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.
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dashboards and reports, which proves to be a slow and painful process that results in disjointed, inaccurate, and outdated data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
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.
You can’t be data-driven without DataOps
The first layer of the modern data stack, data pipelines like Fivetran, collect and integrate databases, files, and more so it can be easily accessed, modeled, and holistically analyzed. But this is where things get tricky. Where the modeling happens – and how it is managed and tracked – depends on the context and business impact.
While there are various tools vying for market leadership in the realm of DataOps to consider once your team (and the amount of data that you’re working with) has sufficiently grown, more and more tools are building in features to help support that eventual adoption.
For example, dbt is a command-line tool that enables data analysts and engineers to transform data in their warehouses by writing SQL statements while also bringing in engineering best practices, such as version control or lineage graphs. By being able to see who’s ran what models, what objects have been affected, and who the actor(s) were, teams are able to build upon tribal knowledge more quickly for accelerated analytics.
Tools have to be able to talk to each other. Integrations with tools incorporating DataOps practices help to uplevel the entire organization on best practices and the complexity of tracking data flows. By creating best practices prior to implementing a DataOps tool, you can uncover what works best for you and your team, as well as what your ultimate evaluation criteria should be when uncovering the gaps present in the tools that you already use.
Unlocking operational analytics at scale
What if the modern data stack didn’t just power downstream data analysis, but could feed into operational systems that drive day-to-day business processes?
For example, think of a solution that could push product usage data out of the cloud data warehouse, combine that with engagement data on other marketing channels, to help create even more targeted marketing email campaigns. Or take key user behavior data, such as interactions with documentation or interactions with feature-focused webinars, from the cloud data warehouse (CDW) directly into customer support software so that help desk agents can have more context readily available to quickly assist customers.
Operational analytics realizes this idea by making data more accessible throughout an organization, so that non-data teams can take action upon it within the context of the day-to-day tools they use. Operational analytics makes data work for users and completes the data and analytics loop:
- ELT tools provide an access point to get data
- CDWs and data lakes co-locate and aggregate the data for analysis
- Modeling and BI/analysis tools provide a way for people to interact, analyze, and explore the data within the CDW/data lake
- “Reverse ETL” provides an access point to get refined results back to the operational applications
This continues down the trend of end-to-end automation to accelerate strategic business changes — from modeling of aggregate data across multiple business applications to pushing changes directly into the applications that would have needed to be manually updated otherwise — and has a side benefit of empowering the data team to more prominently lead the business.
Governance and security remains paramount to any data initiative
High-quality data is among an organization’s most valuable assets. Using it effectively is crucial for corporate sustainability since inaccurate or outdated data can result in misguided decisions. And privacy regulations, such as the California Consumer Privacy Act (CCPA) and EU General Data Protection Regulation (GDPR), demand a firm hold on data security.
Those regulations are a direct response to increased attention around data and privacy from the general population, who are also concerned about who has access to their information. It’s why, each year, organizations spend millions on infrastructure, security solutions, data management systems, and governance initiatives to protect themselves from data breaches. Yet, despite all of the money, time, and resources that go into safeguarding data, the most innocent mistake can end up in a costly breach. In fact, in 2021, the average cost of a data breach rose 10% to $4.24 million.
This movement to protect data and the tools that handle that data on your behalf is only going to continue, and regulations will rightfully only continue to tighten. When making evaluations of tools or building your own internal processes, you have to understand exactly what safeguards are in place to securely pass and store data. Look at your security and/or compliance team as a staunch ally here — the bodyguard making sure that your foray into uncharted data territory ends in successful findings (and no customer data leaks).
BI tools increase collaboration between data teams and business units
The most valuable way business users interact with their colleagues in the current data experience is the messiest — it’s in Slack conversations, over-the-shoulder demonstrations, or ad hoc emails with a thrown-together chart with a few bullet points.
On the other side of the coin, analytics teams often work in isolation, resulting in knowledge silos. Analysts unknowingly rewrite their colleagues’ analyses, or they may fail to grasp the nuances of datasets that they’re less familiar with, resulting in different definitions of a shared business metric from other groups. As a result, organizations suffer from reduced decision speed and quality.
In 2022, collaborative analytics will finally come of age in an experience that mirrors live edits in GSuite. It will enable a collaborative ecosystem for everyone to have a voice in the data conversation from modeling to analysis.
The continued rise of the machine
Apache Spark, Beam, Flink, and other coding frameworks such as PySpark represent the growth of software development to meet the needs of data exploration and the ability to handle the sheer volume of data inputs that are available today. Companies such as Databricks have grown around these philosophies, extending the use of Spark into use case-specific workspaces and workloads, with other notable companies such as Google and Snowflake’s Data Cloud following suit.
Organizations are increasingly leveraging these tools, often in tandem, as they race to be the first to own the competitive advantage that is automated processing of this data and its downstream impacts. This has widespread impacts to every business unit, with a few listed below:
- Marketing can better determine who to reach out to based on existing customer and company firmographics and their engagement with marketing channels
- Product can determine what area(s) in its product could use improving and when to suggest features to which audience(s)
- Sales can cut through the noise of which opportunities will result in more successfully committed revenue by understanding high-performing user actions
- Customer success can better identify signals that lead to customer dissatisfaction and mitigate churn or encourage growth instead
Data Forward In 2022
In 2022 and beyond, companies will be prioritizing data analytics as an essential business function, correctly identifying it as a must-have for business intelligence, product development, and customer happiness.
Check out our live webinar on February 16, 2022, to learn more about the latest and greatest trends and predictions in analytics and business intelligence.
Sigma and Fivetran are here to help leading companies navigate the future of data analytics and business intelligence. To see how we can empower your business in 2022 and beyond, visit www.sigmacomputing.com and www.fivetran.com.
Sigma is the only cloud analytics and business intelligence platform empowering business teams to break free from the confines of the dashboard, explore data for themselves, and make better, faster decisions. The award-winning platform was built to capitalize on the performance power of cloud data warehouses to combine data sources and analyze billions of rows of data instantly via an intuitive, spreadsheet-like interface — no coding required. Sigma automates workflows and balances data access with unparalleled data governance to make self-service data exploration safe for the first time.
Start a free trial today: www.sigmacomputing.com
Fivetran, the global industry leader in modern data integration, powers high volume data movement for the enterprise. Built for the cloud, our mission is to make access to data as simple and reliable as electricity. We enable data teams to effortlessly centralize and transform data from hundreds of SaaS and on-prem data sources into high-performance cloud destinations. Fast-moving startups to the world’s largest companies use Fivetran to accelerate modern analytics and operational efficiency, fueling data-driven business growth.
For more information, visit fivetran.com.