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
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
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
The contact center market continues to shift focus from handling customer calls as efficiently as possible to providing superior customer engagement across multiple touch points. The latest advancement is an joint announcement from IBM and Genesys who have signed a partnership agreement to provide “smarter customer engagement”. The agreement includes a technology partnership and a joint marketing plan, and brings together IBM’s Watson Engagement Advisor and Genesys’ Customer Experience Platform.
Topics: Social Media, Customer Experience, Genesys, Mobile Apps, Self-service, Operational Performance, Cloud Computing, Customer & Contact Center, Customer Service, Call Center, Cognitive Computing, Contact Center, CRM, IBM Watson
At its recent Connect 2014 event IBM announced IBM Kenexa Talent Suite, an integrated talent management suite. The release strengthens its Smarter Workforce initiative by combining IBM and Kenexa products and services in one human capital management (HCM) offering. IBM Kenexa Talent Suite also addresses increasing efforts by human resources organizations to optimize their activities through more effective use of technology, a topic covered in our 2014 HCM research agenda. Specifically, the release integrates talent management process automation capabilities with collaboration and also can be complemented with its workforce analytics to help organizations be more efficient and productive; our benchmark research shows these are the leading benefits of using human capital analytics systems.
Topics: Big Data, Mobile, SAP, Social Media, HCM, Kenexa, Recruiting, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Cloud Computing, Collaboration, IBM, Oracle, Workforce Performance, Cognitive Computing, HR, IBM Watson, Social
With much fanfare and a rarely seen introduction by CEO Ginni Rometty, IBM launched IBM Watson as a new business unit focused on cognitive computing technology and solutions, now being led by Senior Vice President Mike Rhodin. The announcement is summarized here:. Until now IBM Watson was important but had neither this stature in IBM’s organizational structure nor enough investment to support what the company proclaims is the third phase of computing. As IBM tells it, computing paradigms began with the century-old tabular computing, followed by the age of programmatic computing, in which IBM developed many products and advancements. The third phase is cognitive computing, an area in which the company has invested significantly to advance its technology. IBM has been on this journey for some time, long before the IBM Watson system beat humans on Jeopardy!. Its machine-learning efforts started with the IBM 704 and computer checkers in the 1950s, followed by decades of utilizing the computing power of the IBM 360 mainframe, the IBM AS/400, the IBM RS/6000 and even IBM XT computers in the 1980s. Now IBM Watson is focused on reaching the full potential of cognitive computing.
Topics: Big Data, Sales Performance, Social Media, Supply Chain Performance, IT Performance, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, CIO, Cloud Computing, Customer & Contact Center, Financial Performance, Governance, Risk & Compliance (GRC), Information Applications, Information Management, Location Intelligence, Operational Intelligence, Workforce Performance, Cognitive Computing, Discovery, Exploration, IBM Watson
Recently my colleague Mark Smith wrote about the IBM Watson platform. Mark is our expert on technically complex subjects like IBM Watson and cognitive computing and the value it can provide to organizations and wrote an educational white paper on the topic. In fact IBM Watson was awarded the 2012 Ventana Research Technology Innovation Award. I focus on the customer and the customer experience, but I became engaged with the launch of the IBM Watson Engagement Advisor, which uncannily brings the two together.
Topics: Social Media, Customer Analytics, Customer Experience, Social CRM, Mobile Apps, Self-service, Operational Performance, Business Performance, Cloud Computing, Customer & Contact Center, Customer Service, IBM, Call Center, Cognitive Computing, Contact Center, Contact Center Analytics, CRM, IBM Watson, Text Analytics
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