Organizations need to take a deep look at their entire data stack to determine whether all solutions offer functionality, efficiency, and accuracy, or whether there is room to consolidate them into a single, customizable system.
The latest data stack is broken. On a global level, the average organization uses 130 different software applications. With so many technologies available, data management can quickly become complex. Building data management tools in-house can be costly, time-consuming, and take your organization away from its core mission. It’s usually easier to outsource, but as your organization grows, so does your data. Business leaders suddenly have a plethora of software solutions for different aspects of data management, but not all of them work together seamlessly.
It’s time for data management professionals to question the status quo and explore different approaches to data management. Today’s organizations need a common data structure to easily represent all types of data, and a unified and consistent set of tools to manipulate that data efficiently and accurately.
A brief history of data management
Data management has been an integral part of business since the 1960s, but back then it was a much simpler endeavor. The original method of data management involved on-premises solutions running on mainframes, and for the next 40 years the market was owned by a few major companies: Oracle, IBM, and Microsoft.
By the start of the millennium, the growing popularity of data management in the cloud exposed the shortcomings of on-premises data stacks, including higher maintenance costs, lack of users with access to data, and lack of processing power. During the 2000s and 2010s, organizations experienced rapid growth in the variety and volume of data sources, as well as the need to better manage, analyze, and organize data.
Enter the modern data stack. Today’s businesses typically have some cloud-based database and management tools to accommodate expanding datasets. Whether your organization needs to scale, increase velocity, or focus efforts on improving the quality of data-backed insights, your organization can do whatever it takes to tackle everything on your wishlist. Access hundreds of solutions. More than $10 billion will be spent on data-as-a-service tools in 2023 as companies seek solutions to add to their technology stacks.
See also: Streaming plays a key role in the data stack
Why modern data stack stopped working
The world is now entering a new era of data management. Large technology stacks no longer work. It’s becoming more expensive and complex to keep adding to the stack to improve workload efficiency and data quality.
Modern data stacks are highly complex, using multiple tools and platforms. From cataloging to governance to access control, some “new” tool comes to market every quarter that reinvents the wheel. Additionally, every time a new tool is added to the stack, licensing fees increase and data engineers are hired or retrained each time a new solution is used, increasing the total cost of ownership.
Every new addition to the stack is intended to be modular, but the result is a disjointed system that creates more data silos. As a result, collaboration between data scientists, analysts, and product owners is inhibited. They often don’t work on the same platform, processes vary between teams, handoffs lack context, and communication plummets. Large organizations of data engineers and experts currently oversee data initiatives for enterprise organizations, but these initiatives take months and business users and decision makers are not as forthcoming as promised. have not realized the impact or breakthrough insights of
Then there are security and governance issues. Many tools involve a lot of data transfer between teams and silos. It becomes virtually impossible to know who has access to different datasets or whether they have access rights. should You can access it.
The past few years have given rise to advances in artificial intelligence (AI), machine learning (ML), and their generative AI and large-scale language models (LLM). These rely on huge datasets of so-called unstructured data such as text. , files, images. Unfortunately, traditional data architectures were not designed to handle LLM, AI, and ML, requiring investments in specialized multimodal data management solutions that go beyond simple tables and tabular databases.
Tables lack the flexibility needed to properly structure non-traditional data such as images and ML embeddings. However, the well-intentioned but unintended consequence is that organizations flock to bespoke solutions (for images, for vectors), and the list goes on. They congratulate themselves on narrow performance benchmarks and optimizations, but they lose sight of the fact that yet another data silo exists within the organization and increased complexity is now the real impediment to insight.
Over time, more solutions are added to the stack, addressing specific problems individually, creating more data silos, requiring more management oversight, and additional governance and compliance enforcement. Masu. Building and maintaining internal infrastructure is costly, and with that comes the challenge of attracting and retaining talent.
It’s time for database vendors to rethink how they build database systems and strive to do so with strategic intent.
Solution: Unified data model
There are two solutions for fixing the modern data stack. First, by adopting a flexible, unified data model that addresses today’s architectural challenges; second, by having a single platform that unifies all data, compute, and code platforms into a single solution. It’s about hiring.
A viable integrated data model can be built around: multidimensional arrayThis provides organizations with a single system to store all their data and integrate priority catalogs, resource provisioning, governance, and more just once, regardless of the use case.
The second is the need for a unified data platform. For example, to avoid rebuilding separate infrastructure for data coding and storage, organizations have the opportunity to use the same system for coding and storage, which also reuses the same governance and compliance models. Ultimately, this merger will improve cost efficiency and performance by eliminating the need for engineers to replicate and preprocess the same data across multiple systems.
In 2024 and beyond, organizations will be taking a thorough look at their entire data stack to determine whether all these solutions offer functionality, efficiency, and accuracy, or whether there is room to consolidate them into a single, customizable system. You need to judge. However, this problem should not be placed on the shoulders of the end user, but rather on the shoulders of the software vendor who has the ability to create integrated solutions for his customers.
For organizations looking to reduce costs, increase productivity, and simplify operations: Your data infrastructure doesn’t have to be complex, and data management solutions exist to make your life easier. today.
