eBook!

Data Management 2021: Trends, Technologies, Teams, and Organizations