Modern Data Stack
With so much data going in and out of your organization, analysts need to have a set of technologies in place that can manage and analyze it. A modern data stack can help you make the most of data to drive your decisions and strategies.
Let’s discover what makes up a modern data stack and why it's so important for companies and organizations today.
What Is A Modern Data Stack?
A modern data stack is a combination of software tools and platforms that allow analysts to collect, store, analyze, and visualize data in a streamlined manner. Unlike traditional data setups, which were often difficult, time-consuming to create, and inefficient, modern stacks are designed to be more flexible, scalable, and easier to use.
Modern Data Stack Layers and Examples
The data stack typically includes at least five key layers, each with its own set of tools or platforms. Each one is crucial in the process of data management and analysis.
Data sources
Every company generates data from a variety of sources, including websites (with tools such as Google Analytics and Mixpanel), databases (such as Postgres and MongoDB), and SaaS products (like Salesforce, HubSpot, and Stripe). Often, event stream products (such as Segment) can be used to capture relevant events, such as interactions with an online store, advertising, or combinations of relevant activities.
Data integration
A modern data stack often has a common layer that connects to and gathers data from these different sources. Tools such as Fivetran or Airbyte are commonly used since they provide hundreds of pre-built connectors to also help extract and load data from the source into a data warehouse. These tools typically batch this activity at regular intervals or schedules, but other solutions, such as Apache Kafka, can also stream data directly into the warehouse in real time.
Data storage and querying
Once collected, this data needs a place to live, allowing you to access it easily when you need it. This is where data warehouses come into play. Cloud-based platforms like Snowflake, Google BigQuery, or Amazon Redshift provide secure storage and quick access to the data when needed.
These solutions can also contain cloud-based storage solutions that are cost-effective and scalable. Amazon's S3 or Google Cloud Storage, for instance, are popular choices for storing large amounts of data of up to several petabytes. In addition to storage, the data warehouses can be directly queried using SQL and used for analytics purposes.
Data transformation
Once data is securely stored in a warehouse, the next step is to make sense of it. At this stage, the raw data is then processed and transformed into a more appropriate and consistent format to use in data applications. This might involve cleaning up the data, filtering it, or joining/merging it with other data sources. Here, open-source tools such as dbt or Apache Airflow are commonly used. Additional capabilities, such as testing and monitoring of data quality, data governance, and access control, can also be applied at this level.
Data visualization and analysis
With the data in a useful format, organizations can then analyze it to gain insights, identify patterns, and make predictions.
Visualizing data through graphs, charts, and other visual formats makes it easier for stakeholders to understand and interpret. Products such as Klipfolio's PowerMetrics can transform complex data into visually appealing and easy-to-understand charts, graphs, and other visual formats, making it easier to read and interpret. Advanced analysis can also be applied, such as normal range, forecasting, and outlier detection.
Advantages of a Modern Data Stack
As your business grows, it generates more and more data. Understanding the benefits of a modern data stack will help you see why it's widely adopted in various industries. Let's take a closer look at these advantages:
Efficiency
In the past, managing data involved many steps that could slow down data flow. The modern data stack streamlines these processes.
With this system, data moves smoothly from collection to insights. This improves efficiency and allows your business to make data-driven decisions faster and respond quickly to changes or opportunities.
Scalability
Traditional data management systems often struggle to handle increased data volumes without delays or high costs. With a data stack built on cloud infrastructure, your organization can easily handle large data sets without experiencing slowdowns or bottlenecks as your data needs grow.
Flexibility
Data tools and platforms offer a vast selection and are constantly evolving. Fortunately, a modern data stack offers the ability to integrate with a wide range of tools. This flexibility ensures that your business isn’t locked into one solution.
Cost-effectiveness
Maintaining on-premises servers or data centers can be expensive. As such, analysts have to consider the hardware costs and the manpower needed for data maintenance. A modern data stack is typically built using cloud solutions and pay-as-you-go models. Instead of high upfront costs, you can pay based on your actual usage and scale up or down according to your current needs.
Real-time analysis
With customer behavior and trends changing in the blink of an eye, waiting for insights can result in missed opportunities for your business. Data stacks recognize this and offer many tools that provide real-time data processing and analysis capabilities. This allows you to react to real-time data promptly to make timely and relevant decisions.
Factors to Consider When Building a Modern Data Stack
Building a modern data stack can help analysts make the most of your data. However, with so many tools and services available, it can take time to figure out where to start and what to prioritize.
That said, here are five important factors to consider so that you can create a data stack that fits your business's needs perfectly:
Business needs
Every business is unique, which means your data needs are unique too. Before diving into tools and platforms, take a step back and think about what you want to achieve with your data. Are you looking to understand customer behavior, optimize your operations, or forecast future trends? You can select the tools and services that align with your goals by pinpointing your goals.
It's also important to think about the future. While your immediate data needs are crucial, consider what data challenges your business might face in the upcoming years and plan accordingly.
Security and Compliance
One of the top concerns when dealing with data is security. You should make sure that the tools and platforms you choose follow the best security practices.
Besides, certain industries have specific regulations about data handling. For instance, healthcare organizations must adhere to HIPAA regulations, while companies dealing with European consumers need to be GDPR compliant. Make sure you find a tool and platform that complies with these regulations to avoid legal complications.
Additionally, reviewing and updating security measures regularly can help prevent data breaches and keep the integrity and confidentiality of your data. Being proactive and setting up reliable data security measures like encryption and two-factor authentication can strengthen your data defense.
Scalability
Data can grow quickly. Today, you might be dealing with hundreds of data points, but in a year, it could be thousands or even millions. That’s why you must choose tools and services that can grow with your business. Prioritizing scalability ensures that your systems won't become slow or unresponsive as your data needs increase.
User-friendliness
A data stack should be easy to use. If it's too complex, you might find it hard to get the best out of it, and your team might struggle with it too. Thus, look for platforms and tools that have intuitive interfaces and provide good support documentation. This way, even if someone is new to the tool, they can get up to speed quickly.
Remember: time spent trying to figure out how a tool works is time away from drawing meaningful insights from your data.
Cost and budget
While it's tempting to go for the most advanced tools, you must always consider your budget. Assess the cost of each data tool and weigh it against its features and the benefits it brings.
By the way, take note that expensive doesn't always mean better! Sometimes, a more affordable option might offer what you need without the extra functionalities.
Data Stack Backup and Disaster Recovery
Losing important organizational documents can be costly for your business. That’s why you should also invest in backup solutions and disaster recovery plans, which act as safety nets to keep data secure and accessible. Understanding different backup options allows us to make informed decisions to protect your data.
Cloud backup services
Cloud services store your data off-site in data centers. You can access your backed-up data from anywhere with an internet connection. They often have automatic backup settings and reduce the risk of losing data due to local issues, too. Many data warehouse solutions have integrated backup or replication available to help with this.
Just be sure to choose a reputable provider with strong security measures for better data protection. Dropbox, Google Drive, and Microsoft OneDrive are some reliable companies that offer these services.
Network-Attached Storage (NAS)
A NAS is a device connected to a network that allows multiple users and devices to access and store data from a central location. It acts like a private cloud.
NAS units can have multiple hard drives, which allow mirrored backups for extra protection. Setting up a NAS requires some initial setup but offers a reliable backup system with regular maintenance.
Limitations of a Modern Data Stack
There’s no doubt that data stacks can offer many benefits, but it's also important to understand its limitations. Below are some challenges to consider.
Complexity
Even though modern data stacks are made to be user-friendly, they come with many parts that can sometimes be tricky to understand and set up. Companies might have to spend more time and effort to get the system and platform working right and without problems.
Plus, there’s always a learning curve when introducing new things. Provide regular training sessions and step-by-step guides to help your teams understand and use the platform better.
Cost
Modern data stacks can save money over time, but you need substantial upfront capital to get started. For smaller organizations or those just starting, this big expense at the beginning might be too much.
Nonetheless, it’s best to set up a budget and evaluate the return on investment before implementing a modern data stack. You can also look for cost-effective options or even open-source tools that aren’t as expensive but still serve the purpose.
Security
With so much activity online, there's always the risk of data theft or unauthorized access. As such, your company needs to have strong security in place to keep its data safe.
Conduct regular security checks and updates to make sure your data stack is always secure. You may also consider getting security certifications, updating your platform, and keeping your teams in the know about best practices in data safety.
Dependence on external providers
Modern data stacks use tools and services from different companies. Relying on these companies means that if they face problems or make changes, it can affect the company's data work.
Thus, make sure to plan for local backups for important things like your financial data. Communicating with service providers can also help in understanding some potential risks or changes ahead of time.
Modernizing Sales Analytics with AI-Powered Tools
Modern data analytics tools, combined with AI-powered analytics, are continuously transforming the way sales teams operate. Here's a closer look at how these tools are shaping the future of managing sales data.
Self-service analytics
One major shift in the sales data analytics landscape is the rise of self-service analytics platforms. These empower users to gain insights into their business data without relying on a dedicated data team. Tools like Klipfolio allow sales representatives to dive into their data and fine-tune their strategies.
AI-powered analytics
AI and machine learning are no longer just buzzwords surrounding data. They've become integral components of modern sales platforms. AI-powered analytics tools can predict sales trends, identify potential leads, and even automate some parts of the sales process. Examples of these tools are Salesforce Einstein and Zoho CRM, which leverage AI to provide more in-depth, predictive analytics for your data.
Feature store and MLOps
As sales analysts rely more on ML models, the need for a feature store and robust MLOps practices becomes evident. These things ensure that data models are trained, deployed, and monitored effectively.
Tecton and Feast are examples of feature stores that enable easy sharing and serving of features for ML models. On the other hand, platforms like MLflow and Kubeflow provide capabilities for data version control, model tracking, and deployment.
Legacy data stack vs. modern data stack
While some organizations still rely on a legacy data stack, the shift towards a modern data stack has become more crucial. These modern tools pave the way for faster data integration, better governance, and more efficient analytics.
Data catalog
With the increasing amount of data that a company needs to handle, you must make sure that your data is always accurate, reliable, and secure. Tools such as Alation and Collibra that prioritize organizing data, collecting metadata, searching, and data management capabilities make sure that the company's data is consistently compliant and suited for a variety of intended uses and downstream applications.
Cloud data integration
Tools such as Stitch and Talend facilitate data ingestion from various sources into a centralized data warehouse. They guarantee that sales teams always have access to all the data they need to perform better.
Having your data on the cloud means that you can always scale according to your business's needs. If the amount of data you generate is growing, you can easily purchase more storage. Likewise, if you need less space, you can opt for a smaller premium.
Reinvent Your Data Management Strategies
Data will continue to play a big role in the digital age, so organizations need to prioritize data management strategies that work. With a data stack, backup, and disaster recovery measures, you can store data securely and use it to make strategic decisions and succeed.
Learn to invest in the right tools, put in robust recovery protocols, and give your team the skills and knowledge they need to handle your data properly.
FAQs
What is the primary difference between a traditional and a modern data stack?
Traditional data stacks were often on-premises and lacked the flexibility and scalability of cloud-based data stacks. Data stacks, on the other hand, are designed to adjust as your needs change. They allow organizations and businesses to store and manage data without being tied to physical servers or locations.
This difference doesn't just bring about ease in managing data but also in accessing it from anywhere. Thus, the modern data stack provides teams with greater collaboration opportunities.
Are there any industries that benefit more from a modern data stack?
Almost every industry can benefit from a modern data stack, especially those that carry large amounts of data, such as e-commerce, finance, healthcare, and tech companies.
Retail, for example, taps into customer behavior data to improve their sales strategies. Meanwhile, in healthcare, a modern data stack can help in tracking patient health records more efficiently. As a result, they can provide better patient care.
Essentially, any industry looking to derive insights from their data to improve operations can see significant benefits.
How often should I update or modify my data stack?
This depends on your specific needs and how often the trends evolve in your industry. However, regularly reviewing and updating your tools will make sure that you're making the most of the latest features and security updates.
Monitor the performance of your data stack and consider feedback from users. You can also schedule periodic checks, such as quarterly or biannually, to get into the habit of auditing your data system. This way, you stay ahead of potential issues and can maximize new features that could benefit your business.
Is transitioning to a modern data stack disruptive to current operations?
Transitioning can be smooth with proper planning and execution. We recommend having a phased approach to reduce disruptions. While the initial stages might require some adjustments, such as training sessions for your team or temporary changes in workflow, the long-term benefits often outweigh these short-term challenges.
It's also helpful to involve all relevant stakeholders in the transition process. This way, everyone understands the changes and the reasons behind them.