Managing marketing performance is anything but simple. It requires establishing a unified approach to assess the outcomes of initiatives and projects and compare results with investments in marketing people and campaigns. In general, while performance management has been conducted effectively at the corporate levels, it has been a challenge for most lines of business, marketing departments included.
Topics: Sales Performance, Social Media, Marketing, Marketing Performance Management, Marketing Planning, Operational Performance Management (OPM), Customer Performance, Business Analytics, Business Intelligence, Business Performance, Uncategorized, CMO, Demand Generation
Technology innovation is accelerating faster than companies can keep up with. Many feel pressure to adopt new strategies that technology makes possible and find the resources required for necessary investments. In 2015 our research and analysis revealed many organizations upgrading key business applications to operate in the cloud and some enabling access to information for employees through mobile devices. Despite these steps, we find significant levels of digital disruption impacting every line of business. In our series of research agendas for 2016 we outline the areas of technology that organizations need to understand if they hope to optimize their business processes and empower their employees to handle tasks and make decisions effectively. Every industry, line of business and IT department will need to be aware of how new technology can provide opportunities to get ahead of, or at least keep up with, their competitors and focus on achieving the most effective outcomes.
Topics: Big Data, Predictive Analytics, Sales Performance, Supply Chain Performance, Governance, Mobile Technology, Operational Performance Management (OPM), Customer Performance, Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, Cloud Computing, Financial Performance, Governance, Risk & Compliance (GRC), Information Applications, Information Management, Location Intelligence, Operational Intelligence, Uncategorized, Workforce Performance, Business Performance Management (BPM), Financial Performance Management (FPM), Information Optimization, Sales Performance Management (SPM)
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: Big Data, Data Science, Planning, Predictive Analytics, Sales Performance, Social Media, Supply Chain Performance, FP&A, Human Capital, Marketing, Office of Finance, Operational Performance Management (OPM), Budgeting, Connotate, cryptic, equity research, Finance Analytics, Kofax, Statistics, Operational Performance, Analytics, Business Analytics, Business Performance, Financial Performance, Business Performance Management (BPM), Datawatch, Financial Performance Management (FPM), Kapow, Sales Performance Management (SPM)
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: Big Data, Predictive Analytics, Operational Performance Management (OPM), Analytics, Business Analytics, Business Intelligence, Business Performance, Cloud Computing, Internet of Things, Operational Intelligence, Uncategorized
Over the last four years Domo, a new brand in cloud-based data and analytics software, has worked to enable its customers to understand, collaborate and act on data to achieve business results. Led by its founder and CEO, Josh James, the company has worked to deliver software that provides both a good user experience and business value. Recently, at its 2015 customer conference Domopalooza, the company presented itself and its products to the general public. I had a chance to meet with company executives, employees and customers and view its products at this high-energy event and entertainment that I have not seen in years.
Topics: Sales Performance, Supply Chain Performance, Human Capital, Mobile Technology, Operational Performance Management (OPM), Operational Performance, Analytics, Business Analytics, Business Collaboration, Business Intelligence, Business Performance, Cloud Computing, Collaboration, Financial Performance, Governance, Risk & Compliance (GRC), Domo, Risk & Compliance (GRC), SAB Miller
This has been a dramatic year for Informatica, a major provider of data integration software. In August it was acquired and taken private by Permira funds and Canada Pension Plan Investment Board for about US$5.3 billion. This change was accompanied by shifts in its management. CEO Sohaib Abbasi became chairman and now has left, and many executives were replaced while Anil Chakravathy became CEO from being the Chief Product Officer. The new owners appear to have shifted the company’s strategic priorities to emphasize profitability with reduced headcount and return on the purchase investment. Despite these changes, during the past six months Informatica has made key product announcements that will impact its future and the future of data management.
Topics: Big Data, Data Quality, Master Data Management, MDM, Operational Performance Management (OPM), Cloud Computing, Data Integration, Data Management, Data Preparation, Governance, Risk & Compliance (GRC), Informatica, Information Management, Business Performance Management (BPM), Information Optimization, Risk & Compliance (GRC)
The need for businesses to process and analyze data has grown in intensity along with the volumes of data they are amassing. Our benchmark research consistently shows that preparing data is the most widespread impediment to analytic and operational efficiency. In our recent research on data and analytics in the cloud, more than half (55%) of organizations said that preparing data for analysis is a major impediment, followed by other preparatory tasks: reviewing data for quality and consistency (48%) and waiting for data and information (28%). Organizations that want to apply analytics to make more effective decisions and take prompt actions need to find ways to shorten the work that comes before it. Conventional analytics and business intelligence tools are not designed for data preparation, but new software tools can enable business users independently or in concert with IT to perform the tasks needed.
Topics: Big Data, Sales Performance, Supply Chain Performance, Human Capital, Marketing, Monarch, Operational Performance Management (OPM), Customer Performance, Business Analytics, Business Intelligence, Business Performance, Data Preparation, Financial Performance, Governance, Risk & Compliance (GRC), Information Management, Uncategorized, Business Performance Management (BPM), Datawatch, Information Optimization, Risk & Compliance (GRC)
Ventana Research defines a human resources management system (HRMS) as the set of applications and associated processes that store and manage the employee information used by an organization’s human resources department. New technologies make it possible for the HRMS to perform better and be easier to use by HR professionals and members of the workforce. The range of evolving technologies impacting the development of the HRMS include business analytics, big data, cloud computing, mobile technology, business collaboration, social media and wearable computing. These advances enable organizations to streamline the processes that the HRMS supports and more efficiently take advantage of competencies that already exist in the workforce. The changes are so substantive for organizations and their HR departments that we have undertaken new research calledNext-Generation Human Resources Management Systems.
Topics: Social Media, Human Capital, Human Capital Management, Mobile Technology, Operational Performance Management (OPM), Business Collaboration, Cloud Computing, Business Performance Management (BPM), HR, HRMS
The importance of product information management (PIM) has become clear in recent years and especially as it relates to master data management. As I recently wrote handling this business process effectively and using capable software should be priorities for any organization in marketing and selling its products and services but also interconnecting the distributed supply chain. Our research on product information management can help organizations save time and resources in efforts to ensure that product information is an asset to facilitate efficiency in many business processes. Through years of benchmarking, we have developed a blueprint for managing and improving product information. Using this approach enables companies to more effectively align and link their activities and processes. Of course achieving effectiveness also requires using applications that create consistent, reliable product information. We regularly update our Value Index for PIM to enable companies to evaluate vendors and their applications’ suitability for use in all business processes requiring product information.
Topics: Big Data, Master Data Management, Sales Performance, Supply Chain Performance, Enterworks, Marketing, Operational Performance Management (OPM), Stibo Systems, Webon, Business Performance, CIO, Financial Performance, IBM, Informatica, Information Management, Oracle, Information Optimization, Product Information Management, Riversand
Ventana Research defines product information management (PIM) as the practice of using information, applications and other technology to effectively support product-related processes across the customer, commerce and supply chain. As organizations increase the number and diversity of products and services they offer to customers and partners, they increasingly need to address limitations in the ways they manage and distribute product information, including related attributes and content that describes the products. At the same time, competitive pressures require them to be able to incorporate large amounts of new content – video and images, for example – quickly while ensuring that the information presented to customers is accurate, operational processes run uninterrupted and timely data is available for business analysis. In an environment in which consumers, suppliers and partners use multiple channels to get to product information – including websites, kiosks, smartphones and tablets – it is essential that the organization always be able to present complete and up-to-date product information to inspire interest and facilitate purchases.
Topics: Big Data, Master Data Management, Supply Chain Performance, Governance, Marketing, Operational Performance Management (OPM), CIO, Information Management, Business Performance Management (BPM), Financial Performance Management (FPM), Information Optimization, Product Information Management, Sales Performance Management (SPM)
Our recently completed benchmark research on data and analytics in the cloud shows that analytics deployed in cloud-based systems is gaining widespread adoption. Almost half (48%) of participating organizations are using cloud-based analytics, another 19 percent said they plan to begin using it within 12 months, and 31 percent said they will begin to use cloud-based analytics but do not know when. Participants in various areas of the organization said they use cloud-based analytics, but front-office functions such as marketing and sales rated it important more often than did finance, accounting and human resources. This front-office focus is underscored by the finding that the categories of information for which cloud-based analytics is most often deemed important are forecasting (mentioned by 51%) and customer-related (47%) and sales-related (33%) information.
Topics: Big Data, Software as a Service, Operational Performance Management (OPM), Analytics, Business Analytics, Business Collaboration, Business Intelligence, Customer & Contact Center, Operational Intelligence, Business Performance Management (BPM), Data, Information Optimization
The Performance Index analysis we performed as part of our next-generation predictive analytics benchmark research shows that only one in four organizations, those functioning at the highest Innovative level of performance, can use predictive analytics to compete effectively against others that use this technology less well. We analyze performance in detail in four dimensions (People, Process, Information and Technology), and for predictive analytics we find that organizations perform best in the Technology dimension, with 38 percent reaching the top Innovative level. This is often the case in our analyses, as organizations initially perform better in the details of selecting and managing new tools than in the other dimensions. Predictive analytics is not a new technology per se, but the difference is that it is becoming more common in business units, as I have written.
Topics: Big Data, Microsoft, Predictive Analytics, alteryx, Operational Performance Management (OPM), Customer Performance, Analytics, Business Analytics, Business Intelligence, Business Performance, Location Intelligence, Oracle, Information Optimization
Managing prices has always been an activity of keen interest to businesses, but it has become even more critical to do it well. Over the past decade many companies have found their ability to raise prices has been constrained by intense competition resulting from Internet commerce, global competition and other factors. One tool for dealing with this pressure is price and revenue optimization (PRO), an analytic methodology that calculates how demand varies at different price levels and then uses that algorithm to recommend prices that should optimally balance revenue and profit objectives. Computer-supported PRO began in earnest in the 1980s as the airline and hospitality industries adopted revenue management practices in efforts to maximize returns from less flexible travelers (such as people on business trips) while minimizing the unsold inventory by selling incremental seats on flights or nights in hotel rooms at discounted prices to more discretionary buyers (typically vacationers). Price and revenue optimization algorithms are designed to enable a company to achieve fatter profit margins than are possible with a monolithic pricing strategy. Using PRO, airlines and hotels catering mainly to less price-sensitive business travelers found they could match discounters’ fares and rates to fill available seats and rooms without having to forgo profits from their high-margin customers.
Topics: Big Data, Performance Management, Sales, Office of Finance, Operational Performance Management (OPM), Analytics, Business Analytics, Business Performance Management (BPM), Financial Performance Management (FPM), Sales Performance Management (SPM), analytical application, Price Optimization