Radiantadvisors self sufficient data discovery by design by Radiant Advisors

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SELF-SUFFICIENT DATA DISCOVERY BY DESIGN BUSINESS INTELLIGENCE HAS CHANGED

POWER OF DISRUPTION

Today’s business users are more data savvy; they are more comfortable with technology and more familiar with key analytics concepts. Further, they better understand the context of their data and their requirements for how they want to work with it on an every day basis. Technology itself has become more economical: the price of hardware isn’t as expensive, and solutions are more flexible and customizable than ever before with more emphasis on user experience (UX) and user interface (UI) design for simplicity and user intuition. Finally, with mainstream adoption beginning for big data for business analytics, companies are becoming increasingly more data-centric, and are finding more ways to exploit data for competitive advantage and enable more users across the organization to participate in the business process of discovery.

The fundamental extension of traditional BI to include discovery is one that has been shaped not only by the evolving needs and data aptitudes of the business, but by changes in the industry – and its emerging technologies, vendor marketplace, and tool ecosystem --, too.

This transition from traditional, predefined business intelligence (BI) to the new discovery culture has brought with it the evolution of self-service to self-sufficiency, as business users demand more independence to acquire, analyze, and sustain the discovery of new insights from their data. However, being truly self-sufficient requires that technical and non-technical users alike have the tools, environment, and access they need to contribute and collaborate in the discovery environment. And, because the discovery process is one of exploration that hinges on the ability for users to combine data sources and experiment with data within their unique business context, the self-sufficient business user requires independence from IT to define their own discovery experience. As discovery continues to reshape how we earn insights from our data, discovery tools must continue to balance user intuition and self-service capabilities with high-performance for sharable, actionable timeframe insights across the organization. True discovery tools will continue to be differentiated from traditional BI tools by their ability to facilitate “discovery by design,” and increase time to insight through speed, agility, and selfsufficiency inside a scalable solution that can quickly adapt to changing business needs while leveraging rich visualizations for meaningful storytelling that empowers users through ITindependence, collaboration, and mobility.

The story of disruption goes like this: in the years prior, traditional BI tools were routinely the subject of blame for being “too slow” when in reality it was the database behind the tool that was responsible for latency issues. The acquisition of the thenmajor BI vendors – Cognos, Business Objects, and Hyperion (who were subsequently acquired by IBM, SAP, and Oracle, respectively) – left a gap in the BI tool marketplace, and provided the opportunity for new vendors to capitalize on previously unaddressed weaknesses. Incumbent vendors were quick to move data in-memory to achieve high-performance analytics and iteration through caching for a better user experience. The nextgeneration of disruptive BI tools, then, focused on addressing performance through speed, integration, and agility; advanced visualization capabilities; and intuitive UX design and self-service. Today’s disruptive vendors focus on providing tools that facilitate true discovery by design: they are all about providing tools and technology solutions that make the end user – technical and non-technical alike, or the “modern data analyst” – more independent, which is the crux of self-sufficiency in the new discovery culture. However, self-sufficiency doesn’t happen in a silo, nor does it relate to making only singles users self-sufficient. The true discovery tools provide users the ability to connect to large volumes of new data without scripting and to easily join different data sets together to filter, query, and visualize at will to explore data in detail without choking the system or relying on IT. The user independence that powers discovery within the organization is not only influenced by the disruptive nature of the tool, but by the vendor itself and its ability to innovate, adapt, and listen to the voice of the customer in how it continues to enhance the user experience of their core tool. This is where buying companies can benefit by erring on the side of simplicity. While larger, more mature software (and vendors) have their place in the architecture, these have the tendency to fall victim to over-designed functionality and features, difficult to manage © 2014 Radiant Advisors. All Rights Reserved.

incremental updates, and slow response times for support. Smaller, more specialized and disruptive vendors are, instead, driven by customer needs to continuously refine the scope of their core product in a way that deepens the tool’s value without bloating the software in a way that loses sight of the core need it was initially designed to address. Ultimately, navigating the “new breed” of traditionally-minded tools versus truly disruptive discovery tools means we must concentrate first and foremost on the elements that make a tool designed for discovery – a robust, agile IT-independent and user-centric approach that has better access to data, agile highperformance, and is designed for collaboration. Additionally, to truly capitalize on the power of disruption, the vendor behind this tool should be dedicated to continuing to innovate and provide perpetual competitive advantage to its customers through its core technology. THE VALUE OF SPEED Because the discovery culture is a fail-fast environment it therefore places an inherent premium on speed and performance. It is an important note to add that this “need for speed,” too, has the prerequisite of the ability to quickly and agilely harness large amounts and varieties of data for exploration to enrich the discovery process. Remember, in the era of disruption of the BI marketplace, emerging new vendors were eager to fill the weaknesses of the large mega-vendors. One of these was speed for response time between the database and the tool stack, which was the catalyst for many vendors to move data into local desktop in-memory to achieve high-performance analytics at the user’s desktop and iteration through caching for a better user experience.

However, speed for the sake of speed on its own provides little more than an enticing thrill ride. The real value of speed to drive discovery has a direct relationship to the ability to quickly earn insights on data and facilitate interactive and immediate reaction. The value of speed, then, can be found at the intersection of actionable time to insight and the ease of the discovery process. This is the negative correlation between friction – the incremental events that add time and complexity to the discovery process through activities that slow down the process, like those that IT used to do for business users (i.e. connecting data sources, generating visualizations, or sharing insights back through dashboards and reports – all of which can be reduced or removed completely with robust self-service, visualization, collaboration, and sharing capabilities) and time to insight. As friction decreases, time to insight increases. This is the old 80/20 concept, wherein 80% of time is spent in data preparation, leaving only 20% of the time for data science and exploration. Removing friction reduces the barriers to discovery and subsequently increases time to insight – speed. Speed, then, is a function of friction, and the less friction in the discovery process, the more value speed can deliver to the business. DISCOVERY BY DESIGN Discovery is an iterative “many cycles” and “fail fast” revolving process wherein data is abstracted or extracted into in-memory – integrated, derived, modeled –, and then visualized to see insights into the data. Once new insights have been discovered (or not discovered), discovery continues on an exploratory “lather, rinse, and then repeat” cycle until new insights are eventually uncovered and can be shared back into the business. This is the exploratory nature of data discovery: the path isn’t

With cutting-edge database technology and a

The SiSense mantra of “Acquire, Analyze, and

data, SiSense empowers users with the self-ser-

flexible product architecture, SiSense provides

Sustain” echoes the three tenants of discovery

vice ability to access data, build, and customize

the advantages of having a single source of

by design (Verify, Validate, and Communicate).

interactive dashboards and reports and query

truth coupled with high-performance, multi-di-

First, SiSense provides the ability to unify dis-

data at record-breaking speed. Finally, through

mensional analytics, and without the rigor of

parate data into one centralized place to cre-

interactive and collaborative design, SiSense

traditional data warehousing and OLAP model-

ate a single version of the truth using built-in

web-based dashboards – and device-agnostic

ing. And, it is designed with the user in mind,

connectors to join multiple data sources and

mobility – enable sharing and immediate moni-

enabling true, IT-independent self-service for

formats through an intuitive drag-and-drop

toring and reaction-capability to data activity in

both technical and non-technical users.

approach. Then, with a variety of dashboard

an actionable timeframe that sustains dynamic

widgets to pinpoint the best visualizations for

always clear and rarely does the discovery process end up where the original idea was headed. Along the way, mini-insights may change the approach, cause the consideration of new data sources, or illuminate that data quality and stability may not be available and another discovery path is needed. This is also where collaboration, sharing, and collective innovation input can spark new ideas and insights.

should be designed specifically with the business user in mind – it should remove barriers for adopting a BI tool by providing a simple, intuitive user interface and functionality that is designed to empower the self-sufficient user to contribute, share, and discover their own insights – from easily unifying disparate data to building reports, designing visualizations, and sharing back to the larger community.

Discovery is more important today for two main reasons: business analytics and data science are driven by algorithms and data to discover a business solution that is currently unknown to the conscious business, and new, high volume, high velocity data needs to be analyzed to be understood. Having speed – coupled with performance and agility – as the backbone for discovery creates the foundation for a valuable discovery experience. Paired with self-sufficiency, interactive and collaborative sharing environments, visualization, and a strong mobile-orientation, we achieve true discovery by design and overcome barriers to empowering everyone in the organization with valuable insights in real-time.

Designed for Collaboration

Think of self-sufficiency as a triangle – with tools, environment, and access its three points. A failure in any of these areas points becomes an Achilles Heel in discovery. And, all three include the need for the proper administration and governance. Designed for Self-Sufficiency In the past, we – the BI industry-at-large – have come to define self-service as business users having self-service access to the information they need. Today, that definition is being redefined to be less about access and more about ability. The shift from self-service to self-sufficiency is a paradigm shift that is fundamentally intertwined with the extension of BI to discovery in that it changes the impact of the user from being able to consume something that has been predefined and provided to having the ability – through tools, environment, and access – to discover it independently. With the exponential growth in volume, variety, and velocity of data generated today, analytics in the data-driven organization requires users with business knowledge to become partners in discovery. Equipped with better tools, users are earning greater autonomy and IT independence. At the core of the concept of self-sufficient discovery by design is that true discovery tools

Collaboration is an investment strategy, and designing discovery for collaboration is two-fold. First, pairing self-sufficient discovery with collaborative capabilities fosters collective innovation and a more robust discovery environment by providing the ability for technical and nontechnical users to communicate through meaningful reports, dashboards, or visualizations. These can then be shared back into the analytical platform to encourage meaningful collaboration and shared insight; drive higher quality in context definitions; encourage cooperation and sharing between cross-functional units; and, ultimately, enjoy the benefit of having a broader network of creativity, constructive criticism, and business knowledge and awareness. Second, collaborative discovery closes the gap between business users and IT to nurture data democratization and facilitate the shift of IT (as central management and control) from Information Technology to Enablement Technology (ET) – wherein IT’s role will be that of a technology administrator, primarily responsible for enabling more self-sufficient opportunities for business users to gain more actionable insights that brings value to the business while simultaneously educating the business on IT processes and governance. Creating a collaborative environment establishes transparency, too, by supporting the sharing of current and relevant information between units. Finally, it provides a consolidated location for data governance and implementing underlying data security to centralize access permissions and act as a single unified environment to enforce roles and permissions across all federated data sources. Designed for Visualization As humans, we are intrinsically hardwired to be visual creatures. Keeping discovery visual leverages that innate capacity and © 2014 Radiant Advisors. All Rights Reserved.

empowers even the most non-technical user with the ability to meaningfully ingest, synthesize, and take action from their insights. Visualization within the discovery environment makes analytics more approachable to the self-sufficient business user by providing a powerful venue to access huge volumes and varieties of data and convey knowledge in a way that would not otherwise be possible. In fact, visualization vastly improves discovery in some cases and, in others, is the only way to work with and communicate large data sets. Simply, visualization within discovery is the vehicle to move from shared understanding to shared insight. Robust visualization capabilities – whether through dashboards, charts, or advanced visualizations – endow business users with the ability to access data and build well-designed, meaningful visualizations to visually explore data and discover insights at a glance through the careful distillation of images, color and design. Making visualizations interactive allows the exploration process to continue visually – a premise at the heart of visual discovery – and leverages our cognitive abilities of visual communication to enrich the discovery process and follow train-of-thought thinking to insights through discovery. Designed for Mobility While previously mobile has been relegated to an information consumption role, in a rapidly more and more mobile world mobility doesn’t just provide new opportunities for consuming and interacting with data and analytics: it fundamentally alters the paradigm by which we expect to consume and interact with data and analytics. Mobile data discovery, then, demands an intuitive, hands-on “touch” approach to visually interact with data in a familiar, compelling, and meaningful way. Going mobile makes discovery a portable experience, too, and brings with it the expectation of a responsive, secure, and device-agnostic mobility through consistent, cross-platform experiences for continued discovery that is irrespective of device with the ability to launch a discovery session on-the-fly. Today, having a mobile-first stance is the catalyst to enriching visual analytics and enabling self-sufficient, on-the-go business users in a new era of discovery. The continued increase in the mobility of secure and managed data access ensures that selfsufficient, IT independent business users have immediate time to insight and action with access to relevant, live data no matter

their physical location or device of preference. Ultimately, mobility is a personalized experience for each user, and further supports the need for disruptive discovery tools to continue to focus on the individualized user experience. CONCLUSION As mainstream adoption of big data continues to progress, focusing on self-sufficiency and high-performance in emerging discovery tools is key to earning the business value through discovery that is demanded by the data-driven business. With speed, performance, and agility forming the backbone for discovery, layering on the muscle of intuitive self-sufficiency, interactive and collaborative sharing environments, visualization, and a strong mobile-orientation will provide data-centric companies with the power they need to enable true discovery by design and empower everyone in the organization with valuable insights in a low-friction, actionable timeframe. And, as more and more discovery tools come to market, those that continue to concentrate first and foremost on the elements that enable discovery by design – a robust, agile, IT-independent and user-centric approach that has better access to data, agile high-performance, and is designed for collaboration – will continue to be disruptive in the market and allow companies to truly capitalize on the business process of discovery. BY: LINDY RYAN RESEARCH DIRECTOR, DATA DISCOVERY & VISUALIZATION