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Data is an essential ingredient for every aspect of business, and those that use it well are likely to gain advantages over competitors that do not. Our benchmark research on information optimization reveals a variety of drivers for deploying information, most commonly analytics, information access, decision-making, process improvements and customer experience and satisfaction. To accomplish any of these purposes requires that data be prepared through a sequence of steps: accessing, searching, aggregating, enriching, transforming and cleaning data from different sources to create a single uniform data set. To prepare data properly, businesses need flexible tools that enable them to enrich the context of data drawn from multiple sources, collaborate on its preparation to serve business needs and govern the process of preparation to ensure security and consistency. Users of these tools range from analysts to operations professionals in the lines of business.
Data preparation efforts often encounter challenges created by the use of tools not designed for these tasks. Many of today’s analytics and business intelligence products do not provide enough flexibility, and data management tools for data integration are too complicated for analysts who need to interact ad hoc with data. Depending on IT staff to fill ad hoc requests takes far too long for the rapid pace of today’s business. Even worse, many organizations use spreadsheets because they are familiar and easy to work with. However, when it comes to data preparation, spreadsheets are awkward and time-consuming and require expertise to code them to perform these tasks. They also incur risks of errors in data and inconsistencies among disparate versions stored on individual desktops.
In effect inadequate tools waste analysts’ time, which is a scarce resource in many organizations, and can squander market opportunities through delays in preparation and unreliable data quality. Our information optimization research shows that most analysts spend the majority of their time not in actual analysis but in readying the data for analysis. More than 45 percent of their time goes to preparing data for analysis or reviewing the quality and consistency of data.
Businesses need technology tools capable of handling data preparation tasks quickly and dependably so users can be sure of data quality and concentrate on the value-adding aspects of their jobs. More than a dozen such tools designed for these tasks are on the market. The best among them are easy for analysts to use, which our research shows is critical: More than half (58%) of participants said that usability is a very important evaluation criterion, more than any other, in software for optimizing information. These tools also deal with the large numbers and types of sources organizations have accumulated: 92 percent of those in our research have 16 to 20 data sources, and 80 percent have more than 20 sources. Complicating the issue further, these sources are not all inside the enterprise; they also are found on the Internet and in cloud-based environments where data may be in applications or in big data stores.
Organizations can’t make business use of their data until it is ready, so simplifying and enhancing the data preparation process can make it possible for analysts to begin analysis sooner and thus be more productive. Our analysis of time related to data preparation finds that when this is done right, significant amounts of time could be shifted to tasks that contribute to achieving business goals. We conclude that, assuming analysts spend 20 hours a week working on analytics, most are spending six hours on preparing data, another six hours on reviewing data for quality and consistency issues, three more hours on assembling information, another two hours waiting for data from IT and one hour presenting information for review; this leaves only two hours for performing the analysis itself.
Dedicated data preparation tools provide support for key tasks in areas that our research and experience finds that are done manually by about one-third of organizations. These data tasks include search, aggregation, reduction, lineage tracking, metrics definition and collaboration. If an organization is able to reduce the 14 hours previously mentioned in data-related tasks (that including preparing data, reviewing data and waiting for data from IT) by one-third, it will have an extra four hours a week for analysis – that’s 10 percent of a 40-hour work week. Multiply this time by the number of individual analysts and it becomes significant. Using the proper tools can enable such a reallocation of time to use the professional expertise of these employees.
This savings can apply in any line of business. For example, our research into next-generation finance analytics shows that more than two-thirds (68%) of finance organizations spend most of their analytics time on data-related tasks. Further analysis shows that only 36 percent of finance organizations that spend the most time on data-related tasks can produce metrics within a week, compared to more than half (56%) of those that spend more time on analytic tasks. This difference is important to finance organizations seeking to take a more active role in corporate decision-making.
Another example is found in big data. The flood of business data has created even more challenges as the types of sources have expanded beyond just the RDBMS and data appliances; Hadoop, in-memory and NoSQL big data sources exist in at least 25 percent of organizations, according to our big data integration research. Our projections of growth based on what companies are planning indicates that Hadoop, in-memory and NoSQL sources will increase significantly. Each of these types must draw from systems from various providers, which have specific interfaces to access data let alone load it. Our research in big data finds similar results regarding data preparation: The tasks that consume the most time are reviewing data for quality and consistency (52%) and preparing data (46%). Without automating data preparation for accessing and streamlining the loading of data, big data can be an insurmountable task for companies seeking efficiency in their deployments.
A third example is in the critical area of customer analytics. Customer data is used across many departments but especially marketing, sales and customer service. Our research again finds similar issues regarding time lost to data preparation tasks. In our next-generation customer analytics benchmark research preparing data is the most time-consuming task (in 47% of organizations), followed closely by reviewing data (43%). The research also finds that data not being readily available is the most common point of dissatisfaction with customer analytics (in 63% of organizations). Our research finds other examples, too, in human resources, sales, manufacturing and the supply chain.
The good news is that these business-focused data preparation tools have usability in the form of spreadsheet-like interfaces and include analytic workflows that simplify and enhance data preparation. In searching for and profiling of data and examining fields based on analytics, use of color can help highlight patterns in the data. Capabilities for addressing duplicate and incorrect data about, for example, companies, addresses, products and locations are built in for simplicity of access and use. In addition data preparation is entering a new stage in which machine learning and pattern recognition, along with predictive analytics techniques, can help guide individuals to issues and focus their efforts on looking forward. Tools also are advancing in collaboration, helping teams of analysts work together to save time and take advantage of colleagues’ expertise and knowledge of the data, along with interfacing to IT and data management professionals. In our information optimization research collaboration is a critical technology innovation, according to more than half (51%) of organizations. They desire several collaborative capabilities ranging from discussion forms to knowledge sharing to requests on activity streams.
This data preparation technology provides support for ad hoc and other agile approaches to working with data that maps to how business actually operate. Taking a dedicated approach can help simplify and speed data preparation and add value by enabling users to perform analysis sooner and allocate more time to it. If you have not taken a look at how data preparation can improve analytics and operational processes, I recommend that you start now. Organizations are saving time and becoming more effective by focusing more on business value-adding tasks.
CEO and Chief Research Officer
Most people in business management admit that sales is more an art than a science. Organizations have long struggled to find the right mix to improve its effectiveness, and few get the most out of available technology. For many the default is still to use sales force automation (SFA) and spreadsheets to manage processes and try to increase the productivity of sales staff. In our view they should take a holistic approach to sales processes from contact to close and support everything from sales forecasting to pipeline management to compensation with applications designed for these purposes. Those in sales operations need to apply analytics to understand and fine-tune sales activities. Those in sales management need applications that can help recruit, engage and retain the best talent. Even more than elsewhere in business, in sales people matter, and the organizations that most empower their teams are likely to get the best results. Optimizing people and processes requires a balance of information and technology to support the various needs of the sales organization.
Our research in this critical area of business during 2015 will examine applications designed to provide effective execution in sales processes. These applications cover a strikingly wide range of areas including deal management, forecasting, quota and territory, compensation and commissions, product information management, planning and tracking, learning and competency, coaching and objectives, contract automation and configure-price-quote. All rely on information and thus on technology to help people learn from and act on it. In today’s digital world the consistency and quality of product information is more critical than ever and more complex as digital assets such as images and video can make the difference in proposing and closing a sale. Analytics software can increase insights to improve sales execution and outcomes. Sophisticated techniques such as predictive analytics and data discovery can exploit the ever growing volumes now classified as big data. This trend has driven us to engage in new benchmark research for 2015 on next-generation sales analytics to advance knowledge of best practices and methods being adopted by innovative organizations. It will investigate new methods for using analytics to advance modeling and planning that can help sales groups recognize and respond to fast-changing trends and realize their potential. The need for improvement is clear in our next-generation business planning research, in which fewer than half of organizations said they are satisfied with their sales and operations planning (43%) and sales forecasting (47%).
Our research in sales compensation management explores the motivation and impact of commissions and rewards on sales performance. Sales compensation is very important to almost three-quarters (73%) of organizations, but more than one-third have impediments that are motivating management to consider further investments in sales compensation. Leading the list are inconsistent execution in sales, cited by three out of five organizations, and lack of sales effectiveness in almost half of organizations. These and other issues can be addressed by adopting dedicated applications for sales compensation, especially to replace spreadsheets, which almost two-thirds that use them said make it difficult to manage sales commissions efficiently. Managing sales compensation effectively also requires commitment, as I have written, and investing in capable software should be part of that commitment. Used properly it can provide a return in terms of the resources and time saved and improvement in the sales process. Our research finds other benefits of a dedicated sales compensation approach, most often aligning the sales force to business strategy and goals and increasing confidence in achieving the forecast.
Our recent benchmark research on recurring revenue reveals advantages in automatically billing for subscriptions for products and services on a repeating basis. However, the recurring revenue model requires the ability to manage a variety of services that customers can change at short notice and be billed for correctly and promptly. Managing recurring revenue is easier with a dedicated application that links the complete order-to-cash process. These applications also support not just the transactions but the full cycle of interactions with customers, which is the largest challenge for 55 percent of organizations, according to this research.
There also are new tools to help sales professionals improve their own campaigns using demand generation techniques that previously were available only in marketing. They can target prospects, schedule interactions and provide information that matters to prospects. This directly empowers individual sales reps to develop leads for the sales pipeline to achieve their quotas, which in turn can improve the accuracy of the sales forecast. The tools also can be used by teams. Half (53%) of participants in our sales forecasting research said that the forecast is a collaborative and team effort; that even more (57%) said the forecasting process is unreliable shows the need for improvement.
Another technology that can make a difference for sales people and processes is mobile technology, which can facilitate completion of tasks through smartphones and tablets. These portable tools can provide information and updates to sales people on the go. Our research in sales finds adoption booming; for example, tablets are now being used in 43 percent of sales organizations, and almost another one-third (31%) plan to deploy more or plan to start using them. Improvement in mobile applications designed for sales should drive growth even faster.
Mobile technology also can boost another innovative technology – collaboration, which is a natural part of the sales process. New methods for social collaboration are going beyond mimicking consumer social media to relevance in sales. For instance, using collaboration for coaching or gamification for earning badges and awards can spur sales people to compete and improve; these methods are being evaluated or planned for use in one-third and one-quarter of organizations, respectively, according to our sales compensation research. In the end collaboration is about improving the relevance of actions and the quality of decision-making. Collaboration can help improve the execution of any sales activity.
The nature and means of sales are changing, and sales organizations must change with them or be left behind. In pursuit of innovation they must make investments in technology to help them adapt. Cloud computing makes it simpler and more affordable for sales groups to onboard and use applications rapidly. In 2015 Ventana Research will explore further the use of all these innovative technologies in the context of what Sales needs to be successful. In addition we will undertake new research on the next generation of product information management and how it can empower sales through digital commerce by providing complete, consistent product information at all interaction points.
Sales organizations must face the realization that old processes such as merely managing accounts, contacts and opportunities and general-purpose tools such as sales force automation are not sufficient to optimize the execution and efficiency of sales activities. Better tools and processes can shift the focus from manual chores to value-adding work. For example, contract automation can save significant time every week that can be devoted to pursuing more deals. Sales departments must find ways to elevate the level of customer engagement and satisfaction that in turn drive more sales. In 2015 all involved in sales, from the front lines to operations to executives, must step up their game. Analytics can help them measure sales performance in various ways, from quota and revenue attainment (important in almost two-thirds of organizations) to building processes for continuous and sustainable improvement. We should not forget that sales is a team sport that requires leadership and coaching as well as finding and motivating talent.
Our Sales Research Agenda for 2015 will span the areas that matter most to the in-person and digital selling of products and services. We will provide insights and best practices that can help organizations select technology investments and gain fast time to value from them. Please follow as we strive to bring you information and guidance that can make the difference in your sales efforts.
CEO and Chief Research Officer