The data landscape is changing faster than we can think, and that creates both opportunities and challenges for companies big and small. Within this context, data-driven development has shifted from being a strategic advantage for a narrow range of scenarios to the operational mainstream.
These days, all kinds of businesses count on sophisticated analytics to inform their decisions of consequence. In this world, leadership now expects just basic metrics but also crucial, actionable insights that can benefit the bottom line from the sheer tidal wave of vast, complex, and undifferentiated datasets. Using technologies like AI to wrestle with these incredible amounts of data is no longer optional: it is mandatory.
The Evolving Landscape of Data-Driven Development
Any organization that is not embracing AI in its analytics is far more likely to become stagnant, if not outright dysfunctional, in a world where agility, accuracy, and innovation are crucial for business survival. And what separates the companies that are merely enjoying the benefits of the latest IT upgrade from those that are able to consistently move ahead of the pack is their capacity to use AI for this kind of far more intelligent decision-making.
At Hitachi Vantara, where I directed the development of applications centered on data and acted in the technical expert capacity, I have seen firsthand the kinds of changes that AI is bringing to the world of development and business. By using advanced analytics, we have been able to realize a 30 percent reduction in time-to-value with respect to our applications.
As we move into a future of development that is driven by data and AI, the pertinent question will no longer be about the priority or necessity of AI-powered advanced analytics, but rather, what is the best way for businesses to use such a powerful tool to ensure that they are out in front of the development and business curve?
Data Analytics in Software Development
The software development industry has relied on basic metrics such as page views, system logs, and error rates to gauge performance for decades. Those primitive forms of data analysis were limited in their scope and usefulness. But as time passed, the industry evolved toward more forms of data analysis, like real-time dashboards and key performance indicators (KPIs), that functioned within a predictive framework. Nowadays, data analysis has taken another step forward and uses artificial intelligence (AI) to generate automated insights that help business leaders shape future performance.
Problems with scalability frequently plagued conventional data analytics. In trying to resolve this issue, teams took the tedious route of attempting to bring the data together from a number of sources. Yet these groups worked with slow, boxed systems that simply could not handle the increasing data volume and variety.
Now that AI is here, and evidently to stay, analytics has moved from being an industry the size of a large office building into something that feels more like a product in a world that is now driven by AI and ML. Analytics is now everywhere, and it is needed everywhere. The same way people need light to see and dark to rest, organizations need analytics to understand what has happened and what will happen next.
The Business Case for AI-Powered Analytics
When analytics is carried out with AI, benefits accrue to businesses at several levels. First, and most obviously, is the speed factor. Second, and of even more importance, is the clarity of payoff: intelligence can now be generated with a level of speed and accuracy that is almost uniformly superior to that which human beings can deliver. This makes the situation even more consequential: The in-the-lab promise of the new technologies is now an on-the-ground, actualized opportunity to secure real, crystallized benefits on the company’s balance sheet.
When it comes to revolutionizing the environment, AI-powered systems have done it on an unprecedented scale. Using machine learning algorithms, AI can now automatically categorize vast quantities of data with very little human intervention. And it’s getting better all the time. These systems learn from user feedback and usage trends to refine their methods of categorization and improve their accuracy.
At Hitachi Vantara, rather than applying AI to the old way of doing things, we automated the classification of personally identifiable information (PII), for example. This eliminated the data analysts from setting rules manually, which led to a direct 30% reduction in times spent discovering and categorizing PII. And that had a direct impact on achieving faster time-to-value.
The financial benefits of this shift are obvious. By using AI-driven automation to take over some data management tasks, we were able to reduce our data management labor costs by a significant amount. That amounted to 8,200 fewer man-hours managing our data, that’s time we could reinvest into more strategic, higher-return projects. But that’s only half of the savings model. The second part of the financial model of AI automation is risk mitigation.
For example, achieving core system optimizations with AI gave a 23x advancement in processing speeds. That means the apps are faster, and the user experience is improved. And it’s not just a one-off thing; using AI to assist in development is steadily becoming a normal part of the development process, not an exception.
Real-World Applications Transforming Development
The real applications of AI in development are vast and varied. One arena in which AI has proven especially transformative is in metadata discovery and categorization. Companies collect datasets of ever-increasing size. Consequently, the task of identifying and classifying the relevant data has become ever more complex, in fact, it’s become a sort of “nice fit” for the application of AI. In one project, we developed a web-based tool to automate the classification of a large dataset. This project gave me the opportunity to understand the potential for AI to speed up development processes and impact the bottom line. Since then, I’ve looked for opportunities to squeeze AI into development workflows.
One of the places where artificial intelligence has significantly seeped in is predictive development lifecycle management. Elucidating historical data from bygone projects, AI can now foretell with much more aplomb the current state of our affairs: the velocity of sprints we’re chugging along at, the resource requests we’re cranking out, the release timelines we’re (not) hitting, etc. This predictive capability is already helping us allocate resources in a more effective manner. And, consequently it’s been helping us mitigate risks better and ensure projects are on course.
Artificial intelligence is changing our code’s quality. With AI-driven tools, development teams are now able to achieve three main things: detect opportunities for refactoring, improve their coding practices and maintain a very high standard of coding. They can also do all of this while developing at a fully sustainable pace, which many are now calling velocity.
Integration Challenges and Solutions
Despite the many clear benefits AI brings to analytics, the integration of this new technology into development workflows is sometimes a hard sell. One reason for this is data fragmentation. AI solutions too often find themselves hamstrung not by their own technological capabilities but by the underperforming legacy systems that were in place long before AI came onto the scene. These systems, by design, keep data in one place and do a great job of making sure that data doesn’t go anywhere else. In other words, they’re really good at being physical. But because the data isn’t virtual, there’s no way to pull together a meaningful volume of it, no way for the AI to do anything truly intelligent.
The Human Element: Teams and Talent in AI-Driven Development
While assuming an increasingly large role in development, artificial intelligence (AI) is simultaneously pushing the emergence of new job roles bridging traditional software engineering and data science. Data scientists, machine learning (ML) engineers, and AI-focused DevOps professionals now operate as vital cogs in the development landscape. But like so many developments in the tech sector, this one is happening at lightning speed, and not all development teams can keep up.
In my experience, working in globally distributed teams, integrating these new roles within the old frameworks of software engineering can be dicey. I like to think of myself as a knowledge maestro, ensuring effective execution of our AI-driven initiatives and, more important, sharing hard-won insights across the many locations where my teams reside.
At the same time, we must address the AI skills gap. Engineers may know development practices inside and out, but AI brings another level of intricacy, and, let’s be honest, another dimension of development, into play. Your engineers must understand far more than just the rudiments of AI in order for us to succeed in what is being called the AI era. They must know, for instance, how to build and manage AI pipelines, how to read model outputs, and how to implement, top to bottom, end-to-end, AI-driven solutions.
A culture driven by data is a prerequisite for the successful adoption of AI in development. It is vital for organizations to ensure that AI is integrated in a way that aligns with their business goals, boosts team productivity, and fosters integrations that are clear, collaborative, and responsible.
Ethical Considerations and Responsible Implementation
Like any technology, the adoption of AI in development brings up some important ethical issues. A major concern is the bias that can exist in AI systems. Organizations must use not just diverse training data, but also diverse and fair assumptions, to build AI models that won’t make biased predictions. In my work, I’ve stressed the importance of using high-quality code and architecture to avoid, in effect, baking bias into our systems.
Another vital matter is privacy, especially when it comes to sensitive user data. It is of utmost importance to maintain trust and avoid any sort of legal fallout. This is why organizations absolutely must follow the law. And the law requires them to comply with certain regulations, like GDPR and HIPAA, for instance. But the law only tells half the story. These organizations also need privacy, for themselves and for their users. It’s not just a matter of maintaining trust. It’s also a matter of maintaining legally-protected secrets.
What’s Next for AI in Development Analytics
Analytics powered by artificial intelligence has the potential to take business intelligence and advanced analytics and transform them into something even more potent. For businesses of all sizes, AI will enable them to gain more powerful insights from their data. Automated processes will make sense of the incredible volumes of information that they generate every day. With the constant evolution of AI, it is being integrated into the CI/CD pipeline. This allows for the kind of real-time as well as automated feedback that must exist during the development cycle. The more that these tools become a natural part of the development process, the better the insights that teams will be able to get from them.
The Unavoidable Shift Toward AI-Powered Analytics
No longer is AI just a term; it is the baseline for modern scalable development. When companies pour AI into their development workflows, they do so because they want to gain an appreciable advantage in today’s optimizations, innovations, and differentiations that make up the competitive landscape.
And if you’re still on the fence, now is the time to decide. Companies have the option of adopting AI in their analytics to totally reform their development processes, get insights that were previously unreachable, and create value almost everywhere you look. The future is already here, and AI is at the heart of it.
