“Winning Gold three years in a row is a tremendous honor—and a reflection of our team’s relentless innovation and focus on customer success,” said Rick Berger, CEO of Rootstock Software. “This Gold Stevie Award is also a tribute to the results our customers have achieved,” added Berger.

ERP As Digital Nervous System

At the heart of Rootstock’s platform is what it calls the “Signal Chain”—a connected framework across demand, supply, and production capacity. It’s a digital nervous system for manufacturers, enabling them to forecast demand, simulate sourcing changes, and fine-tune production with speed and clarity. In an era of trade tension, supply chain disruption, and economic whiplash, that kind of intelligence isn’t just nice to have—it’s survival gear.

Award judges and industry analysts alike praised Rootstock for its rapid release cycles, tight customer feedback loops, and embedded AI. “Rootstock’s financial and transactional enhancements equip finance and operations teams with precise tools to optimize decision-making in complex manufacturing environments,” one judge noted.

With global manufacturing on track to reach $16.6 trillion in 2025 (World Bank and Statista data) and digital transformation spend expected to top $3.4 trillion by 2026 (International Data Corporation), companies are under pressure to modernize fast. Rootstock’s momentum suggests it’s not just keeping up—it’s helping lead the charge.

What This Means for ERP Insiders

Modernization is no longer optional: The triple Gold win underscores a broader shift: legacy ERP systems are increasingly seen as liabilities. Rootstock’s continued recognition reflects a market shift towards AI-native, cloud-first architectures that minimize technical debt and accelerate deployment timelines. That said, the prevailing direction isn’t necessarily full cloud migration for everyone. The most forward-looking organizations are adopting situationally adaptive hybrid models—balancing public cloud, private cloud, and on-prem infrastructure to meet performance, regulatory, and business continuity needs. ERP leaders still managing fully on-prem or heavily customized systems should reevaluate whether their architectures can support dynamic forecasting, scenario planning, or intelligent automation—all of which are quickly becoming essential capabilities.

AI: from hype to business muscle: Rootstock’s customers are translating AI from buzzword to bottom-line results—specifically through predictive AI used in forecasting and inventory simulation. These capabilities power intelligent workflows that support real-time decision-making across production and supply chain functions. In a world where businesses must respond within hours—not weeks—this form of operational AI is becoming the backbone of agility. According to a Rootstock customer case study shared in 2025, a mid-market electronics manufacturer achieved a 40% reduction in demand planning time and a 15% boost in on-time delivery by leveraging Rootstock’s predictive AI tools to flag sourcing risks before they triggered delays.

Velocity is the new differentiator: Rootstock’s focus on customer success isn’t just anecdotal; it’s operationalized. The company has built an ERP solution that allows manufacturers to pivot rapidly in response to tariffs, labor fluctuations, or global disruptions. For ERP decision-makers, the lesson is clear: speed, adaptability, and user empowerment are today’s true differentiators. Rootstock’s modular, cloud-native design enables faster implementation and continuous configuration—qualities that leading manufacturers now consider essential to staying competitive in a volatile market. Companies embracing this kind of composable, business-led approach are seeing measurable gains in deployment speed and cost efficiency—not in theory, but in practice.

The post Rootstock Grabs Gold—Again: Cloud ERP With Staying Power appeared first on ERP Today.

“What’s new is that you cannot predict your input costs, which is a huge portion of any kind of manufacturing profitability,” Raj Badarinath, Chief Product & Marketing Officer at Rootstock, tells ERP Today.

He notes that while policy and politics are factors beyond direct control, technology offers tangible solutions. “As a modern ERP, we are absolutely keeping our eyes on how can we help manufacturers to track landing costs, manage planning and budgeting, and find alternate local suppliers that bring costs back in line,” he says.

Born in the Cloud, Built for Resilience

A core tenet of Rootstock’s approach is its cloud-native architecture. Unlike many legacy ERP systems retrofitted for the cloud, Rootstock was designed from the ground up on the Salesforce platform. Badarinath argues that this distinction is crucial for agility and resilience.

He shares a stark example: The warehouse of one of Rootstock’s customers, a mid-sized chemical manufacturer, shockingly caught fire during ERP implementation, and everything burned down. While the physical loss was significant, their software transformation remained largely unscathed. “Because they were going with Rootstock, which is cloud-based, they were able to recover with the software transformation that they were going through almost immediately,” Badarinath recalls. “They could use distribution resource planning (DRP) to get back on track within days of the fire.” This real-world crisis highlights that cloud infrastructure is not just a modern trend but a vital component of business continuity.

Being built on the Salesforce platform also provides Rootstock with inherent advantages in configurability and usability. “If anybody’s used Salesforce, guess what? Rootstock ERP is that easy to use,” Badarinath asserts, contrasting it with older systems whose interfaces often resemble outdated technology.

Cutting Through Complexity

In a crowded ERP market, Rootstock differentiates itself with a clear philosophy: unify critical business signals on a single platform. “If you want to bring together demand, supply, and capacity in one platform, it is important that, from an ERP standpoint, all of these are on the same cloud,” Badarinath states. The goal is to minimize complex and failure-prone integrations.

Rootstock calls this unified approach the “Signal Chain.” He explains that manufacturers gain a seamless front-office-to-back-office view by keeping CRM (demand source, often Salesforce itself), ERP (supply and capacity management), and financials on the same platform. Crucially, this integration “sets the foundation for clean, consistent data, which can drive AI,” he says.

Recognizing that manufacturing ERP can be inherently complex, Rootstock is heavily investing in user experience (UX), with new features announced during its Spring ’25 release. “Manufacturing ERP is complex because there hasn’t been a real investment in simplification. But we believe it can be simplified,” Badarinath says, noting that the aim is an intuitive design where a user knows exactly what they are doing with a few clicks.

AI That Matters for Manufacturing

“Rootstock’s annual survey has found that “82% of manufacturers are going to be investing in AI technology in some way,” notes Badarinath. The company is channeling this demand into its “AIRS” (AI from Rootstock) initiative, built upon Salesforce’s Agentforce platform.

In contrast to generic AI, AIRS focuses on specific, high-value manufacturing use cases through dedicated AI agents. “Why not have an agent that actually looks at your entire data set and can determine how to help the business in a much more active way?” Badarinath explains, referencing a potential Tariff Agent that is built into AIRS for manufacturers—other planned agents target engineering, planning, production, and finance.

According to Badarinath, the company’s vision for AIRS incorporates three key concepts:

• Atomic: Modular, composable ERP functions
• Touchless: Voice-based, conversational interaction using large language models (LLMs)
• Agentic: Autonomous, intelligent agents providing foresight and decision support

Together, these concepts “create a touchless ERP paradigm where you can talk to your ERP, and you don’t necessarily need to click on things,” he suggests.

Bridging the Skills Gap with Technology

Perhaps one of the most pressing challenges is the manufacturing labor shortage—“The Evergreen challenge,” as Badarinath calls it, with potentially 2.1 million jobs unfilled. Compounding this is the generational skills gap between experienced plant veterans and tech-savvy newcomers. Badarinath notes that AI, within the Rootstock framework, offers a bridge.

“It’ll be tough to do [reshoring and bridging the gap] without AI,” Badarinath states plainly. With this in mind. Rootstock aims to build AI capabilities that help manufacturers do more with less, capturing veteran knowledge in machine learning models and using AI co-pilots and agents to guide the new workforce. “We want to have the idea of having a conversation with your ERP versus having to have a traditional training process,” he concludes, seeing conversational AI as key to faster onboarding and upskilling.

Finally, by focusing on resilience, integration, intuitive design, and targeted AI, Rootstock aims to equip discrete and project-based manufacturers to not only survive the current volatility but also build a more intelligent, agile, and future-proof operation.

What This Means for ERP Insiders

Market trends favor industry-specific cloud ERPs like Rootstock. Manufacturing is the largest adopter of ERP systems, representing 47% of the market, according to industry reports. Recent studies also indicate that around 65% of organizations opt for cloud-based ERP, with some projections anticipating over 85% of organizations may adopt a cloud-first strategy. Within the cloud, SaaS is a preferred model, and there’s growing demand for industry-specific cloud platforms – with over half of enterprise businesses forecast to rely on these industry clouds by 2028. Rootstock’s focus on complex discrete manufacturers, particularly in verticals like Industrial Equipment & Machinery, High-Tech, Aerospace & Defense, Medical Devices, and project-based environments aligns with these developments. Manufacturers in these sectors are increasingly choosing cloud ERPs built for their specific needs by evaluating vendors based on their vertical expertise and ability to deliver relevant functionality, minimizing customization and leveraging platform strengths like Rootstock’s use of Salesforce.

Ongoing geopolitical uncertainty means tariffs continue to impact input costs. Manufacturers should expect their ERP providers to offer robust tools for accurate landed cost calculation, real-time visibility, scenario planning, and potentially AI-driven alternative sourcing suggestions to overcome these challenges. When considering a hybrid cloud strategy, key decision criteria must include identifying which applications gain most from cloud versus on-prem, ensuring seamless data integration and workflow orchestration between environments, evaluating security protocols across boundaries, and understanding the total cost of ownership, including managing the hybrid complexity. An organization’s ERP strategy must directly address tariff volatility with advanced cost tracking and supply chain visibility tools.

Focus on quantifiable results and futureproofing with AI and integration. Rootstock customers like Matouk, Unionwear and Boston Dynamics have reported specific results such as 98% improvements in inventory accuracy, a 15% increase in on-time delivery rates, and streamlined processes like quote-to-cash, respectively. These examples show that manufacturers must evaluate ERP solutions not just on current features but on their ability to deliver measurable business improvements and their vision for leveraging AI and seamless data integration to address future challenges like labor shortages and increasing operational complexity.

The post Navigating the Manufacturing Maze with Rootstock’s Cloud ERP appeared first on ERP Today.

This move aims to enhance manufacturing, improve supply chain visibility, and bolster reporting capabilities for AIRSYS.

“Selecting Rootstock ERP is a strategic step in the modernization and consolidation of our enterprise solutions, enabling us to effectively scale for exponential growth over the next three to five years,” said Yunshui Chen, CEO of AIRSYS. “We operate in rapidly evolving industries, including AI semiconductors and medical imaging, and need a modern, scalable ERP solution to support our global supply chain. Since Rootstock is built on the same platform as Salesforce Sales Cloud and Propel Software, we can create an interconnected technology stack.”

With 30 years of history rooted in China, AIRSYS has become a global enterprise at the forefront of innovation. The company is expanding into critical areas like AI server cooling, driven by the increasing demand for artificial intelligence technologies.

Previously, AIRSYS relied on a legacy ERP system. However, Rootstock ERP will provide real-time visibility, automate processes, and improve decision-making across AIRSYS’ global operations.

“Rootstock ERP’s modern capabilities and native integration with Salesforce will empower AIRSYS to streamline its operations, scale globally, and continue delivering exceptional value to its customers,” said Rick Berger, CEO of Rootstock Software.

The Rootstock implementation will begin with AIRSYS’ manufacturing operations, targeting a Q1 2025 go-live date. This phase will be integrated with Propel PLM and their existing Salesforce Sales and Service Cloud deployments.

Subsequent phases will migrate AIRSYS’ US operations from their legacy ERP to Rootstock, ensuring global alignment.

Nagarro, the key systems integration partner, will play a critical role in implementing Rootstock.

What This Means for ERP Insiders

AIRSYS will strengthen the customer experience through Salesforce integration.  A 2023 report by Salesforce emphasizes the benefits of connected systems, suggesting that companies with integrated CRM and ERP see double-digit improvements in sales productivity and customer satisfaction. Similar research indicates that best-in-class companies (those with tight CRM-ERP integration) achieve higher customer retention rates and improved sales cycle times. For example, best-in-class companies achieve a 12% greater year-over-year increase in customer satisfaction. Thus, tight integration, like the one Rootstock offers with Salesforce, benefits end users who seek to enhance their customers’ overall experience.

A cloud-native architecture will allow AIRSYS to scale growth.  Studies show that cloud ERP adoption is growing, with a significant percentage of mid-sized and large enterprises moving to cloud-based solutions to support global expansion. SAPinsider research also suggests that cloud ERP deployments can lead to faster time-to-value and reduced IT infrastructure costs, which are crucial for scaling businesses. Moreover, Rootstock is built on the Salesforce platform, a cloud-native environment known for scalability. This architecture allows Rootstock to handle large volumes of data and transactions and to easily add users and resources as a business grows.

Industry-specific functionality is a must for successful ERP implementation.  While AIRSYS’s specific industry needs are unique, the concept of industry-specific ERP is widely recognized. Studies indicate that many ERP implementations fail due to a lack of fit with the specific industry requirements. Rootstock’s flexibility and configurability, allowing for integration with PLM systems like Propel and adaptation to particular manufacturing processes, address this need.

The post AIRSYS selects Rootstock ERP to fuel global growth appeared first on ERP Today.

Big Data is big. To paraphrase the late, great Douglas Adams on space: you just won’t believe how vastly, hugely, mind-bogglingly big it is. And that’s nothing compared to the hype around it. However, like all major technology shifts, paradigms and platforms, innovation and evolution generally consign most elements of IT to legacy status after a while. So, has something changed?

In the era of web-scale everything and the massively parallel processing backbones that span the cloud database era, has the Big Data furor subsided? As the use of multi-cloud computing fabrics now spans every industry vertical and we look forward to a future of data pipelines fueled by AI-enriched filtering, standardization and integration, can we now say that Big Data is dead and that ERP systems spanning this space now represent a bigger entity in, and of, themselves?

Whatever amplifying term we use to describe really large, weighty, voluminous or big quantities of data, the reality is that information will continue to spiral and grow, so the whole notion of Big Data was probably just endemic of the 1990s and our proclivity for snappy nametags in the wake of the dot com bubble.

Beyond the buzz

The next frontier is all about harnessing and exploiting data  – connecting it, analyzing it, injecting it with intelligence, mining it and squeezing every drop of value  – Phil Lewis, Infor

Senior vice president for solution consulting (EMEA and APJ) at Infor, Phil Lewis broadly agrees and says that of course Big Data isn’t actually dead, it just isn’t a buzzword anymore.

“That reality is largely born out of the fact that working with vast quantities of data has become the norm for most companies of any reasonable size. The next frontier is all about harnessing and exploiting data – connecting it, analyzing it, injecting it with intelligence, mining it and squeezing every drop of value it can offer the business,” says Lewis.

With technology organizations in the ERP space, like Infor, now working to apply huge amounts of process intelligence, new strains of RPA and of course the now-ubiquitous application of generative AI to their platform, we can see why the conversation might have elevated beyond “look at all that data” today. It’s clearly time to accept the size of the known universe and work out how to navigate it.

“Faster, more accurate, intelligence-based and increasingly automated decisions will accelerate business performance at the coalface of software application development and data analytics today,” clarifies Infor’s Lewis.

“We should now expect ambitious organizations to thrive and continuously improve via the development and application of hyper-automation tools, processes and functions; all of which will drive productivity, new data-driven processes, AI-driven insights, revenue streams and services unthought of a few years ago. In modern ERP systems and throughout the entire fabric of enterprise software, data-driven decision-making will become instantaneous and will accelerate the way work gets done. When exactly the right information is injected into both the business process and the decision-making process at exactly the right moment, work happens in a more fluid, composed and efficient manner. We all live in an intelligence-driven world and Big Data is still right at the heart of it. Even if we drop the term itself, there’s still a whole lot of bigness out there.”

Dated, but still feted

Big Data was never full equipped to handle the quality of the data. That’s where the combination of ERP and AI comes in – Stu Johnson, Rootstock

It feels like Big Data might be a slightly muted data label by now, but the rationale behind the term being coined in the first place is still very much in place. As we continue to dive into the morass of new information streams, many of which will end up being channeled towards voluminous data lake repositories, we can reasonably expect Big Data-like technologies to pervade for a long time.

“Big Data has indeed become a dated term; it was first used to describe unstructured data and then eventually evolved to encompass much of the work involved with analytics as organizations grappled with the volume, velocity and variety of data being ingested. Unfortunately, Big Data was never fully equipped to handle the fourth V: veracity, which refers to the quality of the data. That’s where the combination of ERP and AI comes in,” says Stu Johnson, VP of product marketing at cloud ERP company Rootstock.

Because we know that almost all of a reasonably sized organization’s data is stored in its ERP system, Johnson says that businesses are increasingly looking to take advantage of AI. They need cloud-based ERP systems that can collect the various Vs of data while also applying it to inform better business decisions. The confluence point between AI and ERP at the edge of the Big Data chasm is what will make the difference.

“Today we can say that AI tools, including large language models (LLMs), are aided by the contextualization that ERP provides. As a result, Big Data analysis is democratized; any individual is able to interpret data in various contexts and roles,” clarifies Johnson. “The most powerful ERP solutions are capable of ingesting streams of data signals coming from all directions and interpreting those signals against a vast store of historical data. The next frontier will occur when LLMs – fueled by the data these ERP systems provide – are combined with predictive analytics, unleashing a whole new wave of signals that will drive intelligent decisions.”

It feels like Big Data is still on a journey then. One where it will be inevitably swept up into the vortex formed by new AI models, the rise of real-time data analytics, the creation of new smart application services and the trajectory that all these technologies take as they pass over the existing topography of ERP deployments. So, basically, something of an evolution needs to happen.

Bare metal, laid bare

‘Traditional’ approaches to Big Data are becoming an impediment to business success – James Sturrock, Nutanix

“It’s important to realize that what we might consider to be ‘traditional’ approaches to Big Data (use of dedicated data silos, often located on bare metal cloud servers) are becoming an impediment to business success due to long time-to-value, complex lifecycle management and poor performance,” says James Sturrock, director of systems engineering for UK and Ireland at Nutanix.

Clearly keen to tell us that we need to get out of the bare metal era and invest in hyperconverged infrastructure (HCI) solutions for Big Data analytics, Sturrock makes the point that we’re moving rapidly to an always-on continuous computing fabric where many data resources lose value comparatively quickly. As a direct consequence, the way we approach the Input/Output (I/O) capabilities of the systems we use to manage Big Data – especially in the gargantuan realms of an enterprise ERP landscape – really starts to matter now.

“It’s imperative that organizations embrace Big Data analytics solutions capable of handling what we like to call the I/O blender effect,” explains Nutanix’s Sturrock. “Because the firehose stream of I/O requests coming from demanding Big Data jobs results in a random mix of I/O that can defeat storage ‘read-ahead’ algorithms, making it difficult for a central storage system to operate efficiently. We add to the challenge when we realize that there are mixed I/O needs, i.e. some Big Data jobs require high streaming performance while others create random I/O and most storage arrays don’t handle both types equally well.”

By consolidating all data services for an ERP deployment on the same platform alongside the core compute function, Sturrock suggests that an organization can simplify provisioning and management while increasing resource utilization. Combine these techniques with a real understanding of data locality and intelligent data tiering (to rank mission criticality across a whole spectrum of levels) and it is argued that Big Data can be handled in environments that enjoy optimum performance without constant tuning.

Quality data, big or otherwise

There is no way to layer an algorithm over the top of a bunch of ERP data and just expect it to deliver meaningful insights – Claus Jepsen, Unit4

The ERP Big Data story is no open-and-shut case. If anything, both disciplines have come under scrutiny, fire and abuse for being clunky, outdated and anachronistic at times. Chief product and technology officer at Unit4, Claus Jepsen, is very much of the mind that we should exert a little more balance when we consider these two technology streams concurrently.

“I don’t think the use, implementation or management of Big Data is the central issue and, likewise, I don’t think ERP is the sole enterprise software solution that we should consider in the mix,” says Jepsen. “It was only a few years ago that everyone was saying ERP is dead – and I don’t think today we’re in a position to expect an ERP system to manage all an organization’s data management and analysis requirements. The key issue is whether a business has access to the right data to make effective decisions.”

Jepsen’s comment is reflective and illustrative of the wider sentiment here; people don’t want to talk about Big Data anymore unless it’s business-relevant accurate data that can track a clearly defined path to a positive business outcome. He reminds us that the value of a good ERP system is that the data within it is highly structured, sanitized and contained in order to drive the information systems in an organization. When all these factors are in line, he says a business can then deliver deeper insights and apply ML algorithms to its information streams, because it has confidence in the structure and semantics of its dataset, big, medium, small or otherwise.

“However, the reality is that we operate in a world of multiple data sources and types, which has created so-called data lakes that are a more convenient way to store and analyze information. Big Data offers the ability to accumulate data from those types of subsystems across multiple domains, to be analyzed alongside any data in the ERP system to get a complete picture of a business,” explains Jepsen. “But this cannot be done in the ERP system itself, as it does not necessarily have sight of the data in these other subsystems.”

Because of this unavoidable truth, Jepsen advises IT practitioners to realize the critical need for any ERP system to have the requisite level of interoperability to share data with other applications. An IT shop can standardize the Big Data analytics solutions in use, but ultimately, if the business is not analyzing the right data then the whole exercise is pointless. Data quality and integrity are critical foundations to ensuring this whole process works, but there is a realization that accumulating vast amounts of Big Data is useless if you don’t know what to do with it.

“We find companies are investing more time in the data management planning phase. This is because they want to know the questions they need to ask before accumulating data for analysis from across their IT systems,” says Unit4’s Jepsen, referencing the real-world projects his firm interacts with on a daily basis. “An organization must work out what it is trying to understand, what the data represents and what its attributes and semantics are. There is no way to layer an algorithm over the top of a bunch of ERP data (or any other source) and just expect it to deliver meaningful insights. This is why we are seeing more focus on developing specific use cases for data analytics today – and it’s a trend that must surely continue.”

A fuel for ERP systems

Complex blending and deciphering processes, with an ever-increasing amount of data, still mean data is essentially big – Lee An Schommer, insightsoftware

Agreeing that ERP had been erroneously consigned to the “where are they now” file some years back, chief product officer at insightsoftware, Lee An Schommer, suggests that the current ERP renaissance still embodies a degree of disconnect at the data level. Reminding us that ERP systems are at the heart of business data locales, she says that users themselves still struggle with information tools and feeds at certain levels.

“These ERP users continue to struggle with enterprise data streams, needing to decipher what insights are ERP-specific (versus those that come from different third-party solutions) in order to effectively help the C-suite understand the health of the business and where to act,” asserts Schommer.

In fact, many mid-market and enterprise companies have more than one ERP by design, in order to address corporate global needs versus those of certain world regions.

This requires complex blending and deciphering processes with an ever-increasing amount of data, which still means data is essentially big. It begs the question, who will make sense of all this data?

As commentators in this space have underlined, while AI and ML will continue to learn and provide business insights, any given model is only as good as the data it can feed upon. It will be up to the business itself to provide clean, relevant data to produce the best outputs. AI models may then also assess synthesized data, point out anomalies and suggest corrections. For Schommer’s money, the secret sauce is having people trained to run different models based on unique business goals and then convince senior leaders that it’s time to adjust the plan.

“This is often where data exercises fall apart,” she says. “The data is collected and then AI is interjected and applied, but afterwards… we tend to see management go back to business as usual because they are not quite ready to trust the technology and its output. As AI evolves and additional filters create quicker connectivity to better-organized data, team leaders must determine how they can validate and then take advantage of the resulting insights. With this in mind, as AI technology and ERP systems continue to come together, it can only be expected that these systems continue to grow and phase out Big Data entirely.”

The modern data firehose

Cloud computing allows companies today to streamline data processing in a way that was almost unthinkable a decade ago – Phil Le-Brun, AWS

It seems clear that the Big Data evolution issue is perhaps nowhere more clearly illustrated than in the ERP space. After all, ERP systems are a) inherently big, b) full of data, c) characterized by the volume of analytics they are able to process and d) typified by their proximity to systems of record and transaction, both of which continuously feed the modern data firehose. Who better to drive home the question of how ERP should dovetail with a big, medium or small data strategy than AWS?

“Organizations today create and store petabytes – or even exabytes – of data from a vast variety of sources, from online transactions and customer interactions through to connected devices and product inventory,” says Phil Le-Brun, director of enterprise strategy at AWS. “Cloud computing allows companies today to streamline data processing in a way that was almost unthinkable a decade ago – and to easily adapt and evolve as an organization’s needs change. This is important because building a data strategy that evolves as the business expands and changes is key to success.”

Le-Brun says that he understands just how big that Big Data mountain might feel to different firms and that every organization’s data needs are unique, which is why it’s important to have access to a variety of tools so they can leverage the right ones depending on their specific needs. “A modern business will require the data spread across their organization to be integrated and connected, so they can act on their data no matter where it lives. Ultimately, they must eliminate structural and departmental data silos to be sure that all the right people can access data at the right time and with the right controls,” concludes AWS’s Le-Brun.

Whether we drop the Big Data term tomorrow or hang on to it by some romantic attachment, its time on Earth has been worthwhile because it has taught us that we can quickly find ourselves with too much to eat at the information buffet or smörgåsbord. A progressive business today needs an IT strategy, a data strategy (which encompasses analytics) and of course now an AI strategy, all of which must exist in symbiotic unity. So long and thanks for all the bits.

The post Dated but still feted: The Big Data universe appeared first on ERP Today.