|Why Handsfree Data Is The Future of Tech Efficiency|
|Written by Harry Wilson|
|Thursday, 10 November 2022|
In our age of automating absolutely everything based on data, why not take that one step further and automate data itself? In this article, we’ll discuss building a handsfree data system harnesing AI, allowing for data management infrastructure to run on its own.
The world is now more data-driven than ever. While once a status chiseled out only for companies that were listening, you’d now have a tough time finding a business that doesn’t pride itself on a data-first approach. Yet, for the most part, there is still a fairly staggering gap between the final analysis and the early collection of data.
Even the most flawless data pipeline has to undergo construction, taking time and resources to perfect. From data profiling to meeting regulatory requirements, data has to jump through a lot of hoops before it’s able to be put to work. With all of these mandatory, yet increasingly mundane steps around cleaning and structuring data, it’s a shock that there is still such a high level of human engagement in the process.
Data itself should now be subject to automation and that is the idea explored in this article.
Why Has Data Become So Hard to Manage?
Every single day, over 2.5 quintillion bytes of data is produced. As we further shift into our digital age, this isn’t a figure that’s going to trail off any time soon. IoT is expanding, data use is expanding, and the network from which we can collect data is equally growing. From all angles, companies are inundated with new data.
That’s not to mention the external data that we actively seek out. For many industries, public data archives or industry repositories make up a vital part of the data that we collect. To provide a comprehensive overview to companies that pride themselves on being data-driven, there are a million different data sets and points to juggle at once.
The sheer volume of data a typical engineer has to handle has made manual management nearly impossible. That’s not to mention the slower legacy systems that many engineers have to put up with.
No matter how many times we’ve suggested this to our bosses, they still haven’t made a full leap to cloud services. The reality of having to work with a slower on-site system decrease the efficiency with which data engineers can handle company data. Instead of instant access and continuous insights, there are delays.
What Have Businesses Attempted To Do About This?
Globally, we’re not saying that the data industry hasn’t progressed or moved in the last decade. That’s actually pretty much the opposite of what’s happened. Data is now more accessible than ever, with businesses around the world investing in data infrastructure. You’d be hard-pressed to find a business that doesn't at least have a cloud data warehouse or access to a cloud data lake.
Even more modern inventions, like a data mesh, are allowing software teams to achieve a level of flexibility and scalability around their data infrastructure that was not possible before. But to engage efficiently with all these different iterations of data management, businesses often have to have access to high-quality data without fail.
Frustratingly, considering the vast majority of data is unstructured, there is still no analysis solution quite as simple as SQL and structured data. In order to obtain this level of efficiency across the board, not just with highly structured data, we need to innovate our current processes.
If your business is currently using hand-coding to create data pipelines between your collection sources as data management solution(s), then this manual process is likely to bottleneck your progress. To improve the efficiency of integrating data into businesses, we need to automate as much of the process as possible.
That’s where AI-driven data management platforms come in, providing a solution that allows businesses to become truly powered by their data. Instead of waiting on manual processes or working through repetitive tasks, turning to a handsfree data solution can further increase the efficiency of a business.
How Do We Turn to an Automatic Data System?
Manual processes, across the entire interaction with data, is the single biggest bottleneck to progress. From limiting the speed of scalability to placing barriers on how quickly a business can produce data insight, manual work is slowing down the entire industry.
With the proliferation of AI tools, more and more developers are turning toward automated data ingestion, helping to push for more data agility. To ensure a flawless system, two main things need to be possible: data proliferation to ensure data quality and limitless scalability.
Running a data management process with AI is all about building a completely trusted environment. Once we construct a system that delivers data on time that’s already been through the several quality assurance stages, we can then connect it to our established live analysis tools.
On the client or customer-facing side, using AI tools to automatically take structured data and turn it into insights is not uncommon. If we scale our offering to cover the initial point of contact with data, we can automate the entire pipeline.
This new era of data management is driven by AI and machine learning. Across the whole process, touching on data preparation and ingestion all the way to governance and analysis, AI tools can streamline our interaction with data. Instead of engineers battling an ever-rising tide of new data, they simply need to look for a way to harness the power of AI.
Once complete, this AI-driven data system will allow engineers to process greater volumes of data and compute a greater degree of structures, all without engaging in manual processes. Simply put, AI development is the future of this industry.
AI and machine learning have now permeated almost every industry in the world. From finance and healthcare to manufacturing and aviation, automated tools are streamlining workflows and increasing efficiency. Most of the thought around these innovations has been about the second half of how we deal with data - how it’s interpreted and used.
Yet, if we expand the reach of AI tools to cover the first half, also touching on the collection, profiling, and structuring, we will be able to automate the entire data pipeline. Removing manual tasks will allow for handsfree data to become the main driving force of data itself. Across every business that data is used in, this process will streamline offerings and allow engineers to get more from their data.
With AI, scalability, efficiency, and complete automation over the data pipeline is possible.
Snowflake Improves Developer Support
To be informed about new articles on I Programmer, sign up for our weekly newsletter, subscribe to the RSS feed and follow us on Twitter, Facebook or Linkedin.
or email your comment to: firstname.lastname@example.org
|Last Updated ( Thursday, 10 November 2022 )|