DATA ANALYTICS

6 Trends Forging the Future of Big Data Analytics

Rachel Serpa

Director of Content, Sigma

If you’re not sure about the future of data analytics, look no further than the nearest cloud. The question on everyone’s mind has shifted from “if” to “when” organizations will migrate to the cloud for computing, storage, and application management. As storage becomes even cheaper, and compute nears infinite levels, cloud adoption is expected to grow at more than six times the rate of general IT spending through 2020 to meet big data analytics goals.

While that growth may seem extraordinary, it makes sense. Organizations that use big data saw an 8 to 10 percent profit increase, and a 10 percent reduction in overall costs. It’s clear that the future of big data analytics holds great promise.

In this blog post, we’re going to explore several key trends set to drive the future of big data for years to come. For a deeper dive, and tips to prepare for these trends, check out our complete eBook 2020 Data Trends to Help You Kickstart the Next Decade

 Data privacy regulation, protection breeds confusion

There have been numerous security breaches, whistleblower announcements, and scandals involving data security and privacy issues over the last decade. In addition, over 1 billion people have had their personal data exposed as a result of security breaches. As a result, companies can expect new data privacy regulations to impact the way they do business.

The General Data Protection Regulation (GDPR) act from May 2018 is a set of far-reaching European regulations that affects Europe as well as other continents. GDPR impacts not only EU residents, but organizations that do business with them.

In the United States, data privacy laws are popping up at the state level. Unfortunately, this means they sometimes overlap and result in incompatible laws and regulations across states—which can be confusing for companies and challenging to navigate.

California is one of the states that is paving the way with the California Consumer Privacy Act (CCPA). This went into effect on January 1, 2020 and enforcement begins in July. Once enforced, it will apply to any of the data that has been collected within 12 months of the enactment. This means companies need to start focusing on these laws immediately.

All of these regulations will require that organizations be vigilant so that they remain compliant and stay away from legal vulnerabilities.

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Read Snowflake’s Chief Technical Evangelist, Kent Graziano’s thoughts on the future of data governance here. 

 CDWs lead the way

The cloud data warehouse (CDW) is expected to replace on-premises and hybrid data warehouses at a swift pace in the coming years. This is primarily because of increased flexibility, rapid scalability, improved connectivity, and decreased cost they provide companies. Even in the area of security, CDW providers now meet high-level standards and certifications that governments, medical facilities, and financial institutions require.

CDWs also relieve organizations of compliance burden. When an organization has all of their data in one location, it makes it easier as they don’t have to handle the complexity of searching multiple data stores for individual records. This makes it easier to comply with GDPR for updates, changes, and deletions.

 SQL, still here after all these years

If it seems like SQL has been around forever, that’s because it was developed almost 45 years ago. Since then, there have been multiple attempts to reinvent SQL because industry leaders believed it couldn’t scale. This led to an emergence of NoSQL databases (i.e. Cassandra, Mongo DB, etc.). However, as data volume and velocity continue to grow exponentially, problems with non-SQL databases have reared their heads. This has resulted in the revival of relational databases and SQL, solidifying its place in the enterprise for years to come.

If you haven’t already, consider a move to a cloud-based data infrastructure. You can learn more about building a cloud-native data infrastructure in our free eBook, Building a Cloud Analytics Stack, and get some tips about choosing the right CDW in this article.

 ELT > ETL: shifting 2 letters makes a difference

Extract transform load (ETL) is the traditional approach to data integration and has been around since the 1970s. It has some serious drawbacks due to its complexity that requires a data engineering team to create highly specialized—and often non-transferable—skills for managing its code base. This makes ETL brittle, difficult to work with, and inaccessible for smaller organizations that don’t have a dedicated data engineer.

By reversing the order of load and transformation, ELT (extract load transform) enables data to be transformed in place on an as-needed basis. This will limit the time data spends in transit while increasing the speed of analysis. As compute, storage, and bandwidth have decreased in cost in recent years, the sequence of transformation and loading can be reversed. This allows an organization to delay the modeling and transformation steps in the process.

Not sure which process is right for your organization? Read our comprehensive breakdown here.

 JSON and semi-structured data lead to breakthroughs

Organizations receive a plethora of semi-structured data that comes from mobile devices, apps, and websites. Semi-structured and unstructured data can make up to 80% of all enterprise data. This data could be videos, photos, emails, documents, and more. This volume of semi-structured data has been made easier to store, analyze, and consume using JavaScript Object Notation (JSON).

JSON has become the de facto format for transferring data on the web. It has grown in popularity because it’s lightweight, parsable, and human and machine-readable. With the amount of semi-structured data growing exponentially, the need for JSON will only continue to grow.

Semi-structured data eliminates the need to parse out and ETL the data into traditional tables and columns — not to mention making it all cloud accessible. The result has made JSON and other semi-structured data easier to store, analyze, consume, and even create analytics for, so organizations can reap the full value.

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Unravel JSON data in seconds with Sigma for deeper analysis. Schedule a demo to see for yourself.

 Augmented analytics drive critical insights faster

Using machine learning and artificial intelligence (AI) to augment data analytics has improved the way analytical data is shared, generated, and processed. It allows AI to assist with data preparation, insight generation, and explanation—improving how humans can explore and analyze data. Augmented analytics allow critical insights to be found with less skill, time, and bias than ever before.

But don’t expect AI to take over just yet. Even as artificial intelligence and machine learning improves in the coming years, there will still be a need to keep humans in the loop. Artificial intelligence is not—and will never be—perfect. People are needed to train AIs and keep a human in the loop to help with the final judgment calls—making sure the system is continues to improve its decision-making.

Data analytics has a cloudy future

Let’s face it, we have a cloudy future ahead of us. As more companies migrate to cloud, cloud data warehouses will become more prominent. Thankfully, SQL, ELT, and JSON will be there to help everyone work with structured, unstructured, and semi-structured data. And as we explore the fascinating world of AI, machine learning, and augmented analytics, remember that it’s important to always keep humans in the loop.

Thirsty for more? Read our 2020 Data Trends and Predictions to Help You Kickstart the New Decade by downloading our free eBook, or watching our on-demand webinar

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