Why Migrate To The Modern Data Stack And Where To Start
Migrating to the modern data stack means poising your organization to meet the varying demands of modern data. The modern data stack seeks to overcome the problems and challenges associated with modern data while enabling an organization to innovate and automate like never before.
As more organizations migrate to the modern data stack and move their data and analytics into the cloud, the benefits are becoming clear. More professionals are being turned into highly efficient and effective data engineers as elastic workloads introduce flexibility and lower barriers to entry. Meanwhile, time and cost savings are obvious as modular design and automation remove bottlenecks.
This new wave of modern data and analytics isn’t going anywhere. So, as an organization interested in making the move, it’s essential to ask yourself: Should you take the first step? Let’s explore the key players and prime reasons that will help you get there.
The Key Players in Data Stack Modernization
Data analysts, scientists and engineers are often considered the “three big roles” in the modern data stack, but they are not the only players. Here’s a look at the responsibilities and essential skills each of these roles brings to the table, along with an examination of yet another key player: analytics engineers.
Data Analysts
Data analysts take data and turn it into answers. By extrapolating information from a company’s data bank, data analysts can bring new insights to the table that can fuel successful business decisions. Some of the responsibilities of a data analyst including cleaning data, analyzing data and creating data visualizations.
Other titles for a data analyst include business analyst, business intelligence analyst, database analyst and operations analyst. Regardless of what you call them, a data analyst fits into countless teams and roles with their core focus being tracking and optimization. Often, the role of a data analyst is viewed as an entry-level role, but mastery of technical tools and effective communication are two critical skills.
Data Scientists
A Data scientist is a specialist with advanced experience in statistics, machine learning models, data visualizations and predictions. The responsibilities of a data scientist include cleaning data, analyzing data and visualizing data, much like a data analyst. However, data scientists have more experience than data analysts, with the additional skills necessary to train and optimize a model for machine learning.
A data scientist can bring incredible value to an organization. They can answer all the same questions as a data analyst, but they can also delve into the more open-ended questions. Their knowledge of statistics and algorithms allows them to explore advanced connections and often understand data from a different perspective. They focus on producing reliable business predictions rather than tracking and optimizing data collection like a data analyst might do.
Data Engineers
A data engineer plays a crucial role in the modern data stack because they build and optimize the crucial systems that enable data analysts and data scientists to do the work they do. Every organization relies on accurate, accessible data, and a data engineer makes sure those boxes are checked. A data engineer controls how data is stored and accessed while also handling data transformation and other critical activities.
Data engineers construct data pipelines using their knowledge of complex tools and methods. A data engineer is well-equipped to handle data even at the largest scale. Unlike data analysts and data scientists, a data engineer will have a skill set that more mimics that of a software professional or developer.
The focus of a data engineer will vary from one organization to the next, but their main function is to handle the infrastructure so that the data analyst and/or data scientist can focus on analytics rather than data storage and movement.
Analytics Engineers
An analytics engineer forms the bridge between the data engineer that builds the infrastructure and the data analyst or data scientist that uses it. The analytics engineer creates and documents datasets that the data analyst or data scientist can use to answer questions.
An analytics engineer has enough technical knowledge to utilize software engineering techniques easily, such as version control, but is also an effective communicator who can teach stakeholders about those techniques and why they’re important.
The focus of an analytics engineer is creating data sets and monitoring and automating them. A small part of the job is also consulting with the data analysts or data scientists to help them clean up their queries and do their job more efficiently.
Reasons to Modernize Your Data Stack
The benefits that the modern data stack (MDS) has to offer are limitless, and that’s why it’s gained momentum so rapidly among growing organizations. Whether you’re already making efforts to adopt business intelligence and more in-depth analytics or you’re working with a completely traditional infrastructure, there are many reasons to make the move.
Automate Your Data Integration
At the core of every modern data stack sits a cloud data platform that’s powerful, scalable and highly elastic. Most often, organizations use a cloud warehouse, but some use data lakes. In either case, data is loaded into the storage facility continuously, and a transformation layer helps convert raw data into sets of data that’s ready to be queried.
From there, a collaborative business intelligence (BI) solution enables powerful data visualizations and interactions that allow data analysts and data scientists to draw meaningful conclusions to guide business decisions. If necessary, you can blend legacy on-premises solutions with cloud-based sources to accommodate your business’s needs. The result is an integrated, automated solution that empowers data analysts and scientists to pull from the most accurate and up-to-date information at all times.
Eliminate the Need for DevOps Resources
Every small organization will find that they eventually need to take on an extensive research project to help them make better decisions and sharpen their competitive edge. Often, these projects require them to tap into multiple data sources (perhaps for the first time) and combine that data into a centralized platform where data professionals can collaborate and report on their findings.
Achieving this collaborative dashboard requires a long list of tools. Most often, the data engineers have to partner with DevOps just to get a working infrastructure model up. Unfortunately, small organizations may lack these resources or find that they don’t have the bandwidth to move as quickly as they wish.
The modern data stack eliminates the need for DevOps resources and allows an organization of any size to get online and start reaping the benefits of business intelligence and advanced analytics.
Agile Data Management
Simply obtaining data and readying it for analysis is often one of the most time-consuming and complicated steps when working with a legacy data stack. A legacy data stack requires slow ETL operations that can cause even a minor change to your query or data to turn into a hair-pulling procedure. The modern data stack brings an end to that frustration.
With the modern data stack, data and the platforms it runs on become agile. Elastic workloads become fast, flexible and easy to work with. Any change, big or small, can be made with ease and efficiency. The result is that your business can do more with its data while spending less time and money getting there.
Reduce Time To Final Data Product
Time to market (TTM) is key to reducing backend costs and achieving efficiency as an organization. For the final data product you’re striving for, whether it’s an answer to inform a mission-critical business decision or a report to fuel product development, reducing TTM is a big deal.
Getting quality answers faster isn’t just a luxury, it’s becoming the standard mode of operation for the world’s leading companies. Cutting back how long it takes to get to the end data product means getting that much farther in front of your competitors. The modern data stack will help your organization step things up and speed things up so that no one is tapping their fingers waiting around for answers.
How to Build a Modern Data Stack
If exploring the many benefits of the modern data stack has you convinced your company is missing out on incredible insights (because of its lack of an MDS), the next logical step is to answer the question: How do you make the move? The perks of migrating to an MDS are clear, but the path to getting there can be much more complex.
The sheer number of tools that comprise a modern data stack can make for a daunting list, but there is nothing to fear. If you follow the best migration practices, making the switch can be surprisingly easy; you just need to start with a well-thought-out plan to ensure your company can pull it off.
Start Simple
Your company can end up incorporating a plethora of tools into its MDS, but that doesn’t mean you need to work from the ground up trying to integrate a dozen tools at the same time. In its simplest form, a modern data stack only requires an ingestion tool, a warehousing tool, a transformation tool, and a business intelligence tool.
To further simplify the matter, countless platforms can fulfill more than one of these roles.
Plan Thoroughly
While you can and should begin with a simplified version of a modern data stack to get the ball rolling and to simplify both migration and adoption, you also don’t want to jump in head-first. Yes, you could save some steps by piecing together the simplest MDS tools out there, but doing so doesn’t make the plan a viable option for your company.
Instead of trying to over-simplify, your company’s primary goal during the adoption process should be to thoroughly understand and plan for all the roadblocks, hurdles, needs, exceptions, and special use cases that are sure to pop up along the way. If you think moving to the modern data stack is daunting, imagine being six months into implementation and realizing you chose the wrong tools. This is a big decision that you must think through.
Choose the Right Partners
At the end of the day, there is no one-size-fits-all approach to adopting a modern data stack. Each company’s plan, tool selections, and timelines will differ based on their size, resources, flexibility, and overall agility.
Modern Data Stack Trends Give Rise to The Applications of AI and Machine Learning
As more organizations make the move to the modern data stack, it’s essential to realize the ever-growing role that machine learning and artificial intelligence are set to play. The trend of AI- and ML-driven predictions is helping to evolve analytics, and it goes hand in hand with the speed and efficiency the modern data stack strives to offer.
Another trend organizations should set their sights on is automation, not just in compiling insights but in executing on them.
Autonomous digital functions have the potential, using the right data, to make systems so reliable that they can take action with little human input. So, how do you get on the path to taking advantage of these trends?
The process begins with evaluating the time you’re currently spending on maintaining workloads and then take some steps to begin automating them. Next, add up the hours your team is spending on maintaining and scaling your databases and seek to move to a cloud solution. After that, consider the difficulty of making changes and upgrades to your data stack, and consider a data build tool to speed development and efficiency.
Consider the advantages that new techniques and tools can bring to your organization. The modular approach, for instance, can help you improve one phase of the data cycle at a time so you can start seeing results more quickly, as opposed to trying to change everything all at once. The last step means understanding your organization’s ability to scale and exploring elastic solutions that will help you go one step further.
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