Scan any business or tech headline right now, and you’re likely to see artificial intelligence (AI) and machine learning (ML), and more specifically the rising niches of GPT and LLMs (generative pre-trained transformer and large language models). GPT and LLMs distill data and return content in natural language, whether as longform narrative, auto-populated answers to questions, or even imagery or videos, all at super-fast speeds. While there’s still much to sort through on what these technologies mean for business, tech, politics, ethics, and more, one thing is clear—they’re breaking new ground for data.

AI, GPT, and LLMs live and die by data. They analyze it, learn from it, and create it, both leveraging and adding to its already explosive growth. And right now, businesses are generating, accumulating, and retaining mountains of it—and spending a considerable amount of money to do so. But to what end?

According to IDC, “Despite spending $290 billion globally on technology and services to increase decision velocity, 42 percent of enterprises report that data is underutilized in their organizations,” and a recent IDC Global Data Sphere predicts that by 2026, seven petabytes of data will be created every second. Boston Consulting Group says the comprehensive costs around data are equally staggering, as “spending on data-related software, services, and hardware—which already amounts to about half a trillion dollars globally—is expected to double in the next five years.”

It’s time to put all that juicy data you’ve collected to work, and investing in AI technologies can help you get there. While GPT and LLM solutions are gaining a reputation for what they can create, they’re also being put into practice for DataOps practices and analytics solutions that can help you make sense of all that data in the first place. Today’s data is so complex that organizations cannot unravel it without the power of AI.

As I covered in my previous blog, DataOps is all about getting your arms around your data by improving data quality, gaining better business insights, and expanding innovation and cloud efficiency. AI and AI-derived technologies can help on all three fronts.

AI can be used to collate, contextualize, and analyze your hard-won proprietary data and then help you use it to learn about your business and your customers. With AI combing through data, you can uncover new insights that were previously inconceivable even a few years ago—and make informed decisions about which data is no longer needed, still missing, needs more details, and so on. From there, that data can be used to train GPT and LLM tools that advance and expand your business and become the targeted solutions and services your customers crave.

The Eckerson Group recently polled data practitioners on LinkedIn and discovered that 43 percent already use LLMs to assist data engineering. In a second poll, 54 percent said they use ChatGPT to help write documentation, 18 percent use it for designing and building pipelines, and another 18 percent are using it to learn new techniques.

Sitting on a mountain of data gets you nowhere if you don’t know what’s in it. With data accumulations surpassing our capacity to sort through, understand, quantify, and qualify it, investing in AI/ML technologies is the way forward. These technologies can help you dig into all that data and yield valuable insights to better understand your business, discover where to expand or change course, identify new opportunities, and ultimately deliver the solutions your customers and stakeholders want.

Making the most of GPT and LLMs relies on a solid data management foundation enabled by the people, process, and technology shifts of a DataOps strategy and methodology. Learn more about how organizations are yielding value from data in Profitable Outcomes Linked to Data-Driven Maturity, a BMC-commissioned study by 451 Research, part of S&P Global Market Intelligence.

Businesses have been on a data collection kick for a while now, and it’s no surprise since IDC says we’ll generate around 221 zettabytes of data by 2026. But if your goal is to turn all that data into insights, where do you start? Do you know what you have? Is it the right data? And, most importantly, is it yielding value for your business?

We commissioned 451 Research, part of S&P Global Market Intelligence, to survey 1,100 IT and data professionals from diverse global regions about what they want from their data, and the challenges they’re facing in achieving those goals. The findings are out now in Profitable Outcomes Linked to Data-Driven Maturity.

The survey revealed a handful of common issues that are impeding progress as businesses try to gather and present a unified view of their data. Among them:

• Meeting the streaming or real-time requirements needed to support data collection from 24×7 business models and Internet of Things devices
• Lack of automation, and a reliance on manual processes and legacy solutions
• Data quality issues with collecting inaccurate and out-of-date information
• Data silos and lack of system interoperability

Additionally, respondents said they need help determining the usability, trustworthiness, and quality of the information they’ve been gathering—and continue to gather—to maximize and optimize that data. If the data is incomplete or incorrect, an organization loses not only the time and effort required to gather and store it in the first place—it also puts itself at risk of noncompliance issues and strategic missteps that damage the bottom line.

Ensuring that you’re gathering the right data, and putting it to good use, requires a tool that can deliver a unified view. Automated capabilities are key to saving time and toil related to data processing, reducing errors, and delivering real-time visibility anytime from anywhere. BMC’s application workflow orchestration solutions, Control-M and BMC Helix Control-M, can help organizations optimize the data they’ve worked so hard to collect, and yield the most value from it.

Control-M simplifies application and data workflow orchestration on-premises or as a service. It makes it easy to build, define, schedule, manage, and monitor production workflows, ensuring visibility and reliability and improving service level agreements (SLAs). BMC Helix Control-M is a software-as-a-service (SaaS)-based solution that integrates, automates, and orchestrates complex data and application workflows across highly heterogeneous technology environments.

Both solutions support the implementation of DataOps, which applies agile engineering and DevOps best practices to the field of data management to better organize, analyze, and leverage data and unlock business value. With DataOps, DevOps teams, data engineers, data scientists, and analytics teams collaborate to collect and implement data-driven business insights.

Automating and orchestrating data pipelines with tools like Control-M and BMC Helix Control-M is integral to DataOps, and can help you yield value from your data and drive better business outcomes by:

• Improving data quality: Once guardrails are in place to identify, collate, and analyze data, you’ll get a better sense of the data you have—and what you still need.
• Gaining better business insights: Now that you’re collecting and analyzing the data you want—and not cluttering it with the data you don’t—it’s an easier task to leverage that information for targeted, revenue-generating activities.
• Expanding innovation and cloud efficiency: With the cost savings achieved through data orchestration and better data processes, you can redirect spend toward innovation initiatives (informed by those very same data insights) that help grow the business.

You can read the full report, Profitable Outcomes Linked to Data-Driven Maturity, here. Visit bmc.com/controlm to learn more about Control-M and bmc.com/helixcontrolm to learn about BMC Helix Control-M.

Customer satisfaction can make or break your business. So, are you collecting and using relevant data to drive meaningful change for your customers’ interactions with your business? We wanted to find out how companies are using—and maximizing—their data to yield value, so we commissioned 451 Research, part of S&P Global Market Intelligence, to survey 1,100 IT and data professionals from diverse global regions. Those finding have just been released in a new report, Profitable Outcomes Linked to Data-Driven Maturity.

Supporting the customer experience is becoming a key focus in the contemporary use of enterprise data, and strong data practices are integral to delivering a Transcendent Customer Experience, one of the tenets of the Autonomous Digital Enterprise, that meets customers where, when, and how they want to be met, providing customer engagement and satisfaction that lead to long-term business profitability.

Fifty-five percent of survey respondents are focused on improving their customer satisfaction levels through the effective use of data. In an increasingly, pervasively online world, the report asserts that failing to capture and understand the context of data derived from customer interactions via digital channels and data-driven mediums “is akin to leaving money on the table.” Over the next 24 months, one-fifth of survey respondents expect customer satisfaction to be the single area of most significant improvement in their data strategy evolution.

The types of data gathered from and about customers can be used to inform and influence different aspects of their overall experience, empowering businesses to:

• Personalize offerings tailored to specific customer profiles, preferences, and previous purchases
• Identify and correct service issues through customer surveys and self-service solutions
• Forecast and respond to trends, adjusting supply chains to meet customer demand
• Incentivize customers with loyalty and rewards programs based on engagement and purchases

Seventy percent of those surveyed said they were highly effective or mostly effective at leveraging data-driven insights for customer-facing processes such as onboarding and signups, while 74 percent said they were highly effective or mostly effective using those insights to help ensure customer service (finding products, placing orders, providing delivery status, etc.).

To make data useful to the business, organizations must be able to have a unified view of their data, as well as automated tools and processes to better manage and organize it; verify its quality; analyze its usefulness; and ensure that it flows to the right place at the right time for faster decision making. The right mix of people, processes, and technology is essential to ensure a democratized data culture and develop true data maturity.

To do this, organizations must take a holistic, enterprise-wide view of their data assets and activity, implementing a DataOps methodology that applies agile and automated approaches to data management to support data-driven business outcomes and leverages appropriate supporting technology to optimize business processes and people. DataOps represents a culture and technology shift. Among organizations with a more mature DataOps strategy, 77 percent indicated that their organization’s use of data has had a most significant impact to date on customer satisfaction, versus 65 percent among total respondents.

To learn more about how DataOps and data maturity can help organizations deliver a Transcendent Customer Experience and tangible benefits of data maturity across the business, visit bmc.com/valueofdata.