Business Analytics for the Business User Thomas H. Davenport Many organizations are embracing business analytics as their processes and decisions become more data-intensive and require optimization. Successful implementation of business analytics requires organizational change in a variety of areas, from leadership and strategy, to analyst recruiting and retention. However, the technology environment for business analytics must also change given changes in business requirements and available technology. In particular, the technology environment for the business user is the one in greatest need of simplification and change. In order to understand the future analytical technology environment for business users, it s helpful to understand the past one which was largely focused on reporting, so I ll refer to it by that name. I ll briefly describe key attributes of the past reporting environment, each of which is likely to change in the future. I ll argue that there is not one future technology environment for business analytics, but actually three different ones including one intended for business users. Of course, there are already a few organizations both vendors and users beginning to employ these new technologies and environments. Their use in the future, however, will become much more broadly distributed. The Reporting Technology Environment The past technology environment for reporting was largely a monolithic environment, with both quantitative analysts and business users being expected to employ the same tools and data sources. That environment worked relatively well for professional analysts, but the much larger group of business users was not generally served well by it. The characteristics of that environment that matter for business users are described below. Multi-Purpose Applications Reporting technologies have since at least the 1970s been multi-purpose. Users were provided an extensive toolbox of analytical methods, techniques, and reporting formats. It was the job of the analyst or the decision-maker to decide what tools were appropriate for what analytical context. This, of course, required a high degree of sophistication one that many analysts and almost all business decision-makers lacked. Many data environments for reporting were also multi-purpose. The idea behind an enterprise data warehouse is to support a variety of analyses and decisions. Less popular data marts were intended to support a single type of analysis, or at least a narrow range. Copyright 2011 International Institute for Analytics.
Warehoused Data The data for analysis in the reporting paradigm comes from a data warehouse or mart. This acted as a staging area for access by reporting applications and tools. If you wanted data in your warehouse, you first had to follow an ETL (extract, transform and load) process to get the data out of your transaction system and into your warehouse or mart. If you wanted to employ data originating in multiple business systems (not to mention external data, which was rarely included in warehouses), there would typically be extensive integration activities that preceded even the ETL process. When organizations built an enterprise data warehouse (EDW) using these approaches, the result was a complex environment with many data sources and possible report outputs. There is certainly value to EDWs, but business users have had difficulty realizing it. Generally Complex For avoidable and unavoidable reasons, reporting environments are typically complex often too much so for the typical business user. As noted above, the tool choices are typically quite large, which increases complexity. Serious analytical tools tend to have less-than-ideal user interfaces, although leading vendors have made inroads into solving this problem. Because of the availability of multiple report formats and data sources, even reporting-oriented tools can be complex for business users to master. Large data warehouses are complex to navigate as well. Premise- and Product-Based Reporting tools have generally been based on the customer premise, and have been sold primarily as products, rather than services or solutions. This may contribute to the complexity problem, in that having to worry about memory and storage limitations and other implementation issues are problems for users. Despite the enthusiasm for software-as-a-service and cloud computing, however, this has been one of the relatively less problematic aspects of the reporting environment. Industry-Generic Historically, the reporting tools sold to a customer in one industry were the same tools provided to those in another there was little or no tailoring of tools for particular industries. This is despite the fact that each industry has business problems that are best solved with a particular analytical approach. And transaction software (e.g., ERP) vendors have long customized their products by industry. While a solution to almost any industry-specific problem can be cobbled together with generic reporting tools, the skills to do this are not widely available at least not among business users. Copyright 2011 International Institute for Analytics.
Problems with the Reporting Model The reporting model often favored professional analysts over business users. Analysts with high levels of technological and quantitative expertise were well-served in this environment. All the data and analytical methods that they could ever need were available, and any question could be answered, any decision supported. However, for the average business user, there were too many tools in the toolbox and too much data in the warehouse. While the concepts of self-service reporting, user drill-down and business intelligence for the masses were often discussed, they didn t happen often. Another problem with the reporting technology environment for business users is that the technology is either too close or too far from the decisions it is supposed to support. In the case of highly departmental applications, the technology does support a specific decision, but it may be difficult to scale or be shared across the company. In the multi-tool, multi-data, multi-purpose enterprise reporting environment described above, the technology is too far from the decisions it is designed to support; software is too difficult to employ by business users, and enterprise data warehouses are too vast to easily navigate. The Future of Analytical Technology for Business Users For the remainder of this paper, I ll describe how each of the foregoing attributes is either already beginning to change, or promises to change in the future, for business users. The most important aspect of the future environment is that it is no longer monolithic. There is not one environment, but rather three different ones. (Figure 1) The Reporting bubble in the upper left symbolizes the multi-purpose, multi-tool environment I ve just described, and it was intended to service both professional analysts and business users though since the latter was a much larger group, the graphic portrays it primarily in that group s camp. Copyright 2011 International Institute for Analytics.
Figure 1 The Changing Analytical Technology Environment Multi- Purpose Application Breadth Single- Purpose Reporting Analytical Apps Analyst Sandbox Embedded Analytics Business Users Primary Users Professional Analysts Since the multi-purpose reporting environment didn t serve business users well, future environments won t be in that cell of the matrix. Instead, they ll evolve into three other primary environments: The single-purpose environment for business users, which I ll call analytical apps because of their resemblance to apps on iphones and other smartphones. This environment is simple to use, and allows business users to easily find the data and produce the queries and reports they need to make specific decisions. Because of their simplicity and small size, these apps should accelerate the cycle of insights-to-decisions-to-action for many managers and organizations. This is the newest analytical environment. The multi-purpose environment for professional analysts, which I ll call the analyst sandbox. This environment provides multiple tools and data sources for analysts that can understand them all and effectively choose among them. It is similar to the old reporting environment, except that there is no longer an assumption that it serves business users, so it doesn t have to be simplified. Its Copyright 2011 International Institute for Analytics.
primary purpose can be the creation of advanced analytics, rather than standard or ad hoc reports. In many organizations this environment already exists, so it will undergo the least change of the three. The single-purpose environment for professional analysts, which I ll refer to as embedded analytics because the primary reason for involving professional analysts in single-purpose applications is to achieve scale and real-time delivery. There are relatively few embedded analytics environments today primarily because they are difficult to develop and integrate but when they exist they require the technological and analytical skills of professional analysts. In the sections below I ll address how the reporting environment will evolve into these new environments, with a primary focus on the tools used by business users. Creating the Analytical Apps of the Future Single-Purpose, Industry- Specific, and Simple Going forward, for relatively simple analytical applications for business users requiring human exploration and interpretation (i.e., non-embedded analytics), multi-purpose analytical packages are not appropriate. Instead, we ll see analytical apps, or single purpose tools that are linked to a particular type of decision. If you need to do a sales forecast, the app will do that and nothing more. Moreover, I believe that these tools will be tied closely to a particular industry. The sales forecasting tool will be designed to forecast retail sales, or discrete manufacturing sales. If you want to do shipment load optimization in a transportation firm, there will be an app for that. The industry-specific apps will know what data are typically employed in an industry, and be able to link to that data easily with only a modicum of system integration work. The combination of decision types and industries will eventually yield thousands of discrete apps. The closest analogy to analytical apps is the apps ( application seems too grandiose a word) environment for smartphones. The simplicity, specificity, accessibility and ease of use of these applications have made both smartphones and their applications take off. It took smartphone apps to demonstrate the power of simple, context-specific solutions that any mainstream user could employ with virtually no training or documentation. Like smartphone apps, these analytical tools will be relatively simple to use and will be guided. They will have intuitive, touch-based interfaces. They will guide users through the process of analyzing the data and even making the resulting decision. They could not only do the needed calculations on the data, but will also steer the user through the process of ensuring that the data are well-suited to it, interpreting the results, and Copyright 2011 International Institute for Analytics.
making a decision based on them. They should provide a faster and better return on information in many business analytics domains. The data environment itself will also need to be different in the analytical app context. Data will need to be quickly and easily supplied to apps from a variety of systems and databases, both internal and external. External data could include competitor activity, overall macroeconomic data, or prospective customer behavior. Data will need to be trusted, comprehensive, and integrated at the point of the app. These analytical apps may be developed by software vendors, consultants and integrators, or internal developers (typically IT professionals and professional analysts) within organizations. Some vendors and consultants, for example, are beginning to offer industry-specific analytical applications or solutions designed to address a single decision. For example, in the pharmaceutical industry, a consultant makes available single-purpose, industry-specific tools for sales forecasting and promotion analysis. In addition, many companies have already developed industry-specific, single-purpose analytical apps for their own use. Wells Fargo bank has developed a tool for testing the relative effectiveness of different types of ATM machines. Amazon.com has developed single-purpose applications to optimize truck loadings. Netflix has single purpose applications to optimize the location of new distribution centers. Professional analysts at Merck have developed an analytical app for determining whether a vacancy in the sales force should be filled. The tool accesses the data necessary to perform the analysis, and leads the business user normally a regional sales manager through the decision process. Perhaps at some point there will be an app exchange for companies to sell or exchange analytical apps that are not deemed to provide competitive advantage. Service- and Solution-Based It would be consistent with the analytical apps environment to have application services delivered as services, rather than premise-based products. That s a simpler approach to providing such apps, and business users wouldn t have to worry about new versions and updates. Service-based applications would also facilitate the use of analytics on mobile devices for industry and process contexts that require them. Not surprisingly, many vendors are beginning to offer analytics as a service, and I expect this trend to continue and accelerate particularly for analytical apps environments. Solutions consisting of bundled products and professional services may not be as necessary in the future as they are today, because apps will be simpler to use by business users. However, it is possible that services will still be necessary to configure apps and ensure that they are drawing upon the correct data sources. For this reason, Copyright 2011 International Institute for Analytics.
it s reasonable to expect some degree of solutions orientation on the part of major vendors. Centrally-Coordinated It seems ironic that in a shift to analytical apps for business users, there will be more coordination by a central IT function. After all, there is little or no central coordination for iphone apps. However, even with analytical apps, there will be a need for some central coordination, although business users will probably initiate their use. They will need to be developed and integrated, and some of that work will be done by internal IT organizations. They will also require data, and IT and data management professionals will need to help provide it. And for apps that are popular across enterprises, vendors may well provide site-license pricing that would require central coordination and distribution. Finally, to avoid the multiple versions of the truth problem, these experts need to ensure that different analytical applications don t overlap, and that similar applications use similar data. Of course, for embedded analytics and analytical sandboxes, IT organizations have typically played important roles in the past, and they will continue to do so. Integrated Vendors For both analytical apps and embedded analytics applications, separate analytics vendors will become part of larger integrated firms offering transaction processing software and services. Of course, this transformation is already largely complete; large software and hardware providers have already acquired most of the freestanding analytical and business intelligence software vendors. These large, integrated vendors are beginning to introduce offerings that integrate analytical capabilities with other software tools. Examples of this integration include: Creating small analytical apps that link to particular modules of transaction software e.g., a trade promotion analysis application linked to the trade promotion transaction system for a retail ERP system. Embedding of analytics and algorithms into transaction software e.g., introducing an automatically-calculated customer lifetime value analysis into the order management function of an ERP system; Implementing in-database processing of calculations for more rapid processing of data-intensive analytics (independent analytics vendors are pursuing this same approach through partnerships and alliances); Copyright 2011 International Institute for Analytics.
Inclusion of reporting if not advanced analytics capabilities in the inmemory versions of transaction software, which offer rapid response and clickbased report design; Incorporation of data warehouse, data mart, and on-demand data assembly by traditional database and storage vendors. This sort of integration is well on its way and will only accelerate. The remaining independent analytics vendors will attempt to match this integration by focusing primarily on the analytical sandbox, and by increased emphasis on partnerships and alliances for embedded analytics. Large services and systems integration vendors are also incorporating analytics into their practices in a substantial way. These firms also focus heavily on transactional and other enterprise software capabilities, and are likely to be active in integrating analytical functions into those environments. Summary These changes in the technology environment for business user-centric analytics will not take place immediately, but they are already happening and will become more widely distributed over time. Some organizations may need to emphasize one of the future environments more than others; those with primarily reporting needs will probably emphasize analytical apps, and firms needing a lot of advanced analytics may emphasize the analytical sandbox. Firms with a strong process and transaction orientation may emphasize the embedded analytics environment. Although analytical apps may represent the bulk of business analytics activity because of the large size of the user base, most large organizations will probably need elements of all three environments in order to support their key decisions with data and analysis. Particularly for business user analytics, we are likely to see more change in the next few years of analytical technology than we have seen in the last few decades. The change is long due, and promises a much closer and more effective link between information and decision-making than ever before. Copyright 2011 International Institute for Analytics.