Last week I attended Spark Summit East 2016 at the New York Hilton Midtown. It revealed several ways in which Spark technology might impact the big data market.
TelStrat is a company with a long history. Founded in 1993 it initially resold products of Nortel, Cisco and other telecom equipment vendors. The first product it developed and brought to market was a call recording system deployed on the customer’s premises. It expanded its portfolio over the years, and today its product suite Engage offers all the key pieces of workforce optimization: call recording, desktop capture, quality management, workforce management and speech, text and desktop analytics. TelStrat built this portfolio through a combination of in-house development and partnering with other vendors. It has achieved considerable business success, having more than 3,300 installations in 55 countries, most of which are delivered through a global ecosystem of some 330 channel partners. Engage is available in three models: Unity is an on-premises, single-server version that supports up to 250 users; Enterprise is an on-premises, multiple-server version that supports unlimited numbers of users at multiple sites; and Cloud is a hosted product that supports unlimited numbers of users and is available through a perpetual license or subscription. The company attributes its recent success to the Cloud version, which it supports through multiple data centers in North America, Europe and Asia Pacific. This and its longstanding team of call center experts and partners prepares TelStrat to help organizations of all sizes improve contact center agent performance.
Using information technology to make data useful is as old as the Information Age. The difference today is that the volume and variety of available data has grown enormously. Big data gets almost all of the attention, but there’s also cryptic data. Both are difficult to harness using basic tools and require new technology to help organizations glean actionable information from the large and chaotic mass of data. “Big data” refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends and associations, especially those related to human behavior and interaction. The challenges in dealing with big data include having the computational power that can scale to the processing requirements for the volumes involved; analytical tools to work with the large data sets; and governance necessary to manage the large data sets to ensure that the results of the analysis are accurate and meaningful. But that’s not all organizations have to deal with now. I’ve coined the term “cryptic data” to focus on a different, less well known sort of data challenge that many companies and individuals face.
Topics: Analytics, Budgeting, Business Analytics, Business Performance, Business Performance Management (BPM), Connotate, cryptic, data science, Datawatch, equity research, Finance Analytics, Financial Performance, Financial Performance Management (FPM), FP&A, Human Capital, Kapow, Kofax, Marketing, Office of Finance, Operational Performance, Operational Performance Management (OPM), Planning, Predictive Analytics, Sales Performance, Sales Performance Management (SPM), Social Media, Statistics, Supply Chain Performance, Big Data
The imperative to transform the finance department to function in a more strategic, forward-looking and action-oriented fashion has been a consistent theme of practitioners, consultants and business journalists for two decades. In all that time, however, most finance and accounting departments have not changed much. In our benchmark research on the Office of Finance, nine out of 10 participants said that it’s important or very important for finance departments to take a strategic role in running their company. The research also shows a significant gap between this objective and how well most departments perform. A large majority (83%) said they perform the core finance functions of accounting, fiscal control, transaction management, financial reporting and internal auditing, but only 41 percent said they play an active role in their company’s management. Even fewer (25%) have implemented a high degree of automation in their core finance functions and actively promote process and analytical excellence.
Topics: Analytics, Big Data, Budgeting, Business Analytics, Business Collaboration, Business Performance, CEO, CFO, CIO, close, Cloud Computing, Continuous Accounting, Continuous Planning, CPQ, end-to-end, Financial Performance, Financial Performance Management, FPM, Governance, GRC, Human Capital, In-memory, Mobile Technology, Planning, Predictive Analytics, Risk, Social Media, Tax, Tax-Datawarehouse, Uncategorized, Office of Finance
The big data market continues to expand and enable new types of analyses, new business models and new revenues streams for organizations that implement these capabilities. Following our previous research into big data and information optimization, we’ll investigate the technology trends affecting both of these domains as part of our 2016 research agenda.
Throughout the course of our research in 2016, we’ll be exploring ways in which organizations can maximize the value of their data. Ventana Research believes that analytics is the engine and data is the fuel to power better business decisions. Several themes emerged from our benchmark research on incorporating data and analytics into organizational processes, and we will follow them in our 2016 Business Analytics Research Agenda:
Topics: Analytics, Big Data, Business Analytics, Business Collaboration, Business Intelligence, Cloud Computing, Information Management, Information Optimization, Mobile Technology, Operational Intelligence, Predictive Analytics
Some followers of Ventana Research may recall my work here several years ago. Here and elsewhere I have spent most of my career in the data and analytics markets matching user requirements with technologies to meet those needs. I’m happy to be returning to Ventana Research to resume investigating ways in which organizations can make the most of their data to improve their business processes; for a first look, please see our 2016 research agenda on Big Data and Information Optimization. I relish the opportunity to conduct primary market research in the form of Ventana’s well-known benchmark research and to help end users and vendors apply the information collected in those studies.
Topics: Analytics, Big Data, Business Analytics, Business Intelligence, Information Management, Information Optimization, Internet of Things, IOT, Operational Intelligence, Predictive Analytics, Unicorns
I have been involved in the contact center, CRM and customer engagement business for more than 25 years. Yet only in the past few years have I seen much change. Until recently nearly all organizations focused on handling customer interactions as efficiently and inexpensively as possible; few made much effort to manage customer relationships over the complete customer life cycle. However, over the last 18 months, the scene has begun to change very rapidly, and I expect that to continue and even accelerate during 2016.
Topics: Analytics, Big Data, Call Center, Cloud Computing, Contact Center, Contact Center Analytics, CRM, Customer Analytics, Customer & Contact Center, Customer Engagement, Customer Service, Speech Analytics, Text Analytics, Uncategorized, Voice of the Customer, Customer Experience
Aria Systems provides companies with software for managing subscription or recurring revenue business models. A recurring revenue business models includes three types of selling and billing structures: a one-time transaction plus a periodic service charge; subscription-based services involving periodic charges; or a contractual relationship that charges periodically for goods and services. Aria’s cloud-based software addresses key requirements of users in the marketing, sales, operations and accounting functions in this type of business.
Topics: billing software, Business Analytics, Business Performance, Business Performance Management (BPM), Cloud Computing, Customer Engagement, customer life cycle, Customer Performance, Customer Service, ERP, Financial Performance, Marketing, NetSuite, Operational Performance, Recurring Revenue, SaaS, Sales, Sales Performance, Sales Performance Management (SPM), Office of Finance, Customer Experience
A new company has emerged in the market for real-time analytics software. Anodot came out of stealth mode in late 2015 with $3 million in funding. It is led by three founders: CEO David Drai, whose company Cotendo was acquired by networking company Akamai Technologies in 2012; Ira Cohen, chief data scientist, who previously held that position at Hewlett-Packard; and Shay Lang, who serves as VP of R&D. Unlike most vendors in the space, the company is delivering anomaly detection and operational intelligence through software as a service (SaaS).
Topics: Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Internet of Things, Operational Intelligence, Operational Performance Management (OPM), Predictive Analytics, Uncategorized, Big Data, Analytics
There were significant technology developments in customer experience management during 2015. Multichannel contact centers in the cloud took hold of the contact center infrastructure market; I counted 21 vendors offering such services. A variety of vendors entered the market for customer analytics, combining analysis of structured data, speech recordings, text, desktop data, Web contacts, and events and processes to provide a comprehensive “360-degree” view of the customer and customer journey maps to track individual interactions over time. In addition a range of self-service or digital customer service applications became available, including mobile apps, voice-activated virtual agents, interactive video and Q&A websites and chat driven by natural-language processing. Digitally connected devices (the Internet of Things [IoT]) and wearable devices began to emerge. In 2016 I will track and try to anticipate the impact these technologies have on the customer experience.
Topics: Analytics, Big Data, Call Center, Cloud Computing, Contact Center, Contact Center Analytics, CRM, Customer Analytics, Customer Engagement, Customer Performance, Customer Service, Speech Analytics, Text Analytics, Uncategorized, Voice of the Customer, Customer Experience
The steady march of technology’s ability to handle ever more complicated tasks has been a constant since the beginning of the information age in the 1950s. Initially, computers in business were used to automate simple clerical functions, but as systems have become more capable, information technology has been able to substitute for increasingly higher levels of human skill and experience. A turning point of sorts was reached in the 1990s when ERP, business intelligence and business process automation software reduced the need for middle managers. Increasingly, organizations used software to coordinate activities as well as communicate results and requirements up and down the organizational chart. Both were once the exclusive role of the middle manager. Consequently, almost every for-profit organization eliminated management layers so that today corporate structures are flatter than they once were. Technology automation also eliminated the need for administrative staff to perform routine reporting and analysis. Meanwhile, over the course of the 1990s, the cost of running the finance department measured as a percentage of sales was cut almost in half as a result of eliminating staff and because automation enabled companies to scale without adding headcount. During the last recession, companies in North America and Europe once again made deep reductions to their administrative staffs, relying on information technology to pick up the slack.
Topics: Analytics, audit, Business Analytics, Business Performance, CFO, ERP, finance transformation, Financial Performance, FPM, Governance, GRC, Human Capital, Innovation Awards, LongView, Oracle, Risk & Compliance (GRC), Sustainability, Tax, Thomson-Reuters multinational, Vertex, Office of Finance