Data analytics is the backbone of a modern data stack in today’s information-driven market. Data helps businesses optimize their services and products to assuage consumers better and outbid their competition. Hence, corporations can leverage emerging tech such as cloud-based SaaS/PaaS services to augment their data operations stack.
But, what is a data stack? What requirements must an enterprise prioritize when planning for its data stack? How should businesses act on the information gathered, when the intent is to adapt to the market’s changing needs?
Read on to get some relevant answers to these pertinent questions.
What’s a Modern Data Stack?
A data stack is the tooling framework used to serve the processing needs of your enterprise’s data operations. Further on, this data stack is a subset of the enterprise’s technological stack. Various specialized tools contribute to the data needs of different organizational departments.
A recent survey by a leading analytics PaaS provider has determined that 63% of data analysts failed to act on the market demands in an agile fashion.
Hence, the modern data stack used by enterprises aims at low-code/no-code data tooling, and multi-cloud tools to streamline various data operations, and assimilate AI/ML for predictive analytics.
To avail an integrated data stack, businesses need to understand and act on the roles played by the data stack, platform, and infrastructure in their strategy.
Data Infrastructure vs. Data Stack vs. Data Platform
In the world of data, there are three standard terms you would encounter frequently. These are data stacks, data platforms, and data infrastructure. Here’s what you need to know about these three terms:
- Data Stack: A set of tools comes in handy for an organization for data CRUD, analytics, report generation, management, and accessibility. The stack comes equipped with specific technical requirements that need to appease all departments’ needs.
- Data Platforms: The data platform helps implement each tool in an infrastructural role. The data platform provides technologies that connect these tools, the services required to avail those technologies, etc. The data integration platform also offers blueprints for infrastructural utilization.
- Data Infrastructure: The data infrastructure empowers the data platform to streamline your data stack in synergy. The infrastructure enables networking between your data repository, ETL pipeline, and data destinations (analytics tools, APIs, etc.).
How Is Data Stack Evolving?
The evolution of a data stack is not sudden, and the modern data stack is evolving from its traditional counterparts, with the introduction of new-age technology.
Here are some noteworthy points worth a mention:
1. Transitions From On-Premise to Cloud-Integration
On-premises data stacks prevent data fragmentation. But, businesses need to think of cloud-integrated data repositories, especially if they want to cut costs and introduce agility in data logistics.
Cloud-based data warehousing enables data mobility while making a cloud data stack’s working performance seamless. Additionally, scalability is effortless when you are dependent on cloud-virtualized infrastructure.
2. The Journey From ETL to ELT
In the past, on-premise warehousing and disorganized data pipelines were huge hindrances to scalability. The biggest challenges are data silos, duplication, and scrub. Subsequently, dependency on row-based RDBMSs poses another problem for data integrity.
Extract-Transform-Load (ETL) strategy tends to work when the data structure is simple, and the volume’s manageable. However, once the enterprise scales upwards and begins to produce petabyte-level data, data engineers fail to write queries that successfully mobilize data-driven operations.
Businesses continue to face challenges with duplicate versions of data assets and fragmented data pieces spread across different repositories. To overcome duplication issues, businesses need to start using cloud data warehouses to write simple queries that perform powerful data analytics on large volumes of data.
Modern cloud warehouses enable ETL operations where data engineers can comfortably write transformation jobs before loading the data. The ETL process accelerates the speed at which structured data reaches analysts.
3. Moving From Self-Served Analytics to a Democratized Data Exploration
Technical know-how goes a long way in strengthening your data engineering processes. However, businesses must not make it a prerogative for data handling. Companies couldn’t act on data in time in the past, given the paucity of technical knowledge and skillsets. But, low-code/no-code data stacks push the limits of what business teams can do with their data.
Employees in various departments can get hands-on experience with data visualizations and solutions to tackle real-time challenges with integrated data services.
Components of a Modern Data Stack
You can take a cue from the phrase data stack itself for compartmentalizing your company’s modern data stack. Think of it as componential layers stacked on top of each other to constitute the whole stack of data operations and technologies.
As such, you can consider the following definite layers to a modern data stack:
1. Data Source
Realistically, businesses have to add omnichannel, disparate data sources in their data stack. Technically, a data source can be:
- A database
- A flat file
- Live data from IoT devices
- Scraped web data from user sessions
- Static and streaming application data
Databases, data warehouses, and data lakes continue to be preferred storage options for many enterprises. Ease of access and storage make these an ideal choice for businesses looking to run various analytical and cloud-based data tools for data integration.
2. Data Ingestion
The ingestion process essentially deals with consuming structured or unstructured data. This data is hauled to the repositories and storage locations for further usage. As a process, data ingestion requires careful implementation as businesses have to account for multivariate data sources, as they continue to scale up.
These sources channel data at various speeds; some process in batch, while others work on the ingested data in real-time. Either way, data repositories are equipped to deal with structured, semi-structured, and unstructured data.
Sometimes, all three data types are ingested; these can later be cleaned and transformed for future usage.
3. Data Storage Solutions
The storage solution is an added feature of your ingestion layer. Once the data is ingested, it is sent to the various storage solutions.
These storage solutions can range from a warehouse, to a localized server, or even a data lake. Premium integrated data services offer packaged solutions based on business size and industry. Some common options include: RedShift, Amazon Web Services, Snowflake, Microsoft Azure, amongst others.
4. Data Transformation and Modelling
If you are sourcing differently structured data (semi-structured or unstructured), then you want to transform it into a uniform standard format before further processing. Cleansing the data helps model it into a legible, user-friendly format that helps make more accurate inferences than raw data.
Data transformations are an essential aspect of any given data asset. In its raw form, organizations will feel themselves falling short of making well-informed decisions, as ineligible data is of no use in day-to-day processing. After all, you don’t want your employees to keep making guesses on the data and what it represents.
On the contrary, with business intelligence tools and other analytical software, raw data can be manipulated and converted to fuel strategic, financial and business relevant decisions. Some notable companies in this regards are dbt and Dataform, who have established their names in the field of data modelling.
5. Data Analytics
Data analytics helps organizations visualize the aggregated data for gaining insightful patterns. The more performant your data sourcing, the faster and easier it is to gather advanced inferences and representations of your data. Data analytics is applicable for long-term business goals and for driving day-to-day operations.
Kloudio, as an organization, facilitates the data connections, right from the source to the end destination. After all, analytics is a major aspect of connecting your data repositories, running business intelligence processes on raw data and converting it into reports, visualizations and meaningful insights.
6. Data Operationalization
Since the collected data is transformed with actionable BI available at your disposal, you need to make it accessible to your team or third-party vendors.
Data availability and accessibility are two different aspects that are often used interchangeably. Nonetheless, the former deals with making data available for business operations as and when needed. On the other hand, the latter talks about having the right level of access and restricting it to the people who need it.
The term data operationalization talks about the concept of Reverse ETL wherein data is extracted, transformed before being loaded into the frontline systems for use by data analysts, employees, and other related parties. Some companies include the likes of Census and Hightouch, who are reputed in the domain of data operationalization.
Why You Should Consider a Modern Data Stack?
A modernized data stack allows your business to do more by spending less in terms of time, money, and effort. Data allows your business to grow; as your business grows, so does the volume of data your business tools are equipped to handle.
Hence, a modernized data stack enterprise infrastructure can reap listless benefits down the line. Data scientists, entrepreneurs, and CTOs must work with the following core stack principles to gain maximum benefits:
1. Automate Your Data Integration
Cloud automation is the only way to work through massive heaps of data. Modern organizations store their data within data warehouses and lakes.
During the ingestion process, it’s impossible to transform, load, and analyze data manually. There is a scope for errors in the ingestion, collection, and transformation layer, even when dealing with smaller volumes.
This is where the process of automation comes into the picture. You can free up your resources and dedicate them to creating more complex queries and visualizations that help draw meaningful conclusions from raw data through automation.
2. On-Premise vs. Cloud Data Warehouses
Fragmented data poses a challenge to data agility in more ways than one. In a broader sense, it also hinders governance and security.
However, the best solution is to use cloud-based data warehouses to:
- Prevent fragmentation
- Decentralize a central data repo for remote accessibility
- Allow your team to rely on the same KPI values and business intelligence
- Accelerate your data pipeline and make it capable of ingesting, analyzing, and reporting real-time data.
Cloud-integrated is one of the most sensible solutions for compiling a modern data stack’s infrastructure planning.
3. Innovations in Data Transformation Tools
Technological innovations in contemporary data stacks will usher trends like:
- Use of data mobility aids that improve data logistics and visibility is a good investment
- Install custom stack frameworks for leveraging multi-cloud tools
- Use of predictive, AI/ML-powered analytics for actionable decision-making, timely DataOps, MLOps, and Platform Ops scaling
- Powerful analytics tool means lesser time and cost spent in data transformation and actualization
4. Make Data Actionable
There is no point in employing sophisticated data models and premium-grade tools unless they offer intelligence for end-to-end business transformation/optimization.
Actionability means that your employees should be able to customize your organizational data stack, models, process, analytics as and when required.
Agility is critical for action, and for that, you need to utilize DevOps with low-code and no-code reporting tools, cloud automation tools, and business analytics.
Related: Low Code/No-Code Explained
5. Shrink Latency Time
A modern data stack should be capable enough to prevent operational latency, to act on the correct data at the right time. As per surveys, 68% and 54% of brands do not orchestrate data governance and security well enough to enhance operational agility.
The primary purpose of a well-equipped modern data stack is to understand the pain areas of data-driven organizations and make best use of the weak spots to convert them into solid, efficient regions.
How Does Kloudio Help With Your Modern Data Workstack Goals?
Kloudio’s products are aimed towards helping organizations make the most out of their data driven goals. By connecting low-code and no-code tools, it’s possible to connect multiple data sources, facilitate reporting, automate your ETL processes, and even auto-scheduling your reporting to make the most out of your day-to-day operational processes.
If you are short on time, or on skills, rest assured, Kloudio’s suite of automation, and data centric tools will come to your rescue, to help you make the most of your work tasks, in a matter of minutes.
Create your free account here.