Data in Action

Cloud-based data output overwhelms human management capacity, according to a Dynatrace survey of 1,300 CIOs and other senior IT leaders. 

More than 4 in 5 respondents said the technology stack has become more complex in the past 12 months. Half expect the complexity to worsen.

Multicloud technology estates include a dozen different platforms and services, on average. The rising complexity hinders security and customer experience, according to the majority of respondents.

Data powers enterprise decisions, but managing the influx of information has long been a challenge in IT management.

The problem is one of missed opportunities, according to Dynatrace CTO Bernd Greifeneder.

Without the ability to turn data into insights, teams are slow to spot IT and operations issues, delaying responses, Greifeneder said in a release accompanying the study.

Manual approaches to log management and analytics won't keep up with the volume of data modern multicloud implementations pump out, according to 4 in 5 respondents. 

Running a data- and analytics-driven business is one of seven major components to high-performing IT operations, according to a Forrester report published in February. An effective data strategy depends on strategy, people, process, data and technology, according to the firm. 

"It all starts with C-level commitment to invest in these capabilities and clearly communicate the mandates to the rest of the organization," Forrester said. "High performance also requires balancing governance and agility via proper organizational role alignment and responsibilities and leveraging democratized low-code (but governed) development of analytical applications."

Despite interest in generative AI, most CDOs say their companies have not made changes to their company's data infrastructure to support the new technology, according to an AWS study.

Data quality, use case fit and a lack of guardrails top the list of key obstacles standing between companies and widespread adoption of generative AI, according to the survey of 334 CDOs, conducted in partnership with the International MIT Chief Data Officer and Information Quality Symposium.

Generative AI adoption is still far from pervasive, with just 6% of executives reporting their organizations have one or more use cases in production.

Generative AI won't make a major dent in next year's IT budgets. However, executives do have plans to bolster their spending on data systems that underpin the technology.

Nearly three-quarters of CIOs say their organizations will increase spending on cloud platforms in 2024, according to Gartner data. A similar proportion will increase spending on data analytics and business intelligence tools used for model training.

Overall IT spending will grow 8% in 2024, Gartner projects, surpassing $5 trillion for the first time in the company's annual forecast. 

While Gartner attributed the increase to deferred 2023 project spending and rising vendor costs, the trend also also reflects a desire for modernization.

Nearly half of CDOs say their role focuses on data management initiatives, including the enhancement of data infrastructure for specific analytics and AI use cases. 

The major cloud providers — AWS, Google Cloud and Microsoft Azure — have spent the past year pitching products and services that can help with that transformation. So far, adoption trends suggest enterprise leaders are more likely to leverage generative AI within services they already consume.

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By Fullstory

For companies today, effectively harnessing and leveraging data is crucial for staying ahead. However, becoming a data-driven organization takes work. With it comes a set of hurdles that without thoughtful planning and prioritization, can lead to missed opportunities, weakening the impact of data on decision-making and growth.

In this article, we'll discuss common challenges faced in data management that businesses need to correct to capitalize on their data.

Perfecting data collection

Effective data collection isn’t about amassing vast amounts of information but strategically gathering high-quality, relevant, and actionable data that aligns with and informs your business strategies. This approach involves using appropriate tools to ensure each data piece contributes to informed decision-making and aligns with organizational goals.

Overcoming data silos

Data silos block the free flow of information critical to organizational success. Addressing this issue requires more than technology; it demands a culture of collaboration and openness. Integrating data management systems and promoting open communication can transform isolated data into a unified strategic asset.

By fostering cross-departmental collaboration and using technology for data integration, organizations can facilitate a seamless data ecosystem. This approach unlocks insights, improves decision-making, and enhances efficiencies, ensuring your data drives progress.

Expert advice "Prioritize transparency and shared objectives, and leverage technology that supports data accessibility for all. By breaking down these barriers, we not only ensure consistent information used by all functions within the organization, but also unlock the full potential of our collective data, driving more informed decision-making that paints the full customer experience picture and is actionable. This ultimately drives innovative solutions across the enterprise." Nancy Combs Papa Johns | VP, Digital at Papa Johns International

Ensuring data quality

The foundation of trust in data-driven insights lies in the data's integrity. It's crucial to have a solid framework for data governance and quality control to guarantee that the data is accurate, complete, and consistent.

Your business can take significant steps toward minimizing errors by implementing regular data audits, adopting stringent validation processes, and setting standardized data entry and maintenance protocols. These measures build trust in data-driven decisions, ensuring that every step forward is informed by the highest quality data available.

Fostering data literacy

Data literacy is essential not only for data scientists but across all organizational levels. Creating comprehensive training programs and resources to improve employees' grasp of data principles, analytical methods, and how data can be practically applied in decision-making is vital.

Initiatives like these make sure everyone is equipped to actively participate in a data-driven culture. This approach doesn't just enhance individual skills—it cultivates an environment ripe for innovation and strategic insights, enabling everyone to contribute significantly.

Optimizing organizational design

The structure of an organization plays a pivotal role in its ability to harness data effectively. Crafting an org design that promotes efficient data flow, fosters collaboration across teams, and aligns perfectly with strategic ambitions is not just important—it's essential.
 

Creating that structure could involve:

• Establishing specialized data teams.
• Ensuring data roles are integrated throughout various departments.
• Implementing flexible methodologies to improve responsiveness and adaptability.

The objective is to develop a blueprint that supports and enhances data-driven initiatives, enabling data use at every level.

Expert advice “Consider the decision about Org Design as derivative from your overall definition of success for how you want Data to support and drive your business. Be aware that you will likely have competing definitions of success from your leadership team, so driving towards clarity and consensus on that is critically important. Not only does Org Design include how to determine the degree of centralization vs decentralization of your resources, but it should also address the consequential question of where your Data Org reports in the organization (i.e., to the CEO? To the CFO? COO? CTO?). Each option will offer varied points of emphasis, investments, and outcomes.” Bill Saltmarsh Children’s Mercy - Kansas City | VP, Chief Data Officer

Cultivating a data-driven culture and buy-in

Embracing a data-driven approach demands a transformation in an organization's culture and mindset. Leadership is at the forefront of this change, advocating for the importance of data-informed decisions and nurturing an atmosphere where trial, learning, and insights from data are valued.

By acknowledging and rewarding achievements rooted in data, leaders can ignite a passion and dedication to data initiatives, weaving data into the very fabric of the organizational identity. This step is crucial in building a culture where data is not just an asset but a core principle.

Not sure which hurdle your business should tackle first? Explore Fullstory's assessment guide to identify where your team can make an immediate impact on the road to data excellence.

L'Oréal CEO Nicolas Hieronimus sees AI playing a key role in driving growth for the company in the year ahead, he said speaking during the company’s Q4 2023 earnings call. 

To reach sustainability goals, the company plans to use AI and its “10,000 terabytes of beauty data” to help its researchers identify new solutions and kickstart creativity, Hieronimus said.

“It goes without saying that AI now transcends many of the things we do,” Hieronimus said.  

L'Oréal has high hopes for what AI and expanded access to data insights will do for the business. 

The company’s data platform houses 10 petabytes of data, which teams use to power “all types of AI models, including the latest LLMs,” Hieronimus said during a keynote at CES in January. 

L'Oréal plans to use AI to expand data-driven insights, boost researcher creativity and speed up work. 

“All of it is bringing data in because this new world we’re in is a world where we’re going to be leveraging AI and we need data to fuel AI,” Hieronimus said during the call. 

AI-related plans are part of nearly every organization’s strategy. Estée Lauder Companies explored various generative AI use cases submitted by employees last year. The cosmetics giant was also an early adopter of ChatGPT Enterprise. P&G worked with OpenAI’s technology as well last year, rolling out an internal generative AI tool built using OpenAI’s API. 

But not all enterprises have taken the steps to put their data to work. 

Most chief data officers admit to making no changes to their company’s data infrastructure to support generative AI, an AWS study published in October found. 

Bad data will make bad models, so tech leaders must prioritize infrastructure upgrades. CIOs and IT leaders expect data and AI budgets to grow this year as enterprises work to clear adoption roadblocks, according to an MIT Technology Review survey of 600 tech leaders.

IT team project requests rose by 39% over the last year, according to Mulesoft. The Salesforce-owned integration software vendor surveyed 1,050 tech decision-makers for its annual connectivity benchmark report. 

Skills gaps and compliance concerns are slowing progress, the survey found. Four in 5 respondents said siloed data impeded digital transformation and 3 in 5 respondents reported their data systems are not yet configured to fully leverage emerging AI capabilities.

Organizations are asking more of IT to drive digital transformation, deploy generative AI tools and implement large language models, according to Param Kahlon, EVP and GM of automation and integration at Salesforce.

Data operations and insights are a consistent enterprise priority, but generative AI has placed a keener focus on feeding new sources of unstructured data into the growing pipeline.

As CIOs prioritize upgrades, IT teams are grappling with siloed data and ecosystems rife with complexity.

“Expecting organizations to have one unified data source for all of their customer, product and financial data sets is unrealistic,” Kahlon told CIO Dive. “It's just not going to happen.”

Enterprise aspirations have outgrown data stacks.

As vendors expand cloud-based AI marketplaces, IT leaders anticipate the number of LLMs they use to increase steadily over the next three years, growing by nearly 70%, Mulesoft found. Four in 5 respondents said their organization already uses multiple predictive and generative AI models.

Talent to support more robust data operations remains hard to source. Lack of tech skills was the top AI implementation concern for organizations, according to an MIT Technology Review report commissioned by Databricks.

Despite talent woes, Salesforce has seen a jump in what organizations expect from IT teams, Kahlon said.

“Companies are basically saying that transformation and technology are going to change the way [they] do business,” said Kahlon. “IT is seeing a lot of demands come in from the business to do more projects.”

More than half of enterprise leaders say unstructured data mostly stays in a silo, and less than half of information actually gets shared between employees or systems, according to IDC data. The report, sponsored by Box, surveyed 400 IT leaders. 

For 2 in 5 enterprise leaders, the majority of the data their company stores is used only once, then left unaccessed. 

Idle data brings higher costs, according to the analysis. Costs associated with security breaches double for companies with more unstructured data, IDC found.

Data fuels IT ambitions. That includes the recent generative AI wave and more traditional digital tools, such as personalized recommendation engines in e-commerce or critical financial forecasts. 

Without proper data to fuel analytics systems, organizations won't reap the full benefits of their IT estate. This effort includes finding ways to properly structure data and put it to use.

IDC found companies that used their unstructured data in the past 12 months cited improved customer satisfaction, data governance and regulatory compliance, among other positive outcomes, according to the study.

Enterprise reliance on SaaS and other digital solutions led to the proliferation of data sources. Migration to the cloud drove the availability of data past the analytics tools of the average company, according to a Coleman Parkes study published last year.

Nearly 3 in 4 leaders said data management was a challenge, Coleman Parkes found, and almost all executives struggled to extend observability and security across environments.

Accenture anticipates generative AI adoption will accelerate enterprise modernization and boost the IT services and consulting firm’s revenue growth, according to Chair and CEO Julie Sweet, speaking during the company’s Q4 2023 earnings call.

“We estimate that only 40% of workloads are in the cloud today, only one-third of clients have modernized their ERP platforms, and less than 10% have what we define as mature data and AI capabilities,” Sweet said.

Adoption will require infrastructure enhancements, according to Sweet.

“Gen AI is an amazing technology,” she said. “It's going to do great things. And what I tell all my clients: can't use it unless you're in the cloud, have data and you modernize your core.”

Accenture felt the sting of slowing growth as customers trimmed discretionary spend and paced tech investments in a year clouded by macroeconomic uncertainty.

The company saw revenue growth contract to 4% year over year in the final quarter of its fiscal year, a far cry from the previous year’s 15% increase. Revenue for managed services grew 10% year over year, offsetting a 2% decline in consulting.

As mixed economic signals persist, the tech industry has turned to generative AI to spark revenue growth.

Accenture has already found clients eager to adopt the technology, according to Sweet.

“We have projects across all our industries, with banking, public service, consumer goods and utilities leading in activity,” Sweet said. “Clients are doing a variety of different types of work, from strategy and use case implementations; to tech enablement; to scaling; to model customization, tuning and training; to talent and responsible AI.”

Earlier this year, Accenture committed $3 billion to its AI practice and it currently has roughly 300 projects underway, according to Sweet.

The company’s AI-related sales doubled quarter-over-quarter, to approximately $200 million in Q4, up from $100 million in the prior four months, Sweet said.

Accenture is also on the way to doubling its ranks of skilled AI practitioners to 80,000 and has trained 600,000 employees in AI fundamentals, according to Sweet. The company employs 733,000 people in 120 countries according to its corporate fact sheet.

Sweet sees strong demand for enterprise migration and modernization services as a harbinger of a coming wave.

“While still in the early stages, gen AI technology is maturing rapidly, and we believe it will be a significant source of value for us and our clients over time,” Sweet said.

With engineers testing potential use cases and leadership erecting guardrails, IT teams are busy laying a foundation for the data-hungry, compute-heavy technology.

“Implementing Gen AI is not easy,” said Sweet. “Entire environments need to be set up.”

CIOs have doubled down on modernizing enterprise data systems to ease AI adoption, as the computing demands of the technology pose a challenge to adoption ambitions. 

Most technology executives expect their organizations to increase investments in data infrastructure and AI adoption over the next year, according to an MIT Technology Review report commissioned by Databricks. All 600 CIOs and IT leaders surveyed in the report said data and AI budgets will grow over the next year, and nearly half anticipate a boost of more than 25%.

The initial barrier to entry for generative AI adoption is low, but the barrier to productionize the technology remains high, Naveen Zutshi, CIO at Databricks, said. “Right now, when digging deeper, you find more experiments and less production use cases,” Zutshi said.

As eager organizations greenlight pilot programs and task innovation teams with developing generative AI business applications, CIOs are refining the fuel that drives the emerging technology — data.

Despite efforts to streamline architecture, unlock silos and integrate disparate sources, data ecosystems often succumb to entropy over time.

“It's not that organizations want to be complex,” Zutshi said. “They become complex because there were legacy systems and there were decisions made over time that resulted in that complexity. Unwinding is not easy.”

Hybridizing AI strategy

From a data standpoint, experimenting with off-the-shelf models available via cloud marketplaces and SaaS solutions is a relatively easy lift. But most organizations favor a hybrid AI strategy, leveraging available tools for immediate use cases while tuning and training models on internal data, the report found.

More than half of respondents — 58% — said they are open to both building and buying the technology. While only slightly more than 1 in 10 are solely focused on engineering their own tools, just 3 in 10 were content with commercial models.

Databricks has teams building foundational models for customers. The company is also leveraging available technologies for enterprise use cases, primarily for internal chatbots to assist in customer support, contract reviews and job descriptions, according to Zutshi.

“Building a foundation model costs hundreds of thousands or millions of dollars and only a small number of companies will do it,” said Zutshi.

Most organizations will take the middle path, using internal data to tune off-the-shelf models for specific tasks. The primary lever for feeding an LLM external data, a process known as retrieval-augmented generation, creates risk and opportunity.

“Companies have a lot of unstructured data stored somewhere that they do very little with,” Zutshi said, pointing to information stored across platforms such as Confluence and Google Drive or in PDF form.

Generative AI can ingest data from these sources, surfacing previously buried insights. But most organizations are new to the idea of using unstructured data without first putting it in a structured database, so there will be a learning curve, Zutshi said.

Data first

Laying the foundation for generative AI requires upfront investment.

“Just moving landlocked data can be a pretty gargantuan exercise,” Zutshi said. “These problems don’t go away because you have generative AI.”

Even organizations that have been relatively proactive in data operations can require remediation, as technologies and strategies have evolved rapidly.

“Data infrastructure that worked five years ago doesn’t work now,” Murali Brahmadesam, CTO and head of engineering at financial services company Razorpay, said in the report.

Zutshi said he had a data warehouse coupled with on-prem databases and a cloud-based data lake five years ago, when he served as CIO at Palo Alto Networks. Data duplication and separate access rules for data science and analytics teams contributed to operational complexity.

“Until a few years ago, we weren’t talking about lakehouses,” said Zutshi. “We were using data warehouses and they tended to put data in separate silos. We built systems based on that paradigm and now that paradigm has changed.”

Incremental migration and integrations that show value to the business can remove resistance.

“If I told my CEO that I was going to spend the next 18 months fixing my data foundation, but there won’t be any immediate returns, I would be laughed out of the room,” said Zutshi.

Enthusiasm for generative AI may buy CIOs some time. According to a KPMG survey of 1,300 CEOs, most expect ROI to arrive in three to five years, not tomorrow or the next day.

CIOs are worried about the accuracy of AI-generated information as pressure mounts to deploy the technology, according to a Juniper Networks survey in partnership with Wakefield Research.

Nearly 9 in 10 tech leaders believe it may not be possible to know if their company’s AI output is accurate, the survey of 1,000 global executives involved with AI at their organization found. 

The vast majority of respondents, 91%, say employees trust AI more than they should. More than three-quarters of executives expect the increase in AI deployments will lead to employees taking on more responsibility, according to the data.  

AI is making waves across enterprises as deployments accelerate, and the disruption will impact employee workflows, for better or worse. 

While businesses are lured by prospective productivity boosts and seamless user experiences, tech leaders will need to underline the importance of understanding, and mitigating, AI risks. 

When it comes to accuracy, many generative AI tools have been trained on data and information found on the internet, which can have drawbacks. The wealth of knowledge provides these tools with information on copious amounts of topics, but the quality of that information isn’t always up to par. 

Business leaders have to identify these risks for employees and train them on how to move forward while using generative AI. These tools have a tendency to hallucinate, for example, requiring employees to operate with a healthy level of skepticism when assessing generated outputs.

But mitigation techniques don’t have to stop at fact-checking. Businesses can work to ground generated responses in accurate information by training solutions on in-house data, which is what Juniper Networks does, the report said. 

Enterprises also know that the performance of generative AI solutions can change. Monitoring tools can give CIOs a sense of whether a tool’s accuracy is degrading over time and alert employees when necessary.

Ballooning data costs afflict most companies, according to a Wakefield Research survey of 200 DevOps and Site Reliability Engineering professionals commissioned by Edge Delta.

Nearly all respondents said their organization encountered data cost overruns at least a few times per year and over half experienced overages or unexpected spending spikes monthly. The firm also surveyed 300 observability, Kubernetes and CX application professionals for the study.

Despite the business value of analytics, organizations often have to purge data. Nearly all respondents admitted their organization discards data to cut costs and more than 4 in 5 said they commonly limit log ingestion for the same purpose. Nearly one-third acknowledged data is discarded randomly by their company.

Data is the lifeblood of business, but too much of even a good thing can be toxic.

Nearly 4 in 5 respondents admitted disputes arose within their company over data ingestion and the same number said they’re paying too much for observability solutions even after imposing limitations.

The scope of the problem is reflected in the rate of data estate expansion.

Log data for the average company has expanded by a factor of five in the last three years, the report found. More than one-third of respondents said their company generates up to 1TB of data daily. An additional 37% generate more than 1TB each day.

As mining tools improved, sources proliferated and cloud unlocked a practically bottomless well of storage, enterprises turned to observability solutions and FinOps practices to rationalize usage and reduce cost.

“The area that businesses were most focused was on how to drive more value from data.” Naveen Zutshi, CIO at data software company Databricks, told CIO Dive earlier this year. “Now, they are struggling with operational complexity.”

Data analysts, data engineers and data scientists each use discrete tools and different production pathways, Zutshi said, which can lead to a disconnected data ecosystem. Tagging by function ties ownership to workgroups and gives IT leaders more clarity into spending, he said.

While greater visibility into data operations helps organizations determine who’s using what and how much it costs, observability solutions bring additional headaches. More than 9 in 10 respondents said their leadership team is concerned about rising observability costs and anticipates greater spending scrutiny by their organization over the next year.

Ultimately, solutions require investments, too, and more than 4 in 5 respondents said ROI on observability platforms has not kept pace with spending.