Modern Data Stack has helped businesses follow trends and elements of the future. Moreover, it includes AI and ML-driven analytics.
Above all, the modern data stack and its trends help businesses stay ahead of the curve. Industry leaders and experts also agree data is an integral part of digital transformation. As a result, businesses need to adapt to the changes and evolution in data management.
Previously, we Learn Everything You Need to About Modern Data Stack. In this article, we will walk through the reasons to use modern data stack and the trends that emerge in this data revolution.
Reasons to Consider a Modern Data Stack and Trends for Businesses to Follow:
According to research by Salesforce, 63% of retail decision-makers don’t respond to consumer demands and insights in an agile manner.
Moreover, a modern data stack works well when data pipelines, cloud data platforms, and business intelligence tools. Hence, using these components presents actionable data for high-impact decision-making processes.
Here are the reasons for choosing and following modern data stack and its trends:
- Firstly, a modern data stack helps automate data integration processes. Furthermore, it is a robust cloud platform that distinguishes storage and computation with elastic scalability.
- Moreover, it enables the collaboration between business intelligence and visualization solutions. Hence, allowing businesses to extract data and actionable information.
- A modern data stack can certainly be integrated with legacy and on-premises solutions that businesses often use. That is to say, it provides automation access to support businesses to keep up with mission-critical and consistent updates.
- In contrast to legacy, a modern data stack provides a better user experience, performance enhancement, and more potential.
- Previously, gathering data followed the ETL process, which is the extract, transform and load. However, now cloud data warehouses follow an ELT process.
Hence, classifying the data pipeline into four stages that are collect, load, transform and analyze.
- Most importantly, a modern data stack makes it easier for businesses to depend on solutions for actionable insights. Although, it is not only used to extract actionable information.
- For instance, some businesses use a modern data stack for automating payroll and billing, monitoring intrusion activities, detecting market trends, etc.
- Above all, a modern data stack helps reduce the latency time. As a result, actionable insights are processed and collected in time for data analysis.
To begin with, one of the most prominent modern data trends is to make data accessible. Moreover, it is pivotal for industries to discover faster ways to replicate applications, databases, events, etc.
Moreover, making the data mobile and increasing the transparency of the process of analyzing the data. Hence, offering seamless solutions for exploring, transferring, and governing the data to optimize its potential.
In addition, insights and information are made actionable for decision making. Subsequently, using AI and machine learning capabilities for accurate predictions. Hence, helping industries prepare for new challenges and trends.
Business Intelligence has become the reality for businesses across industries. Although, BI without data is incomplete.
Simultaneously, BI is a process that is still evolving and turning into actionable BI. Actionable BI is a term that implies the use of information to make positive strategies to develop businesses. As a result, decisions using actionable BI can be autonomous and help with predictions.
Currently, businesses have also discovered a promising application of BI. As a result, it is implemented in the workflow.
Moreover, it helps create a loop between data pipelines and operational pipelines. Therefore, merging BI and workflows to create a customizable, no/low code actionable BI platform.
Above all, it also automates the feedback loops using data to make predicted changes. Hence, improving the delivery of products, services, and solutions offered by businesses.
One of the most important modern data stack trends is the democratization of data stack. In other words, the democratization of data refers to the access of data for an entire enterprise regardless of their positions or tenure.
For example, a company like Netflix that deals with large sets of data depends on hiring various data engineers and spending large sums of money on open source technology. Although, most of these issues are resolved when they subscribe to high-end data warehouses and their solutions.
According to George Fraser, CEO of Fivetran, “Data management is getting easier. Technology often goes the other way, it gets more complicated. But we’re seeing a winnowing and I think that’s significant and a good thing. The cost of the fundamental components has come way down but it’s also true that people are doing more with data.”
He added, “A few analysts can accomplish what five years ago Netflix would have had to invest $10 million in, which is cool. It’s making it accessible to companies with less sophistication and companies that aren’t on the coasts or hiring the fanciest teams with the best LinkedIn profiles. More like mere mortals can do this stuff, which is a good thing.”
Businesses involved with various industries are now investing in cloud and multi-cloud solutions for analytics.
Moreover, there is a large shift in businesses to adopt cloud services. Most importantly, businesses now recognize the potential of the cloud due to its capacity and accessibility.
Furthermore, not just startups and digital companies, but also traditional businesses now embracing cloud solutions. As a result, paving the way for digital transformation for various industries.
Recently, the pandemic has evolved and rendered many businesses to adopt digital solutions and cloud services. Hence, allowing their employees to run the businesses remotely.
Earlier, data security was a priority among businesses, and only authorized employees were allowed to access the information remotely. Although, the advancement of cloud services has led to high security, privacy, and data governance. Hence, businesses trust and depend on these solutions more.
These cloud platforms also provide solutions for various categories of data sets. Above all, businesses can depend on it regardless of industry, location, and users.
Data is a valuable asset for any business. As a result, businesses are not recognizing its value for predictive actions.
According to Joe Maguire, Senior Research Director at Gartner, “Aligning the data, data science, and ML pipelines alongside the application deployment process is fundamental to the continuous delivery and continuous integration of periodically enhanced ML models within AI-based solutions. This requires leveraging DataOps, MLOps and Platform Ops for AI to scale the AI architecture. Hence, AI orchestration platforms to operationalize AI are emerging.”
Hence, proving that it is imperative for businesses to study their history of data to help make decisions and executions in real-time. Moreover, businesses that have the ability to make actionable and accurate decisions must invest in predictive analysis and models.
AI and Machine Learning will play a very important role in Modern Data Stack. Moreover, AI/ML-driven intelligence and predictions would evolve analytics. Hence, one of the upcoming modern stack trends would be to leverage the capability of using historical and real-time data.
Moreover, companies are now evolving their business to execute autonomous digital functions. Hence, evolving the data analysis for accurate actionability. As a result, this leads to automation in the execution of insights.
Furthermore, AI and ML are an integral part of modern technology. As seen, businesses have already evolved their solutions to provide better services to their customers.
Debanjan Saha, Vice President of Data Analytics at Google Cloud states, “The way people, ordinary business users, are using AI/ML to do extraordinary things, is going to change the way businesses operate in future.”
He then continues, “Next year there will also be more augmented analytics, where you are going to see more and more AI and machine learning being integrated into people’s natural business workflows. The modern BI is about creating a data API on top of your data assets and then integrating your dashboard and your workflows into your business applications.”
Data lock-in or cloud lock-in refers to the transition of data, products, or services that are made difficult to transfer to another platform. Hence, making the data, products, or services costly and making customers dependent on a single solution.
According to Colin Zima Chief Analytics Officer at Looker, “The big fear that I always have is that people are using more and more of these SaaS tools. The average company has a ton of SaaS tools and all of them have data locked in. So Salesforce has some of your sales data but Slack has chat data and you’ve got all these systems that are holding data sets.”
He then continues to state, “My biggest fear is that these services will start trying to lock up their data more. Salesforce just bought Slack and they’ve got Tableau. And I’m always scared that that data is not going to be available for other products or services.”
Although, the concern for data lock-in may pave the way for certain vendors to let their data be open.
The Director of Product Marketing at Actian, Lewis Carr, addressed the concern, “Analytics tools that try to become one-stop shops for all underlying data and cloud data warehousing needs will also meet challenges, as customers will be wary of vendor lock-in.” He then continued to state, “For a modern data stack to work, it needs to be open to all origination sources, analysis and visualization destinations.”
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