Artificial intelligence using machine learning has passed through the bright, shiny object stage and software vendors are well into the process of making the concept a reality in their offerings. Ventana Research defines AI as the use of technology to process information in much the way humans do, including improving accuracy in recommendations, actions and conclusions as more data is received. I like the alternative term “augmented intelligence” because it emphasizes that these systems enhance – rather than replace – the capabilities of the humans employing them, especially through improved decision-making and eliminating the need to perform repetitive work.
Topics: Planning, Machine Learning, Budgeting, Business Planning, Financial Performance Management, forecasting, AI and Machine Learning, digital finance, profitability management
Alteryx Inspire 2019, this year's user conference for Alteryx, drew around 4500 customers, partners, and prospects to Nashville’s Gaylord Opryland Resort & Convention Center in Tennessee last month. The strong attendance was a reflection of the strong growth Alteryx has experienced over the last year; roughly 50% growth year-over-year. This year's conference focused on Alteryx's evolution from data preparation to AI and machine learning, and both were front and center.
Topics: Big Data, Data Science, alteryx, Machine Learning, Data Integration, Data Management, Alteryx Inspire
Dreamforce has become the largest enterprise software event for businesses in the United States, and it is evident why when looking at it this year. With over 170,000 business and IT professionals attending, Salesforce came to show off upcoming product announcements and innovations. This year's biggest focus was on Einstein Voice (a personalized and intelligent conversational assistant), integration with other platforms, and Salesforce Customer 360. The last of these is the start of an answer to a problem we have well documented; businesses struggle getting a full view of the customer and provide a frictionless response to issues and interactions. For the full breakdown of Dreamforce 2018, and my analysis of all the largest announcements, watch my hot take video.
Topics: Salesforce.com, Customer Experience, Machine Learning, Marketing, Voice of the Customer, CRM, Dreamforce, Sales Performance Management, SPM, Digital Technology, Digital Marketing, Robotic Process Automation, AI, natural language processing
In 2017 Strata + Hadoop World was changed to the Strata Data Conference. As I pointed out in my coverage of last year’s event, the focus was largely on machine learning and artificial intelligence (AI). That theme continued this year, but my impression of the event was of a community looking to get value out of data regardless of the technology being used to manage that data. The change was subtle: The location was the same; the exhibitors were largely the same; attendance was similar this year and last. But there was no particular vendor or technology dominating the event.
Topics: Big Data, Data Science, Machine Learning, Analytics, Business Intelligence, Data Governance, Data Integration, Data Preparation, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing
All too often, software vendors view analytics as the end rather than the beginning of a process. I’m reminded of some of the advanced math classes I’ve taken in which the teaching process focused on a few key aspects of a mathematical proof or solution, leaving the rest of the exercise to be worked out by the students. In other contexts, you may hear people say the numbers speak for themselves.
Topics: Data Science, Machine Learning, business intelligence, Analytics, Collaboration, Data Governance, Information Optimization, Digital Technology, collaboration for business
Employee engagement has been a dominant theme in both human capital management (HCM) and the systems to manage it in recent years; lately (though not necessarily appropriately) it is a topic often equated with the notion of the employee experience. On a related point, Gallup’s annual employee engagement survey has consistently found the majority of today’s workforce to be disengaged, defined as “not enthusiastic or passionate about their work.” Interest in the degree to which HCM technology can improve employee engagement (or mitigate disengagement) now rivals the attention given to such perennial chief human resources officer (CHRO) concerns as attracting and retaining top talent and retooling the workforce.
Topics: Big Data, Data Science, Human Capital Management, Machine Learning, Learning Management, Analytics, Business Intelligence, Cloud Computing, Collaboration, HRMS, Workforce Management, Digital Technology, Workforce Optimization
We now are well beyond the year depicted in 2001: A Space Odyssey, a cinematic perspective on the future of artificial intelligence in which HAL 9000, a computer, is able to simulate human behavior and control machines. Anyone reviewing the past two years of marketing around AI in the business technology industry can be forgiven for believing that we have arrived at the futuristic state Stanley Kubrick imagined. We have not.
Topics: Big Data, Data Science, Mobile, Customer Analytics, Customer Engagement, Customer Experience, Machine Learning, Mobile Technology, Analytics, Business Intelligence, Cloud Computing, Collaboration, Customer Service, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing, Cybersecurity, Billing and Recurring Revenue, Workforce Optimization, collaboration for business
Workday recently presented a technology summit for industry analysts. The presentations focused on Workday’s ongoing product advancements as well as its approach to employing emerging technologies. These technologies include artificial intelligence (AI) and machine learning (ML), robotic process automation (RPA) and bots utilizing natural language processing. Ventana Research uses the term “robotic finance” to refer to these technologies when used in the office of finance. In our view, they will have a profound impact on the nature of white-collar work over the coming decade. Financial management and ERP software vendors are focusing on these technologies because they will disproportionately affect finance and accounting departments: I estimate that their adoption has the potential to eliminate one-third of the accounting department’s workload within a decade.
Topics: Big Data, Data Science, Mobile, Machine Learning, Office of Finance, Continuous Planning, Cloud Computing, Collaboration, Financial Performance Management, ERP and Continuous Accounting
Advancing the potential of any business requires continuous improvement in the processes and technology that support it. Many companies have embraced attempts at a digital transformation, and it’s become a goal to which organizational resources and budgets have been dedicated around the globe.
Topics: Big Data, Data Science, Mobile, Sales, Customer Analytics, Customer Engagement, Customer Experience, Human Capital Management, Machine Learning, Marketing, Marketing Performance Management, Mobile Technology, Office of Finance, Wearable Computing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Customer Service, Data Governance, Data Integration, Data Preparation, Internet of Things, Contact Center, Information Optimization, Product Information Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Machine Learning and Cognitive Computing, Pricing and Promotion Management, Cybersecurity, Billing and Recurring Revenue, Workforce Optimization, collaboration for business, mobile marketing
After more than a decade of steady development, ERP systems today are changing fundamentally, facilitated by the availability of advances such as cloud computing, advanced database architecture, collaboration, improved user-interface design, mobility, analytics and planning. This was evident when Oracle recently held its third analysts-only ERP Cloud Summit in New York to coincide with its Modern Finance Experience event. Oracle now has an increasingly robust set of business applications that reside in the cloud and a growing list of live customers – large and midsize – from a range of industries across the world, both of which were offered as part of the here-and-now technology theme at the event.
Topics: ERP, Machine Learning, Cloud Computing, Robotic Process Automation, Artificial intelligence, blockchain, AI
SAP recently held a teleconference to highlight its blockchain strategy. Lately, the major business software vendors have been calling attention to their blockchain initiatives. While the focus on this technology might seem premature to those who still equate it with cryptocurrencies, evidence is pointing to a future pace of adoption similar to the rapid take-up of the internet in the 1990s. That blockchain is useful for a wide range of business functions isn’t news – just google “blockchain use cases.” Payment, provenance, testament and efficiency are four main themes driving a multitude of applications of the technology. That said, blockchain isn’t technology in search of a mission but is something more like the internet, both in its broad utility and in value multiplication through network effects.
Topics: Machine Learning, Office of Finance, finance transformation, Robotic Process Automation, Artificial intelligence, blockchain, AI, bots, robotic finance
Robots of the physical sort are not about to take over finance and accounting but we have arrived at the age of “Robotic Finance”. I coined this term to focus on four key technologies with transformative capabilities: artificial intelligence and machine learning, robotic process automation, bots and natural language processing and blockchain distributed ledger technology. Embracing these technologies will enable any department to redefine itself as a forward-looking strategic partner to the rest of the company.
Topics: Machine Learning, close, closing, Robotic Process Automation, Artificial intelligence, blockchain, AI, Accounting, bots
The use of blockchain distributed ledgers in business processes is now a common theme in many business software vendors’ presentations. The technology has a multitude of potential uses. However, presentations about the opportunities for digital transformation always leave me wondering: How is this magic going to happen? I wonder this because the details about how data flows from point A to point B via a blockchain are critically important to blockchain utility and therefore the pace of its adoption.
Topics: Planning, Predictive Analytics, Forecast, FP&A, Machine Learning, Reporting, budget, Budgeting, Continuous Planning, Analytics, Data Management, Cognitive Computing, Integrated Business Planning, AI, forecasting, consolidating
We at Ventana Research recently published our research agendas for 2018. Analytics and business intelligence are evolving and so is our research on their use across practice areas. Earlier research has shown that analytics can deliver significant value to organizations; for example, our predictive analytics research shows that 57 percent of organizations reported achieving a competitive advantage and half created new revenue opportunities with predictive analytics. Waves of investment in self-service analytics have propelled the market for analytics tools, significantly empowering line-of-business organizations to create their own analytics and set their own analytic priorities. But organizations are also beginning to recognize some of the limitations of current analytics implementations – for self-service, for example. Our Data Preparation Benchmark Research reveals that fewer than half (42%) of organizations are comfortable allowing business users to work with data not prepared by IT. Our research this year will continue to explore both the successes and challenges organizations face as they continue to use analytics and BI.
Topics: Machine Learning, Analytics, Business Intelligence, Collaboration, Internet of Things, IOT, Artificial intelligence, natural language processing, Natural Language Generation
Ventana Research uses the term “predictive finance” to describe a forward-looking, action-oriented finance organization that places emphasis on advising its company rather than fulfilling the traditional roles of a transactions processor and reporter. Technology is driving the shift away from the traditional bean-counting role. The cumulative evolution of software advances will substantially reduce finance and accounting workloads by automating most of the mechanical, rote functions in accounting, data preparation and reporting. (I recently summarized these in a “Robotic Finance”)
Topics: Planning, Predictive Analytics, Forecast, FP&A, Machine Learning, Reporting, budget, Budgeting, Continuous Planning, Analytics, Data Management, Cognitive Computing, Integrated Business Planning, AI
For several years, I’ve commented on a range of emerging technologies that will have a profound impact on white-collar work in the coming decade. I’ve now coined the term “Robotic finance” to describe this emerging focus, which includes four key areas of technology: Artificial intelligence (AI) and machine learning (ML), robotic process automation (RPA), bots utilizing natural language processing, and blockchain distributed ledger technology (DLT), each of which I describe below. Robotic finance will have a disproportionate impact on finance and accounting departments: I estimate that adoption of these technologies potentially will eliminate one-third of the accounting department’s workload within a decade.
Topics: ERP, Machine Learning, close, Consolidation, Continuous Accounting, Reconciliation, CFO, Robotic Process Automation, blockchain, AI, natural language processing, Accounting, RPA, bots, voice automation
I recently attended SAP TechEd in Las Vegas to hear the latest from the company regarding its analytics and business intelligence offerings as well as its data management platform. The company used the event to launch SAP Data Hub and made several other data and analytics announcements that I’ll cover below.
Topics: Big Data, SAP, Machine Learning, Analytics, Data Preparation, SAP TechEd
I’m thrilled to announce to my HCM vendor and practitioner network as well as the ever-expanding Ventana Research community that I’m now directing Ventana’s HCM practice. I will be working closely with our CEO and Chief Research Officer Mark Smith, who is a fellow HCM enthusiast and thought leader.
Topics: Big Data, Data Science, Mobile, Human Capital Management, Machine Learning, Learning Management, Analytics, Cloud Computing, Collaboration, Internet of Things, HRMS, Workforce Management, Payroll Optimization, Customer Digital Technology
The Strata Data Conference is changing and it’s changing in a good way. At the recent Strata Data Conference in New York, Mike Olson, chief strategy officer at Cloudera, which co-sponsored the event, commented that at prior events we used to talk about the “Hadoop zoo animals,” meaning the various components of the Hadoop ecosystem of which I have written previously. Following last fall’s Strata event, I observed that the conference was evolving to focus on the use of data. Advancing that evolution, this year’s event focused on a particular type of usage: artificial intelligence (AI) and machine learning. The evolution from a focus on zoo animals to a focus on business value using advanced analytics shows further maturation of the big data market.
Topics: Big Data, Machine Learning, Analytics, Hadoop, Artificial intelligence
The application of artificial intelligence (AI) and machine learning (ML) to business computing will have a profound impact on white collar professions. This is especially true in heavily rules-based functions such as accounting. Companies recognize the transformational potential of AI and ML, but the progression and pace of the adoption of these technologies is unclear. Some applications of AI and ML are already in use but others are a decade or more away from replacing human tasks.
Topics: Big Data, Machine Learning, Office of Finance, Analytics, CFO, finance, CEO, AI, accountants, NLP, Accounting
Fra Luca Pacioli, a 15th-century Franciscan friar living in what’s now Italy, is credited with codifying double-entry bookkeeping, which is the foundation of accounting. Pacioli, a polymath, was well acquainted with his contemporary and fellow polymath Leonardo Da Vinci. So, given they were at times collaborators, it’s fitting that one of the most important applications of SAP’s Leonardo technology will be in helping to disrupt finance and accounting organizations in corporations.
Topics: ERP, Machine Learning, Office of Finance, Internet of Things, CFO, Artificial intelligence, AI, Leonardo
Recently Hortonworks announced some significant additions to its products at the DataWorks Summit. These additions reflect the fact that the big data market continues to evolve, as I have previously written.
Topics: Big Data, Machine Learning, Analytics
Natural language generation (NLG), the process of generating text or narratives based on a set of data values, can reach a broader audience. NLG narratives can be used for a variety of purposes, but in this perspective I focus on how NLG can be used to enhance business intelligence (BI) processes. In the case of BI, NLG can be used to explain what has happened and why it is happening, and even what actions to take. The NLG narratives can be understood by a broader range of business users than the tables and charts of data that are the typical output of most BI applications or analytics tools.
Topics: Machine Learning, Natural Language, Analytics, Business Intelligence
If we look at the focus of technology vendors for analytics and business intelligence or business applications providers deploying these capabilities in the last five years, we see that they have elevated the importance on the value of visualization and dashboards. These promotions might be understandable, but will they make business and the people using them more intelligent?
Topics: Big Data, Data Science, Mobile, Machine Learning, Analytics, Business Intelligence, Cloud Computing, Collaboration, Information Optimization, Digital Technology, Machine Learning and Cognitive Computing
The importance of analytics for sales organizations is clear and, as I pointed out in my recent analyst perspective on the next generation of sales analytics, these capabilities optimize revenue potential. However, utilizing sales analytics requires a set of data skills that most organizations still find challenging and are thus not fully prepared to support. The efficient access and preparation of data underlies any analytics processes, which must meet demanding needs that are not always automated. Our research into next generation sales analytics has found many impediments that must be addressed and is a critical part of our expertise agenda for sales organizations.
Topics: Big Data, Sales, Machine Learning, Office of Finance, Analytics, Cloud Computing, Collaboration, Product Information Management, Sales Performance Management, Digital Technology, Digital Commerce, Sales and Operations Planning, Machine Learning and Cognitive Computing, Sales Enablement and Execution, Sales Planning and Analytics
Oracle recently held its second ERP Cloud Summit with industry analysts. The all-day event wasn’t just about ERP. The company covered a range of its business applications, including financial performance management as well as its Adaptive Intelligent Applications. And it wasn’t just about the cloud. After more than a decade of steady developments, ERP systems have begun to change fundamentally, facilitated by the growing availability of new technologies including cloud computing, advanced database architecture, collaboration, user interface design, mobility, analytics and planning. Here are my key takeaways from the event:
Topics: Big Data, Data Science, Mobile, Customer Experience, Human Capital Management, Machine Learning, Office of Finance, Analytics, Data Integration, Internet of Things, Cognitive Computing, HRMS, Financial Performance Management, Mobile Marketing Digital Commerce, Digital Marketing, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, ERP and Continuous Accounting
Cloudera provides database and enabling technology for the big data market and overall for data and information management. As my colleague David Menninger has written, the big data and information management technology markets are changing rapidly and require vendors to adapt to them. Cloudera has grown significantly over the last decade and now has approximately 1,000 customers and provides support and services in countries around the world. Its product and technology strategy is to provide a unified data management platform, Cloudera Enterprise, that can meet the data engineering and science needs for a range of analytic and operational database applications. Its primary focus is its Enterprise Data Hub, which as a data lake can handle organizations’ big data and analytical needs. As David Menninger asserts, the data lake is a safe way to invest in big data. It also helps shift the focus away from the V’s (volume, velocity and variety) of big data to the A’s, which are analytics, awareness, anticipation and action.
Topics: Big Data, Data Science, Machine Learning, business intelligence, Analytics, Cloud Computing, Data Governance, Data Integration, Data Preparation, Internet of Things, Cognitive Computing, Information Optimization, Digital Technology
Business process reengineering was a consulting fashion in the early 1990s that spurred many companies to purchase their first ERP systems. BPR proposes a fundamental redesign of core business processes to achieve substantial improvements in market and customer responsiveness, productivity, cycle times and quality. ERP systems support business process reengineering by guiding the step-by-step execution of the redesigned process to ensure that it is performed consistently. They also automate the handoffs between individuals and departments to accelerate completion of that process.
Topics: Big Data, Data Science, Mobile, Customer Analytics, Customer Experience, Machine Learning, Office of Finance, Wearable Computing, Continuous Planning, Analytics, Business Intelligence, Cloud Computing, Data Integration, Internet of Things, Financial Performance Management, Digital Technology, Digital Marketing, Digital Commerce, Operations & Supply Chain, Enterprise Resource Planning, Machine Learning and Cognitive Computing, ERP and Continuous Accounting, Sales Planning and Analytics
I am happy to provide my personal perspective on the potential of sales organizations, processes and technology to supercharge business activity in 2017. The sales processes of organizations – whether they involve digital commerce or direct or indirect physical selling – should be part of continuous optimization efforts to reach maximum results. To do this, the people leading and running sales processes must be able to use technology that supports their responsibilities and analyzes the crucial information coming into the business. For almost 15 years, we have advocated for sales applications and tools that are necessary to optimize sales effectiveness and improve the outcomes of their sales efforts. The available portfolio is much larger than sales force automation (SFA) and involves more than the continued use of CRM, which has clear limits in its ability to manage customer relationships. The applications on offer include many facets of sales: coaching, compensation management, contract management, configure price quote (CPQ), forecasting, quota and territory management, planning and optimization, pricing and revenue optimization, and target or market intelligence. New applications designed for sales also enable digital effectiveness that can transform organizations. Let me provide my perspective on six topics that are shaping the way sales can and should operate in 2017, and which are part of our sales research agenda for the year.
Topics: Big Data, Sales, Machine Learning, Mobile Technology, Office of Finance, Analytics, Cloud Computing, Collaboration, Product Information Management, Sales Performance Management, Digital Technology, Digital Commerce, Operations & Supply Chain, Sales and Operations Planning, Machine Learning and Cognitive Computing, Sales Enablement and Execution, Sales Planning and Analytics
Until recently most organizations deployed systems on their own premises to build communications and contact center infrastructures, which often required them to integrate products from several vendors. In the past few years many vendors have moved their systems to the cloud, and others have begun as cloud-based suppliers. This trend has opened up the opportunity for more organizations to take advantage of modern communication systems and contact centers. Using the cloud for either, or both can save money and resources, reduce risk, and make available more integrated, multi-channel systems. While the adoption of such systems has undoubtedly increased and is likely to continue to do so, our benchmark research into next-generation contact centers in the cloud finds that many organizations still prefer to remain on premises, and adoption of cloud-based systems occurs on a case-by-case basis. In addition, many organizations look for vendors that support multiple models so they have the option of starting out using one model but transitioning later to another, including to a hybrid model in which some systems are on-premises and others are cloud-based..
Topics: Big Data, Mobile, Customer Analytics, Customer Engagement, Customer Experience, Machine Learning, Wearable Computing, Analytics, Business Intelligence, Cloud Computing, Collaboration, Internet of Things, Contact Center, Digital Commerce, Subscription Billing
Big data initially was characterized in terms of “the three V’s,” volume, velocity and variety. Nearly five years ago I wrote about the three V’s as a way to explain why new and different technologies were needed to deal with big data. Since then the industry has tackled many of the technical challenges associated with the three V’s. In 2017 I propose that we focus instead on a different letter, which includes these A’s: analytics, awareness, anticipation and action. I’ll explain why each is important at this stage of big data evolution.
Topics: Data Science, Machine Learning, Analytics, Business Intelligence, Collaboration, Data Preparation, Internet of Things, Information Optimization
Ventana Research awarded our Governance, Risk and Compliance (GRC) Business Innovation Award for 2016 to IBM for IBM Regulatory Compliance Analytics, powered by Watson (IRCA). This application of cognitive analytics is designed to streamline the identification of potential regulatory requirements and suggest methods for compliance. In so doing the cloud-based system can cut the time and cost of compliance while creating an effective means of ongoing management and control of compliance processes.
Topics: GRC, Machine Learning, Office of Finance, Dodd-Frank, Risk Analytics, compliance, finance, Financial Services, Watson
IBM recently held its inaugural World of Watson event. Formerly known as IBM Insight, and prior to that IBM Information on Demand, the annual event, attended by 17,000 people this year, showcases IBM’s data and analytics and the broader IBM efforts in cognitive computing. The theme for the event, as you might guess, was the Watson family of cognitive computing products. I, for one, was glad to spend more time getting to know the Watson product line, and I’d like to share some of my observations from the event.
Topics: Big Data, Data Science, Machine Learning, Analytics, Cloud Computing, Data Governance, Data Integration, Internet of Things, Information Optimization, Machine Learning and Cognitive Computing
I recently attended .conf2016, Splunk’s seventh annual user conference. Splunk created the market for analyzing machine data (shorthand for machine-generated data), which consists of log files and event data from various types of systems and devices. Our big data analytics benchmark research shows that these are two of the most common sources of big data that organizations analyze. This market has proven to be fertile ground for Splunk, growing steadily with revenues more than doubling over the previous two fiscal years. Machine data is also the backbone for the Internet of Things (IoT) and operational intelligence, which form the basis of forthcoming benchmark research from Ventana Research.
Topics: Machine Learning, Splunk, Analytics, Machine data, Operational Intelligence
Splunk’s annual gathering, this year called .conf 2015, in late September hosted almost 4,000 Splunk customers, partners and employees. It is one of the fastest-growing user conferences in the technology industry. The area dedicated to Splunk partners has grown from a handful of booths a few years ago to a vast showroom floor many times larger. While the conference’s main announcement was the release of Splunk Enterprise 6.3, its flagship platform, the progress the company is making in the related areas of machine learning and the Internet of Things (IoT) most caught my attention.
Topics: Big Data, Predictive Analytics, Machine Learning, IT Analytics & Performance, Operational Performance, Plunk, Analytics, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Information Management, Internet of Things, Operational Intelligence, Data, Information Optimization
IBM Watson blends existing and innovative technology into a new approach called cognitive computing. At the simplest operational level it is technology for asking natural language-based questions, getting answers and support appropriate action to be taken or provide information to make more informed decisions. The technology relies on massive processing power to yield probabilistic responses to user questions using sophisticated analytical algorithms. A cognitive system like Watson accesses structured and unstructured information within an associated knowledge base to return responses that are not simply data but contextualized information that can inform users’ actions and guide their decisions. This is a gigantic leap beyond human decision-making using experience based on random sources from the industry and internal sets of reports and data.
Topics: Sales Performance, Supply Chain Performance, Machine Learning, IT Performance, Operational Performance, Business Analytics, Business Intelligence, Business Performance, Customer & Contact Center, Financial Performance, IBM, Information Management, Workforce Performance, Cognitive Computing, Expert Systems, IBM Watson