A world where everything is connected to the internet; if we go back a little over a couple of decades, this idea would have sounded like science fiction. But, in 2020, there were 9.7 billion devices connected to the internet. So, that idea is already a reality, yet, it is still only the beginning. By 2030 the number of connected devices is expected to reach 29.4 billion.
This explosion in connected devices can only mean one thing, a corresponding explosion in data being generated. Cloud, IoT devices, applications, software, hardware, endpoints, models, infrastructures, and everything under the big wide world today, have either begun generating data or using it.
By 2023, global data creation is projected to reach 120 zettabytes. By 2025, the data generated is expected to cross 180 zettabytes.
With the sheer volume of incoming data, businesses need a 360-degree modern data strategy that is cloud-based, modular, customized, and metadata-driven. They need to create teams that excel in data optimization and prepare and budget for the changing data environment. And the first step towards that is accounting for what’s in store for the coming year. So, here are 5 data trends your business needs to keep track of for 2023.
One of the biggest challenges with data is to clean, manage, and store it properly. Although businesses have invested heavily in it over the years, with more and more workloads and data moving to the cloud, the cost of data engineering is expected to grow substantially in the coming years. Yes, there are data lake and warehouse vendors to help, but they also need to bring down increasing churn rates as consumption grows.
The process is expected to soar in costs, and businesses will need expert guidance for strategy and management of data. This is why maximizing data value while bringing solutions to optimize the costs in the process will take center stage in 2023. We expect to see data engineering teams trying to increase savings, revenues, and finding ways to monetize data while increasing efficiency and boosting customer value.
At this point, data warehouses and data lakes have become necessities for most organizations. But the process of working with them comes with its challenges.
With businesses wanting to apply Artificial Intelligence (AI) at scale, data lakes still lacked the basic functionalities businesses need. They also lagged in implementing governance and ACID (atomicity, consistency, isolation, and durability) compliance. Above all, when businesses use extract, transform, and load solutions to migrate data from a data lake to a warehouse, the process becomes expensive and time-consuming, with the chance of data loss.
This is where open-source Data Lakehouses can make a world of difference by blurring the lines between lakes and warehouses and blending both. They help convert a data lake into a warehouse, negating the need to migrate or move the data. This will allow businesses to leverage massive amounts of data for their Machine Learning (ML) models and applications.
Data lakehouses would offer businesses flexibility, performance, compliance, and security by bringing down silos with a unified approach to data architecture.
Data quality has always been a critical concern of data teams, specifically unforeseen schema changes. An update reaches a service while the downstream data systems struggle to cope, leading to production run time issues and breaks in the data flow.
With data increasingly being treated as products, monitoring changes in distributed architectures becoming more complex, and increased coupling, the chances of problems arising lie pretty high
This chaos is what data contracts, which define the serialization of each parameter or return type, will work to avoid. As a formal agreement between a service and a client, data contracts work similarly to service-level agreements (SLAs), guaranteeing interface compatibility. They list out how the data can be used, version numbers, delivery of data quality, availability, error rates, uptime needed, and depreciation.
Although the concept is still nascent, we can expect to see businesses adopting data contracts in 2023 at an early stage since they will play a critical role in pipeline execution, validation, schemas, establishing rules for data loss, and interoperability.
Data maturity defines the amount of value a business derives from data. This value comes from deeply entrenching data into decision-making processes, making it the differentiating factor between businesses.
As investments in terms of time, cost, resources, and solutions to manage data increase, businesses will move to boost their data maturity in 2023. Businesses will need to assess how they fare on the four pillars of strategy, culture, organization, and capability while boosting ways to increase data-based decision-making processes into the mix.
As businesses boost their data maturity, we will also see businesses reap the benefits of the efforts with increased efficiency, returns, and reduced costs. The goal will be to increase reliance on data and derive insights that matter with the right tools, infrastructure, and talent in place. This will also lay the groundwork for businesses to establish a more substantial unity between the data on legacy on-premises and the cloud.
With the data world evolving by the second, businesses need the right talent by their side to bring in the proper modernization, migration, and computing strategies that garner results. They need to upgrade their competencies while retraining and upskilling their teams to work with the changing infrastructures, environments, and stacks. They need guidance on the solutions they should adopt and what trends businesses should adopt to fit their vision for the future.
We will see businesses moving towards hiring more data experts in 2023, along with more retraining activities. This can be an expensive affair amidst a dearth of the right talent, but this is where businesses can leverage the expertise of end-to-end partners, who have expertise in how they should progress and have expert teams.
We at Cambridge Technology have been priding ourselves on this with expert teams with relevant competencies in end-to-end data engineering.
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