By Cassandra Balentine
An organization’s data is arguably one of its greatest assets. However, it is only significant if properly cleaned, managed, and leveraged. As more businesses begin to understand the power of data, and technology trends such as cloud computing, mobility, and the Internet of Things (IoT), continuously bring in new data, many don’t have the internal skills and manpower to properly harness it.
Self-service business intelligence (BI) tools help businesses manage and visualize data without the need for data scientists and IT staff. This isn’t to say that IT isn’t involved, but once implemented, others can take the wheel.
According to Gartner’s 2016 Magic Quadrant for Business Intelligence and Analytics Platforms, most business users and analysts within organizations will have access to self-service tools to prepare data as part of the shift to deploying modern BI platforms by 2018.
“Self-service BI tools reduce time to actionable insights,” sums up Bipin Singh, senior product manager, TIBCO Spotfire. “Business users want to respond to opportunities as soon as possible to generate a competitive advantage. Self-service BI tools enable users to explore their data and look at it in a way that makes sense to them. The faster you generate insights that you can act upon, the faster you can respond to business opportunities.”
Jeremy Sokolic, VP, product marketing, Sisense, agrees, stating that when insights are easy to access in a timely fashion, self-service BI can dramatically improve performance across the business. “When KPIs are transparent and easily measured, teams are more responsive and focused on achieving specific outcomes,” he adds.
The benefits of self-service BI include the reduced need and cost of IT resources, faster time to insight, and agility. In this article, we discuss best-case scenarios for self-service BI, including early adopters; common challenges associated with self-service BI tools; tips on what businesses should look for when researching self-service BI tools; and highlights of self-service BI tools on the market today.
Early Adopters and Use Cases
BI is utilized by nearly every industry and every size business. It is the level of use, details of the data, and accessibility of data that make it powerful. While larger enterprises may have the funds to invest in a skilled team of data scientists, many businesses do not. Therefore, BI software leaders have opted to provide simplified BI tools tailored to the business user.
“As the name implies, self-service data discovery is universal, and highly relevant to any part of the organization that wants to explore and make decisions with the data,” says Dan Sommer, senior director, market intelligence lead, Qlik. He says organizations with a big focus on the sales cycle would be a good example of the type of business quick to adopt self-service BI tools. He says this technology has essentially revolutionized the sales cycle. “Self-service BI enables salespeople to optimize results by evaluating sales data to close business faster and more profitably. Self-service BI also empowers sales teams to solve their own problems, creating the optimal conditions for user-driven innovation,” he shares.
Sokolic says early adopters include anyone from small- to medium-sized businesses that do not have internal IT resources and cannot afford expensive consultants to organizations with business units within large organizations that do have access to IT, but cannot wait for the lengthy turnaround needed to implement and maintain traditional BI solutions.
“Early adopters of self-service BI are companies that have quickly realized the business advantages that are available when you have direct and immediate access to advanced analytics,” says Marc Altshuller, VP, Watson Analytics and BI, IBM Analytics.
He explains that until recently, access to advanced and predictive analytics was controlled by statisticians and data scientists, but self-service analytics allow business users to streamline analytic projects without investing in complex IT infrastructure and ultimately gain insights to critical data faster.
In the next few years, he believes access to new data sources—and the ability to smartly augment internal data analysis with these new and existing data sources—will enhance existing use cases and generate new ones. “Incorporating social media, weather, Internet of Things (IoT), or economic data will allow users to uncover even more powerful insights that they wouldn’t have otherwise discovered. We also anticipate an uptick in the number of users relying on self-service data discovery within an organization,” says Altshuller.
Vijay Anand, senior director, product marketing, MicroStrategy, notes that self-service tools cater to line of business and departmental needs for applications ranging from customer segmentation to sales forecast, and performance analysis. “While self-service solutions typically serve business teams, IT teams are also heavily adopting these tools,” he explains. “From large banks to retail chains, IT teams are now able to speed up the cycle time to produce dashboards by collaborating more easily with business users.”
According to Andy McCartney, director of BI and analytics product marketing, Information Builders, software companies are often seen to be on the leading edge of innovation hen it comes to adopting self-service technologies. “Their willingness to experiment with innovation has played an important role in the development of self-service,” he offers.
Ellie Fields, VP of product marketing, Tableau, has seen all kinds of use cases, from retail organizations empowering regional and store managers with the data needed to make decisions based on previous sales and inventory data to marketers evaluating the effectiveness of their event participation or social media efforts.
“Organizations across many verticals, such as healthcare, travel/hospitality, financial services, professional services, and technology are taking advantage of self-service BI,” notes Sokolic. “In addition, functional leaders across the business, including sales, marketing, human resources, finance, operations, and customer support are all well positioned to utilize self-service BI,” he adds.
With the proliferation of self-service BI platforms in the modern business world, Sokolic says companies are inventing new ways to use data analysis and visualization every day. “Some examples of companies that have found remarkably innovative use cases for business analytics software include the role of complex data in politics on the campaign trail and in marketing and advertising applications,” says Sokolic. He says both types of organizations find themselves having to pull from large, disparate data sets and need to quickly mash up the data to gain insights into ongoing campaigns. “A self-service approach enables the agility to shorten time to insight to make real-time adjustments that can significantly impact campaigns in a matter of hours instead of days or weeks. We expect self-service BI to expand beyond dashboards and screens in the future to make it simpler for business users to consume insights.”
We’ve outlined the benefits of self-service BI and pointed to applications where they are starting to make an impact, but it is important to note some of the challenges associated with the technology.
Sommer notes that businesses face a myriad of options that can be daunting to choose from. “The main challenge businesses face when looking to implement a self-service BI tool is that they have many options, but not many that offer the scope of capabilities they want. For example, a product may offer beautiful visualizations, but does it also grant the right level of governance? Will that graph or pie chart present just as nicely on a mobile device as it does on the desktop? Does it provide true discovery also for business users, not just analysts? Further, will it scale as discovery in the organization grows?”
Altshuller says one of the biggest challenges to getting started with self-service BI is having a clear understanding of all the different data sources available. “Not knowing how much data it has access to may cause an enterprise to overlook some of the business problems it’s trying to solve. Related to this is the readiness of the data for analysis,” he cautions. “Most data requires significant cleansing and shaping before it is ready to be analyzed, especially by the self-service user.”
Yet another challenge is matching users and their skill levels to the appropriate tools. For example, you wouldn’t give a business professional access to R—an open source programming language for statistical analysis—and expect them to start writing code.
Fields points to security and authentication as a main challenge. “Can IT properly secure that data so that these non-technical workers can only analyze the data and not change anything, and only have access to the data that is important to their role? You don’t want someone in the development team to have access to confidential employee information, and the human resources team doesn’t need to see complex product data that the development team is working on.”
She says successful organizations will make IT a true partner to the business, so that they can enable broad groups of users with the appropriate controls to achieve true data governance. “Thanks to this, business users don’t have to go fishing for their data or worry about its security, and IT can manage access to keep the data accurate and secure. When done right, data governance is about providing the right data to the right people whenever and wherever they need it so they can answer their own questions.”
McCartney says businesses are faced with not having a credible platform to manage the many requirements of a multi-user environment. They also must deal with a lack of governed data sources, and must ensure the distribution and collaboration with governed BI content. “In addition, trying to push a specialist tool down the throat of non-technical and non-savvy users can spell disaster, so businesses need to ensure the tool and environment adapts to the spectrum of needs.”
Choosing a Solution
When the time is right to invest in self-service BI, a few factors are worth considering. Challenges come in the form of cost, training, and a need for cultural buy in.
Singh says organizations should research out-of-the-box capabilities needed today and what will be needed in the near future. “Data exploration, connecting to data, including big data sources as well as data wrangling—merging, transforming, cleaning, drilling, enriching data—should all be considered part of the core capabilities for the self-service BI tool. The self-service BI should be future-proof and adaptable to changing needs of the organization.” He explains that geo analytics and advanced analytic capabilities should be built-in and users shouldn’t have to hunt for another piece of software should they need to work with location analytics and predictive analytics. “Governance and security features are also important to satisfy organizational needs for controlled access to intellectual property and data.”
Sommer stresses that self-service BI tools are never a one size fits all. “Establishing an analytics solution should come with a broader strategy. It’s very similar to building a house; you would not approach an architect without knowing the size, location, or budget of the structure you’re looking to build,” he says.
Start the research process by defining its use cases for a self-service BI tool, suggests Altshuller. What kind of business questions are you looking to answer? Will it be used across the organization or for a specific business role or industry? How much data are you looking to analyze and what format is it currently in? What type of insights are you trying to uncover? What is the skill set of your users and what type of solution will be best for their needs and abilities? “These are examples of the types of questions a company should answer before diving into self-service BI,” he states.
Anand says that with the majority of tools on the market today, there are plenty of options to solve a short-term problem, but the challenge becomes that investments made in those tools come with a restrictive shelf life. “Organizations often come to the painful realization that focusing on short-term issues and ignoring long-term goals and strategies have costly implications.”
He explains that standalone data discovery solutions may solve the immediate analytical needs of a small team, but fall short when it is time to scale to a larger team or the wider organization. “These point solutions fall short in regard to self-service capabilities like data preparation, automated distribution, and personalization. As a result, the organization will need to invest in multiple tools to meet the evolving needs of self-service deployed on a larger scale. It’s the nature of self-service, users will want to do more on their own so it’s important for tools to provide a more comprehensive set of self-service capabilities beyond data discovery.”
Anand adds that when larger organizations solely invest in point data discovery solutions, they often hit other roadblocks associated with a lack of security, governance, and scalability. “Beyond that, with analytics fast becoming a key part of new initiatives like big data and IoT, it’s more important to invest in tools that have a wider set of capabilities—including mobile transactions, real-time analytics, and in-memory analytics—as these will allow analytics initiatives to grow beyond initial goals.”
An important component when evaluating a self-service BI tool is how it fits into the overall enterprise data quality initiative, suggests McCartney. “After all, it is the technology tools that drive the strategy forward. The right solution will offer an array of capabilities—from data profiling, validation, and cleansing, through enrichment and data governance—to make information as timely and trusted as possible.”
A variety of vendors provide self-service BI tools that help organizations gain insights from their data without the need for IT.
Datawatch Monarch is a self-service data prep solution that enables business users to acquire, manipulate, and blend data from virtually any source. “Both data analysts and novice business users can reduce their data preparation time by acquiring and preparing data locked away in previously inaccessible multi-structured, semi-structured, and unstructured sources. With the ability to retrieve and use not only the right data, but all of the data required to get the whole story, Datawatch Monarch users can focus on preforming analysis that will result in timely, more informed business decisions and better operational processes,” says Dan Potter, CMO, Datawatch Corporation.
Datawatch Monarch complements self-service BI solutions. Potter says industry vendors, such as IBM, have selected the solution to tackle data prep to enable users to better harness the power of cognitive computing.
IBM Watson Analytics is a cloud-based analytic service that offers natural language cognitive querying, predictive analytics, and visual storytelling to help users make sense of their data. “Our strategy with Watson Analytics is to empower business professionals with the same cognitive powered predictive and prescriptive capabilities of a data scientist with an intuitive straightforward user experience and interface,” says Altshuller.
Watson Analytics offers a full range of self-service analytics—including access to data refinement and warehousing services—to make it easier for business users to acquire and prepare data for analysis and visualization. It automates steps like data preparation, predictive analysis, and visual storytelling for business professionals across data intensive disciplines including marketing, sales, operations, finance, and human resources.
“Watson Analytics democratizes data analysis by enabling business users to bypass constraints around hardware, software, and expertise—allowing them to uncover critical insights on their customers and business processes that they can use to increase sales, limit waste, and improve employee retention,” says Altshuller.
IBM Watson Analytics simplifies the data discovery process by guiding data exploration with tailored questions and answers based on users’ data. It then automatically generates and recommends visualizations that help simplify how organizations answer business questions, analyze trusted data, tell a compelling story, and take confident action on the business insights.
Informatica’s Cloud analytics solutions are designed for the breadth of self-service analytics use cases from sales operations dashboards, to heat map visualizations for financial KPIs, to business discovery from marketing data lakes. Informatica Cloud’s extensive data management offerings and capabilities include self-service data integration wizards, data cleansing and preparation tools, along with pre-built mapping templates. These enable business users to quickly build intelligent dashboards that leverage secure and connected data across cloud and on premises sources.
Information Builders helps companies drive their strategies forward and bridge the gap between static reports and flexible BI tools with WebFOCUS InfoAssist, part of the WebFOCUS BI platform. The solution is designed to help business users overcome the obstacles associated with traditional ad hoc reporting.
MicroStrategy Desktop is a solution available for PCs and Macs that provides business analysts with a powerful visual data discovery toolset that they are able to install and setup on their own, including all of the self-service capabilities needed to connect to data and build dashboards to make data-driven decisions. It also includes data preparation, data blending, advanced analytics, and D3 visualization support, most of which isn’t available in competitive tools. “While business analysts are the primary target market for this product, IT designers can use MicroStrategy Desktop for offline prototyping as well,” says Anand.
The company also offers MicroStrategy Web, featuring all of the data discovery capabilities offered by MicroStrategy Desktop available on a MicroStrategy Web interface. Using virtually any browser, Web users can access the full spectrum of MicroStrategy’s analytics capabilities beyond data discovery, from viewing and designing pixel-perfect reports, dashboards, and scorecards to administering core metadata objects, automatically publishing and distracting personalized content. A critical benefit of the Web interface is that it is connected to the centralized metadata architecture, making it easy to govern and secure access and distribution of information.
Organizations leverage MicroStrategy Mobile to put powerful decision-making tools directly into the hands of remote employees, so they’re engaged and productive while on the move. Using the MicroStrategy Mobile application, business users can access all of their desktop and Web-based reports, and data discovery dashboards directly on their iOS or Android devices. Beyond consuming reports and dashboards on a mobile device, the MicroStrategy App Platform provides tools to build powerful custom apps that put critical enterprise information in the hands of decision makers. These business apps combine sophisticated workflows, interactive analytical dashboards, transactional forms, and access to multimedia libraries into a productivity tool that can be customized for any user role.
Qlik is “essentially a tightly coupled front-end for analysis with a back-end for data management—all in one, providing depth and breadth, and a unique analytic experience that can also scale,” explains Sommer.
Qlik’s platform approach enables a range of use cases for self-service visualization as well as centrally deployed guided analytics, embedded and custom-built analytics, collaboration, and reporting. The company says its major differentiator is its innovative associative model, where users can explore, find associates, and see the whole story of all an organization’s data in a non-linear way.
Sommer says with Qlik, no data is left behind. “We don’t make anyone pre-determine the type of analysis they may want to do and then join the data to support that pre-conceived approach. We believe in equal opportunity for all data. We take it in any form and you are free to explore and use it in any way you like.”
Sisense handles the full cycle of BI, from data preparation to analysis and dashboard visualization, along with robust extensibility and governance. The company provides a single, stand-alone solution that can also be delivered as embedded analytics. Key components of the software include a visual environment for business users to import and prepare data for analysis, and a browser environment to create, share, view, and administer dashboards.
Tableau Server is a solution for larger organizations, which provides a lot of support for data governance. Projects and workbooks are secured at both the user and group level, administrators can set customer permission and filtering, and multi-tenancy options make it simple to separate users from content.
Fields says Tableau lets people see and understand their data. And where standard BI offers answers within days—or weeks, Tableau provides a way for users to answer their own questions and puts organizations on a rapid path to true self-service BI. Additionally, the Tableau solution deploys a live connection to the database for analytics, and lets users quickly build and share beautiful, effective dashboards—all without the need for programming or consultants. The result is high user adoption with minimal demands on IT resources.
TIBCO Spotfire allows users to interactively manipulate data and explore data as opposed to simply visualizing data. Spotfire provides immersive data wrangling as a built-in capability that allows users to merge, transform, clean, and enrich data during the analytics work without any external tools. More than 40 relational and big data stores are supported by Spotfire. The tool features a hybrid in-memory data engine is architected for massive scale. It has a smart recommendation engine built into the product that gives users automatically created like visualizations to choose from. “Users create process-specific dashboards and apps without any coding. They can also take advantage of comprehensive geocoding and mapping capabilities that are built in. Spotfire also features a fast runtime engine for R,” says Singh.
The solution is designed for business users, analysts, or data scientists who want to benefit from self-service analytics with minimal IT support.
In today’s world, waiting for an answer is not always an option. With self-service BI tools, businesses can gain important insights from their data without the need to involve IT. Self-service BI tools ensure this is done simply and securely for a variety of vertical markets.
“Organization and business units often deal with the problem of having too much data and not enough time. Self-service tools address that,” suggests Singh. “The insights and methodologies for generating the insights can be leveraged by users, business units, and organizations through collaboration, threaded discussions, and purpose-built analytic applications.”
These insights help users make better business discussions in every area imaginable—from marketing to human resources. SW
Jul2016, Software Magazine