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As a new generation of business professionals embraces a new generation of technology, the line between people and their tools begins to blur. This shift comes as organizations become flatter and leaner and roles, vr_ngbi_br_importance_of_bi_technology_considerationscontext and responsibilities become intertwined. These changes have introduced faster and easier ways to bring information to users, in a context that makes it quicker to collaborate, assess and act. Today we see this in the prominent buying patterns for business intelligence and analytics software and an increased focus on the user experience. Almost two-thirds (63%) of participants in our benchmark research on next-generation business intelligence say that usability is the top purchase consideration for business intelligence software. In fact, usability is the driving factor in evaluating and selecting technology across all application and technology areas, according to our benchmark research.

In selecting and using technology, personas (that is, an idealized cohort of users) are particularly important, as they help business and IT assess where software will be used in the organization and define the role, responsibility and competency of users and the context of what they need and why. At the same time, personas help software companies understand the attitudinal, behavioral and demographic profile of target individuals and the specific experience that is not just attractive but essential to those users. For example, the mobile and collaborative intelligence capabilities needed by a field executive logging in from a tablet at a customer meeting are quite different from the analytic capabilities needed by an analyst trying to understand the causes of high customer churn rates and how to change that trend with a targeted marketing campaign.

Understanding this context-driven user experience is the first step toward defining the personas found in today’s range of analytics users. The key is to make the personas simple to understand but comprehensive enough to cover the diversity of needs for business analytic types within the organization. To help organizations be more effective in their analytic process and engagement of their resources and time, we recommend the following five analytical personas: (Note that in my years of segmentation work, I’ve found that the most important aspects are the number of segments and the names of those segments. To this end, I have chosen a simple number, five, and the most intuitive names I could find to represent each persona.)

Information Consumer: This persona is not technologically savvy and may even feel intimidated by technology. Information must be provided in a user-friendly fashion to minimize frustration. These users may rely on one or two tools that they use just well enough to do their jobs, which typically involves consuming information in presentations, reports, dashboards or other forms that are easy to read and interpret. They are oriented more to language than to numbers and in most cases would rather read or listen to information about the business. They can write a pertinent memo or email, make a convincing sales pitch or devise a brilliant strategy. Their typical role within the organization varies, but among this group is the high-ranking executive, including the CEO, for whom information is prepared. In the lines of business, this consumer may be a call center agent, a sales manager or a field service worker. In fact, in many companies, the information consumer makes up the majority of the organization. The information consumer usually can read Excel and PowerPoint documents but rarely works within them. This persona feels empowered by consumer-grade applications such as Google, Yelp and Facebook.

Knowledge Worker: Knowledge workers are business, technologically and data savvy and have domain knowledge. They interpret data in functional ways. These workers understand descriptive data but are not likely to take on data integration tasks or interpret advanced statistics (as in a regression analysis). In terms of tools, they can make sense of spreadsheets and with minimal training use the output of tools like business intelligence systems, pivot tables and visual discovery tools. They also actively participate in providing feedback and input to planning and business performance software. Typically, these individuals are over their heads when they are asked to develop a pivot table or structure multi-dimensional data. In some instances, however, new discovery tools allow them to move beyond such limitations. The knowledge worker persona includes but is not limited to technology savvy executives, line of business managers to directors, domain experts and operations managers. Since these workers focus on decision-making and business outcomes, analytics is an important part of their overall workflow but targeted at specific tasks. For analytical tools this role may use applications with embedded analytics, analytic discovery and modeling approaches. Visual discovery tools and in many instances user friendly SaaS applications are empowering the knowledge worker to be more analytically driven without IT involvement.

Analyst: Well versed in data, this persona often knows business intelligence and analytics tools that pertain to the position and applies analytics to analyze various aspects of the business. These users are familiar with applications and systems and know how to retrieve and assemble data from them in many forms. They can also perform a range of data blending and data preparation tasks, and create dashboards and data visualizations along with pivot tables with minimal or no training. They can interpret many types of data, including correlation and in some cases regression. The analyst’s role involves modeling and analytics either within specific analytic software or within software used for business planning and enterprise performance management. More senior analysts focus on more advanced analytics, such as predictive analytics and data mining, to understand current patterns data and predict future outcomes. These analysts might be called a split persona in terms of where their skills and roles are deployed in the organization. They may reside in IT, but a lot more are found on the business side, as they are accountable for analytics tied to the outcomes of the analytics. Analysts on the business side may not be expert in SQL or computer programming but may be adept with languages such as R or SAS. Those on the IT side are more familiar with SQL and the building of data models used in databases. With respect to data preparation, the IT organization looks at integration through the lens of ETL and associated tool sets, whereas the business side looks at it from a data-merge perspective and the creation of analytical data sets in places like spreadsheets.

The roles that represent this persona often are explicitly called analysts with a prefix that in most cases is representative of the department they work from, such as finance, marketing, sales or operations but could have prefixes like corporate, customer, operational or other cross-departmental responsibilities. The analytical tools they use almost always include the spreadsheet, as well as complementary business intelligence tools and a range of analytical tools like visual discovery and in some cases more advanced predictive analytics and statistical software. Visual discovery and commodity modeling approaches are empowering some analyst workers to move upstream from a role of data monger to a more interpretive decision support position. For those already familiar with advanced modeling, today’s big data environments, including new sources of information and modern technology, are providing the ability to build much more robust models and solve an entirely new class of business problems.

Publisher: Skilled in data and analytics, the publisher typically knows how to configure and operate business intelligence tools and publish information from them in dashboards or reports. They are typically skilled in the basics of spreadsheets and publishing information to Microsoft Word or PowerPoint tools. These users not only can interpret many types of analytics but can also build and validate the data for their organizations. Similar to the analyst, the publisher may be considered a split persona, as these individuals may be in a business unit or IT. The IT-based publisher is more familiar with the business intelligence processes and knows the data sources and how to get to data from the data warehouse or even big data sources. They may have basic configuration and scripting skills that enable them to produce outputs in several ways. They may also have basic SQL and relational data modeling skills that help them identify what can be published and determine how data can be combined through the BI tool or databases. The titles related to publisher may include business intelligence manager, data analyst, or manager or director of data or information management. The common tools used by the publisher include business intelligence authoring tools, various visualization and analytic tools, and office productivity tools like Microsoft Office and Adobe Acrobat.

Data Geek: A data geek, data analyst or potentially as sophisticated as a data scientist has expert data management skills, has an interdisciplinary approach to data that melds the split personas discussed at the analyst and senior analyst levels. The primary difference between the data geek and the analyst is that the latter usually focuses on either the IT side or the business side. A senior analyst with a Ph.D. in computer science understands relational data models and programming languages but may not understand advanced statistical models and statistical programming languages. Similarly, a Ph.D. in statistics understands advanced predictive models and associated tools but may not be prepared to write computer code. The data scientist not only understands both advanced statistics and modeling but enough about computer programming and systems along with domain knowledge. The titles for this role vary but include chief analytics officer, enterprise data architect, data analyst, head of information science and even data scientist.

To align analytics and the associated software to individuals in the organization, businesses should use personas to best identify who needs what set of capabilities to be effective. Organizations should also assess competency levels in their personas to avoid adopting software that is too complicated or difficult to use. In some cases you will have individuals that can perform multiple personas. Instead of wasting time, resources and financial capital, look to define what is needed and where training is needed to ensure business and IT work collaboratively in business analytics. While some business analytics software is getting easier to use, many of the offerings are still difficult to use because they are still being designed for IT or more sophisticated analysts. While these individuals are an important group, they represent only a small portion of the users who need analytic business tools.

vr_bigdata_obstacles_to_big_data_analytics (2)The next generation of business intelligence and business analytics will in part address the need to more easily consume information through smartphones and tablets but will not overcome one of the biggest barriers to big data analytics: the skills gap. Our benchmark research on big data shows staffing (79%) and training (77%) are the two biggest challenges organizations face in efforts to take advantage of big data through analytics. In addition, a language barrier still exists in some organizations, where IT speaks in terms of TCO and cost of ownership and efficiency while the business speaks in terms of effectiveness and outcomes or time to value, which I have written about previously. While all of these goals are important, the organization needs to cater to the metrics that are needed by its various personas. Such understanding starts with better defining the different personas and building more effective communication among the groups to ensure that they work together more collaboratively to achieve their respective goals and get the most value from business analytics.

Regards,

Tony Cosentino

VP and Research Director

A few months ago, I wrote an article on the four pillars of big data analytics. One of those pillars is what is called discovery analytics or where visual analytics and data discovery combine together to meet the business and analyst needs. My colleague Mark Smith subsequently clarified the four types of discovery analytics: visual discovery, data discovery, information discovery and event discovery. Now I want to follow up with a discussion of three trends that our research has uncovered in this space. (To reference how I’m using these four discovery terms, please refer to Mark’s post.)

The most prominent of these trends is that conversations about visual discovery are beginning to include data discovery, and vendors are developing and delivering such tool sets today. It is well-known that while big data profiling and the ability to visualize data give us a broader capacity for understanding, there are limitations that can be vr_predanalytics_predictive_analytics_obstaclesaddressed only through data mining and techniques such as clustering and anomaly detection. Such approaches are needed to overcome statistical interpretation challenges such as Simpson’s paradox. In this context, we see a number of tools with different architectural approaches tackling this obstacle. For example, Information Builders, Datameer, BIRT Analytics and IBM’s new SPSS Analytic Catalyst tool all incorporate user-driven data mining directly with visual analysis. That is, they combine data mining technology with visual discovery for enhanced capability and more usability. Our research on predictive analytics shows that integrating predictive analytics into the existing architecture is the most pressing challenge (for 55% or organizations). Integrating data mining directly into the visual discovery process is one way to overcome this challenge.

The second trend is renewed focus on information discovery (i.e., search), especially among large enterprises with widely distributed systems as well as the big data vendors serving this market. IBM acquired Vivisimo and has incorporated the technology into its PureSystems and big data platform. Microsoft recently previewed its big data information discovery tool, Data Explorer. Oracle acquired Endeca and has made it a key component of its big data strategy. SAP added search to its latest Lumira platform. LucidWorks, an independent information discovery vendor that provides enterprise support for open source Lucene/Solr, adds search as an API and has received significant adoption. There are different levels of search, from documents to social media data to machine data,  but I won’t drill into these here. Regardless of the type of search, in today’s era of distributed computing, in which there’s a need to explore a variety of data sources, information discovery is increasingly important.

The third trend in discovery analytics is a move to more embeddable system architectures. In parallel with the move to the cloud, architectures are becoming more service-oriented, and the interfaces are hardened in such a way that they can integrate more readily with other systems. For example, the visual discovery market was born on the client desktop with Qlik and Tableau, quickly moved to server-based apps and is now moving to the cloud. Embeddable tools such as D3, which is essentially a visualization-as-a-service offering, allow vendors such as Datameer to include an open source library of visualizations in their products. Lucene/Solr represents a similar embedded technology in the information discovery space. The broad trend we’re seeing is with RESTful-based architectures that promote a looser coupling of applications and therefore require less custom integration. This move runs in parallel with the decline in Internet Explorer, the rise of new browsers and the ability to render content using JavaScript Object Notation (JSON). This trend suggests a future for discovery analysis embedded in application tools (including, but not limited to, business intelligence). The environment is still fragmented and in its early stage. Instead of one cloud, we have a lot of little clouds. For the vendor community, which is building more platform-oriented applications that can work in an embeddable manner, a tough question is whether to go after the on-premises market or the cloud market. I think that each will have to make its own decision on how to support customer needs and their own business model constraints.

Regards,

Tony Cosentino

VP and Research Director

IBM’s SPSS Analytic Catalyst enables business users to conduct the kind of advanced analysis that has been reserved for expert users of statistical software. As analytic modeling becomes more important to businesses and models proliferate in organizations, the ability to give domain experts advanced analytic capabilities can condense the analytic process and make the results available sooner for business use. Benefiting from IBM’s research and development in natural-language processing and its statistical modeling expertise, IBM SPSS Analytic Catalyst can automatically choose an appropriate model, execute the model, test it and explain it in plain English.

Information about the skills gap in analytics and the needvr_bigdata_obstacles_to_big_data_analytics (2) for more user-friendly tools indicates pent-up demand for this type of tool. Our benchmark research into big data shows that big data analytics is held back most by lack of knowledgeable staff (79%) and lack of training (77%).

In the case of SPSS Analytic Catalyst, the focus is on driver analysis. In its simplest form, a driver analysis aims to understand cause and effect among multiple variables. One challenge with driver analysis is to determine the method to use in each situation (choosing among, for example, linear or logistic regression, CART, CHAID or structural equation models). This is a complex decision which most organizations leave to the resident statistician or outsource to a professional analyst. Analytic Catalyst automates the task. It does not consider every method available, but that is not necessary. By examining the underlying data characteristics, it can address data sets, including what may be considered big data, with an appropriate algorithm. The benefit for nontechnical users is that Analytic Catalyst makes the decision on selecting the algorithm.

The tool condenses the analytic process into three steps: data upload, selection of the target variable (also called the dependent variable or outcome variable) and data exploration. Once the data is uploaded, the system selects target variables and automatically correlates and associates the data. Based on characteristics of the data, Analytic Catalyst chooses the appropriate method and returns summary data rather than statistical data. On the initial screen, it communicates so-called “top insights” in plain text and presents visuals, such as a decision tree in a churn analysis. Once the user has absorbed the top-level information, he or she can drill down into top key drivers. This enables users to see interactivity between attributes. Understanding this interactivity is an important part of driver analysis since causal variables often move together (a challenge known as multicollinearity) and it is sometimes hard to distinguish what is actually causing a particular outcome. For instance, analysis may blame the customer service department for a product defect and point to it as the primary driver of customer defection. Accepting this result, a company may mistakenly try to fix customer service when it is a product issue that needs to be addressed. This approach also overcomes the challenge of Simpson’s paradox, which is a hindrance for some visualization tools in the market. On subsequent navigations, Analytic Catalyst goes even further into how different independent variables move together, even if they do not directly explain the outcome variable.

Beyond the ability to automate modeling and enable exploration of data, I like that this new tool is suitable for both statistically inclined users (who can use it to get r-scores, model parameters or other data) and business users (whom visualizations and natural language walk through what things mean). Thus it enables cross-functional conversations and allows the domain expert to own the overall analysis.

I also like the second column of the “top key driver” screen, through which users can drill down into different questions regarding the data. Having a complete question set, the analyst can simply back out of one question and dive into another. The iterative process aligns naturally with the concept of data exploration.

IBM seems to be positioning the tool to help with early-stage analysis. From the examples I’ve seen, however, I think Analytic Catalyst would work well also as a back-end tool for marketers trying to increase wallet share through specific campaigns or for efforts by operations personnel to reduce churn by creating predefined actions at the point of service for particular at-risk customer populations.

IBM will need to continue to work with Analytic Catalyst vr_ngbi_br_importance_of_bi_technology_considerationsto get it integrated with other tools and ensure that it keeps the user experience in mind. Usability is the key buying criteria for nearly two-thirds (64%) of companies, according to our benchmark research into next-generation business intelligence.

It is important that the data models align with other models in the organization, such as customer value models, so that the right populations are targeted. Otherwise a marketer or operations person would likely need to figure this out in a different system, such as a BI tool. Also that user would have to put the analytical output into another system, such as a campaign management or business process tool, to make it actionable. Toward this end, I expect that IBM is working to integrate this product within its own portfolio and those of its partners.

SPSS Analytic Catalyst has leaped over the competition in putting sophisticated driver analytics into natural language that can guide almost any user through complex analytic scenarios. However, competitors are not standing still. Some are working on similar tools that apply natural language to sophisticated commodity modeling approaches, and many of the visual discovery vendors have similar but less optimized approaches. With the less sophisticated approaches, the question comes down to optimizing vs. satisfying. Other tools in the market satisfy the basic need for driver analysis (usually approached through simple correlation or one type of decision tree), but a more dynamic approach to driver analysis such as offered by IBM can reveal deeper understanding of the data. The answer will depend on an organization and its user group, but in fast-moving markets and scenarios where analytics is a key differentiator, this is a critical question to consider.

Regards,

Tony Cosentino

VP and Research Director

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