Data delivers value to today’s organizations in myriad ways, from fueling fact-based decision-making to expanding data-focused product offerings. And to that end, the data and business intelligence (BI) ecosystem is constantly evolving, driven by the maturation of market leaders like Fivetran and Snowflake, as well as companies such as Census and dbt.
A mature data stack ecosystem provides the analytics community with an opportunity to erase 20-year-old paradigms that led to nothing but frustration and delayed business outcomes in favor of establishing new best practices for its ecosystem. These practices can usher in a new era of data exploration, one that’s agnostic of technical skillsets and limited only by imagination.
Table of Contents
- Prediction 1: Your data is in the cloud – and your BI solution will be, too
- Prediction 2: ELT is evolving to fully managed ELT
- Prediction 3: You can’t be data-driven without DataOps
- Prediction 4: Unlocking operational analytics at scale
- Prediction 5: Governance remains paramount to any data initiative
- Prediction 6: BI tools increase collaboration between data teams and business units
- Prediction 7: The continued rise of the machine
Your data is in the cloud — and your BI solution will be, too
The rise of the modern data stack (the layered stack of technologies, cloud-based services, and data management systems that collects, stores, and analyzes data) has brought a new way of thinking to traditional data processes, and with it, the demand for faster, yet still accurate, reporting.
Unfortunately, as businesses are just now evaluating or beginning their data stack modernization journey, BI looks something like this:
Executives race to uncover why QoQ growth is slowing before a board meeting
Different business units struggle to agree to joint reporting metrics because there’s no unified metrics layer or centralized reporting destination
Business users are constantly extracting data to create dashboards and reports, which proves to be a slow and painful process that results in disjointed, inaccurate, and outdated data
Teams struggle to access the latest data to make key insights
Legacy BI tools struggle to maintain data integrity due to the number of hands and processes the data goes through on its journey to a dashboard or report
Building A Modern Cloud Analytics Stack: A Guide for Data-driven CompaniesDOWNLOAD
The cause is simple: The systems and tools supporting the stack provide a disjointed experience for BI teams and data consumers alike. BI and data teams spend a lot of time cleaning, preparing, and modeling data before handing it off to business domain experts — and rinse and repeat for every new data source and question. At this point, domain experts can finally make clear and informed decisions to drive the business forward.
But, in 2022, organizations of any size will be able to do the same utilizing a suite of fully managed SaaS solutions that:
Automatically connects and normalizes data from across sources in real time, preparing it for storage and querying using analysis-ready schemas
Provides elastic infrastructure, unlimited scale, cost-effective risk mitigation, security management, and other cloud-specific benefits that traditional on-prem warehouses do not
Allows organizations to maximize the value of their data by building a bridge from the past (Excel) to the future of analytics
This positive feedback loop between the integrated technologies and the benefits to agility that form the modern data stack will provide room for unforeseen opportunities and open the door for more collaborative, organization-wide data experiences.
ELT is evolving to become fully managed
The idea that ELT (Extract – Load – Transform) is a completely new approach is a myth. ELT has been around for as long as ETL (Extract – Transform – Load), but the new technologies and approaches to each step of the process have evolved.
ETL was formerly the standard order of operations for data loading, that made sense of the varied data structures and constrained the amount of data that was put into the warehouse to avoid slow query times or outright crashes.
With new cloud warehouses and other supporting technologies, teams are now looking for more speed and flexibility. This translates into a few key business requirements for data pipelines:
The expectation of fresh data at real-time or near real-time intervals
Not having to host and scale runtime infrastructure to accommodate continuously increasing data volumes
Shortened onboarding times required to implement and learn how to use tools
Having all data available, at all times, to answer ever-evolving business questions
Ultimately, teams tackle these sorts of efficient pipeline builds and maintenance by either building upon a flexible, cloud-based infrastructure and orchestration supported by a vast engineering and data organization. They can also shorten time-to-value by adopting tools that manage every step of these processes to ultimately build a flexible and agile data analytics environment.
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