The Two Worlds of Business Intelligence: What You Need to Know
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The landscape of BI and analytics has changed. Historically, BI leaders have lived in a world where they centrally own and manage the data warehouse, and are responsible for delivering reports to the business. Today there are two distinct worlds, serving different types of users who have different analytical needs. One world provides casual users with reports and dashboards built to answer predefined questions using IT-certified data. The other gives power users unfettered access to any data to answer unanticipated questions using ad-hoc query and analysis tools. Join Wayne Eckerson, Founder and Principal Consultant at Eckerson Group, and Pedro Arellano, Senior Director of Product Strategy at Birst, as they share their thoughts on this new model for analytics. In this webinar you will: Understand the specifics about these two environments and learn what BI leaders must do to ensure success Learn organizational and technical methods to bring these two worlds together as a coherent whole Hear how Birst’s 2-tier analytics technology enables BI leaders to serve the needs of both types of users
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The Two Worlds of Business Intelligence: What You Need to Know
- 1. Two Worlds of Business Intelligence: What You Need to Know
- 2. 2 Please send questions using the online interface Attendees muted upon entry Webinar Housekeeping
- 3. 3 I. Introduction II. The Two Worlds of Business Intelligence III. Birst: 2-tier BI and Analytics IV. Q&A Agenda
- 4. 4 Featured Speakers Pedro Arellano Senior Director, Product Strategy Birst Wayne Eckerson Principal Consultant Eckerson Group
- 5. © Eckerson Group LLC Wayne Eckerson Hingham, MA wayne@eckerson.com (781) 740-9504 Wayne Eckerson• 20+ year thought leader in BI and analytics • Sought-after speaker and consultant • Principal Consultant, Eckerson Group • Industry analyst, TechTarget • Former director of research at TDWI • Author of hundreds of articles and reports Performance Management BI/Analytics Data Governance
- 6. © Eckerson Group LLC The Two Worlds of Business Intelligence: A New Model for BI and Analytic Success Wayne Eckerson Founder and Principal Consultant Eckerson Group
- 7. © Eckerson Group LLC Waves of BI BusinessValue High 2010-20 Reporting 1980’s Analysis Prediction Monitoring Query, Excel, OLAP, Visual Discovery Dashboards, scorecards Statistics, data mining, optimization Static & interactive reports “What happened?” “Why did it happen?” “What’s happening?” “What will happen?” Low = Reporting = Analysis 1990’s 2000’s AllusersBusiness analysts Execs,Mgrs, Workers Statisticians/Da taScientists Tools Users
- 8. © Eckerson Group LLC Business users who use information to do their jobs Executives Managers Front-line workers Monitor Analyze Casual Users 80% of employees Detail Interactive dashboards and reports Data warehouse and data marts Text- and voice-based search tools Built by IT Managed by IT 80% of time* Power Users 20% of employees Report Analysts Business analysts Statisticians Analyze Explore Mine Business users who are hired to analyze information. PublishData scientists Data warehouse Hadoop Operational systems NoSQL Systems External Data Text/ Documents Mashup Tools Visualization Tools Data Mining Tools Programming tools Managed by BUsSourced by BUs 100% of time* Two Worlds of Business Intelligence Top-down BI Bottom-up BI Users Functions Data Architecture Tools
- 9. © Eckerson Group LLC Reporting and Monitoring Predefined Metrics TOP-DOWN “IT-driven - Casual Users” Analysis Begets Reports Reports Beget Analysis Pros: - Data Consistency - Business Alignment Cons: - Hard to build - Hard to change - Expensive - Politically charged Pros: - Quick to build - Easy to change - Low cost - Politically uncharged Cons: - Data Consistency - Business Alignment DW Architecture Certified Data Exploration and Prediction Ad hoc queries Sandbox Architecture Non- certified Data
- 10. © Eckerson Group LLC Teams Reporting & Monitoring Predefined Metrics IT-driven – Casual User “Business Intelligence” DW Architecture Certified Data Business unit-driven – Power User Exploration and Prediction Ad Hoc Queries Analytics Architecture Non-certified data Corporate BI Team (Centralized) Dept Analysts (Decentralized) Data architects, ETL developers, report developers, data administrators, DW administrators, technical architects, requirements specialists, trainers, etc. Report analysts, business analysts, statisticians, data scientists TOP-DOWN BOTTOM-UP •Build data warehouse/data marts •Maintain BI “schema” and certified data •Create certified, production reports •Create ad hoc reports •Conduct analyses •Research business unit issues “Analytics Intelligence”
- 11. © Eckerson Group LLC Enterprise BI Managed by the corporate BI team Divisional BI Managed by Business Units Top-down BI Standard reports and dashboards for casual users Divisional Reports -Produced by embedded BI staff & -BI relationship Mgrs Divisional Analysis (ad hoc reports) - Produced by embedded analysts Enterprise Analytics (models/scores) - Produced by statisticians/data scientists Enterprise Data and Reports - Produced by corporate BI team BI/IT Generated (with business help) Business Generated (with BI help) Handoff Training Scores Models Data Expertise Requirements Prototypes Director of BI Bottom-up BI Ad hoc analysis and reports by power users
- 12. © Eckerson Group LLC Create appropriate data structuresMAD DASHBOARD ANALYST SANDBOXES Staging (Hadoop) Virtual sandbox (in DW) Physical sandbox (outside DW) BI sandbox (Server-based wrangling/analysis) Top Down BI Built by IT Around a data warehouse That answers known questions Using certified data Geared to casual users To monitor processes Bottom Up Driven by business unit Driven by power users Who answer unanticipated questions Using any data and tools To solve business problems Dept Dashboard 8-12 KPIs 20 dimensions Monitors key processes Tailored by role and user Answers predefined questions Answers 50% of all questions Sandboxes Upload desktop data Combine with corporate data Collaborate w/ other analysts IT monitors Reuse analysts Detail Analyze Monitor Managers Analysts Front-line Graphical Data KPIs Summary Data Dimensional Analysis Transaction Analysis Report or DW Data
- 13. © Eckerson Group LLC Power User Casual User IT-driven Certified data Predefined KPIs Business-driven Ad hoc queries New insights Source Data Source Data Staging Area and Archive Staging Area and Archive Business Intelligence (Reporting & analysis - DW) Business Intelligence (Reporting & analysis - DW) Analytics Intelligence (Exploration & Discovery) Analytics Intelligence (Exploration & Discovery) Ad hoc Query, Visualization, and Predictive Tools Ad hoc Query, Visualization, and Predictive Tools Corporate Dashboards & Reports (Web and mobile) Corporate Dashboards & Reports (Web and mobile) Continuous Intelligence (Alerting) Continuous Intelligence (Alerting) Analytics/SearchStreaming ETL Query Key: Rules Conceptual Architecture – Analytical Ecosystem Query Processing Systems Intelligence Tools AnalyticFunctions OperationalReporting Business Users Data Capture and Pre- processing Systems Operational Systems ContentIntelligence SemanticLayer Semantic Layer DataDirectory Relational Data (ERP, CRM, Legacy) Non- relational Data (Files, Images, Text, etc.) On Premise, Cloud, or Hybrid
- 14. © Eckerson Group LLC Governance 20 Models •Source data models •ETL/ELT models •DW/DM models •BI semantic models •DQ/validation rules Data Warehouse Data Warehouse Data Marts Data Marts Certified Reports Certified Reports Data Mining MartData Mining Mart BI/DW Team Statistician Casual User Data definitions Data policies Data standards Change processes Data Dictionary Data Dictionary MDM Hub MDM Hub Data Governance Office Chief Data Officer Data owners Data stewards Data Custodians Hadoop ClusterHadoop Cluster Hive Tables Hive Tables SQL Tables SQL Tables Tableau Server Workbooks Tableau Server Workbooks Hive Tables Hive Tables Hive/DW Tables Hive/DW TablesHadoop ClusterHadoop ClusterHadoop ClusterHadoop Cluster Semantic LayerSemantic Layer Data Scientist Business Analyst Report Analyst Casual+ User Python/Java/ Perl/MR Data wrangling tools Visual discovery tools Top-down governance/modeling Bottom-up governance/modeling
- 15. © Eckerson Group LLC Governance 21 • Source data models • ETL/ELT models • DW/DM models • BI semantic models • DQ validation/cleansing BI/DW Team Data Scientist Business Analyst Report Analyst Casual+ User Data definitions Data policies Data standards Change processes Data Governance Office Chief Data Officer Data owners Data stewards Data Custodians Top-down governance/modeling Bottom-up governance/modeling Publish Insights and Ad hoc Reports Organizational Handoff Technology Handoff Data Governance Check DG Gate Silver service Silver service Certified Reports Self service Self service Spread Marts Self-service analytics Self-service integration Self-service BI
- 16. © Eckerson Group LLC Key Challenges: Reconcile Opposites Top- down Bottom- up Speed Standards Business IT Dept Departm ent Enterprise
- 17. © Eckerson Group LLC Questions? I’m listening!
- 18. The Two Worlds Birst 2-Tier BI and Analytics
- 19. 25 Less time to Respond Explosion in end user demand Explosion of data volumes Costs inhibit large scale BI deployments $$$ Leveraging your data assets continues to get harder Limited amount of data is in an EDW
- 20. 26 Challenges facing IT Leaders • Are you able to provide a consistent view of the business? • Do your users have access to all the data they need? • How many different BI technologies are you supporting today? • How much time does your team spend developing reports? • Are you satisfied with the user adoption rates of your BI tools? • Are you worried users are downloading their own non-standard tools?
- 21. 27 Discovery BI “Freedom” • Leaves out non-technical users • Difficult to combine data from multiple sources • Data silos, no unified view • Many people in meetings with different numbers CENTRALIZED DATA Data Warehouse, traditional tools “Control” • Not all data available • No end-user self service • High costs and long turn-around timeframes • User frustration is growing Traditional BI “Control” The challenges with today’s BI landscape
- 22. 28 Enterprise DataTierUserDataTier Birst: 2-tier Analytics & Automated Data Refinement Data Warehouse Existing Data Stores Apps + Big Data User-ready Data Store Automated Data Refinement Business Model and Data Navigator ERP, CRM, SCM
- 23. 29 Automated Data Refinement (ADR)*: Making data user ready *Patent Pending
- 24. 30 Business Data Gone Wild EnterpriseBI Experience Enterprise reporting Predictive analytics Interactive dashboards Visual discovery Design studio Mobile analytics Open Client Interface Enterprise DataTierUserDataTier Data Warehouse Existing Data Stores Apps + Big Data User-ready Data Store Automated Data Refinement Business Model and Data Navigator ERP, CRM, SCM Birst: World class enterprise BI and Analytics
- 25. 31 Plugs into and extends existing EDW, application and discovery investments. Complete and consistent view of company data available to non technical users. Freedom to analyze all data. Data is secure, stored cost effectively, and controlled centrally. Deployed in cloud or on premise. Implement in weeks, not months or years. Significantly lower total cost of ownership. Benefits of this new unified approach
- 26. 32 Enter at business use case, deliver rapid results Birst ImpactInitiative 2X Increase in Assets under managementSales analytics 75% Uplift in Catalog SalesMarketing analytics $2M increase in profitsOperational analytics 5X increase in inventory turnsSupply chain analytics Financial analytics $7.5M increase in revenue Digital marketing 10% increase in click-through rate HR Analytics 80% reduction in reporting resources & time
- 27. 33 Delivering value across multiple use cases
- 28. 34 Who is Birst? “Birst has defined the pioneering vision of what a set of cloud BI and analytics capabilities should look like.” 2015 Business Intelligence and Analytics Magic Quadrant• Leader in Cloud Analytics • 10,000+ organizations rely on Birst across all verticals • Founded by Siebel Analytics veterans • 80+ Strategic Partners
- 29. 35 Proven results: Birst customers are the happiest Gartner Group Survey of Business Intelligence Customers, Aug 2014 Product Evaluation Frequency (Number of Unique Quarterly Pageviews) Full-Stack Business Intelligence Platforms TrustMap™ 5.0 4.5 4.0 3.5 3.0 2.5 0 2,400 4,800 7,200 9,600 12,000 AverageUser-Rating Trust Radius Survey of Full Suite BI Clients, December 2014 “ No. 1 in product functionality and customer (that is, product quality, no problems with software, support) and sales experience.”
- 30. 36 Our business value is different. Operationalizing more data, one business case at a time. Every person, every decision, made better. Our technology is different. Cloud BI. 2-tier Analytics and BI. Unique Automated Data Refinement. Our point of view is different. Not just about the front end -- the data is the critical piece. Agility and consistency of data are key. In Conclusion: Birst is different!
- 31. 37 Experience 2-tier Analytics • Learn more about Birst – 866-940-1496 / 415-766-4800 – sales@birst.com • Join a live product demo – Tuesdays and Thursdays 11am PT/2pm ET – Wednesdays at 12:30pm BST • Coming soon: Birst 2-tier whitepaper – How Birst enables a centralized and decentralized model for BI
- 32. 38 Thanks!
Notes de l'éditeur
- Hi, my name is __________________ and I’m today to talk to you about Birst’s cloud BI platform and how Birst can help you run your company in a better and more profitable way.
- Eckerson Group is a research and consulting firm that provides strategy, design and assessment services for small and large organizations in information-driven disciplines, including BI, DW, data governance, analytics, performance management, and Big Data. www.eckerson.com.
- From a user perspective, BI has evolved in four stages, alternating between waves of reporting and analysis. Reports serve the needs of casual users, while analysis serves the needs of power users. Each successive wave has delivered greater business value and catered to the information needs of new types of business users. Reporting During the 1980s, BI was just reporting, mainly static, paper-based reports. Our reports today are online and more interactive, even analytical. Reports generally answer the question, “What happened?” using pixel-perfect or page-oriented formats and geared to everyone. Analysis The second wave of BI delivered desktop query and reporting tools to business analysts so they could answer the question, “Why did it happen?” Excel is the dominant analysis tool, followed by desktop query/reporting and online analytical processing (OLAP) tools. The newest entrant to this category is visual discovery tools, which use in-memory storage and visualization to support speed of thought analysis. Monitoring In the 2000s, organizations rolled out dashboards to monitor operational processes and scorecards to monitor progress towards achieving strategic goals and objectives. These were primary geared to executives, managers, and front-line workers. Prediction In 2010, the focus shifted back to power users—namely statisticians and data scientists who create analytical models to describe patterns and relationships in the data and predict the future based on historical data.
- If you are going to bake a cake, you don’t start with the icing or the batter. You start with the customer to find out if they even like cake, and if so, what kind? Likewise, the key strategy for success in BI is to understand your audience of business users. In BI/analytics, there are two major categories of users: casual and power. These two types of users form the basis of the two worlds of BI: top-down and bottom-up. Top-down BI meets the needs of casual users. It is IT-driven and uses highly modeled data warehouses and dashboards populated with certified data to meet the analytical needs of casual users, which is 80% of the time is to monitor metrics that represent a core business process for which they are responsible. Bottom-up BI meets the needs of power users. It is totally ad hoc driven, with users using whatever low-cost query, reporting, data integration, analytics and visualization tool they can get their hands on to access whatever data they can find that will help answer questions from the business as they arise. Whereas the top-down environment answers predefined questions using modeled data sets, the bottom-up world answers unanticipated questions using unmodeled data sets.
- The two worlds are flip sides of the same coin: reports beget analysis and analysis begets reports. But the pros/cons are completely opposite: The top-down world delivers consistent data (precisely defined metrics, dimensions, and hierarchies that are well documented and managed) that aligns the business, whereas the bottom-up world delivers spreadmarts—highly customized data sets defined by individual analysts. The top-down world is highly modeled—data source, ETL, DW, and BI models must be created, aligned and continuously syncrhonized. As a result, the top-down world is hard to build and change and is costly. It’s also highly politicized—getting different business units to agree on the definition of core terms is not easy. The bottom-up world is none of those things. It’s a power user with a low-cost tool defining the world as he sees it without having to negotiate with anyone or certify his data set for quality, consistency, reliability, scalability, and security. Consequently, these two worlds tend to fly in different orbits and people in them tend to see each other as natural enemies instead of natural allies, which they are. The goal for you, as BI professionals, is to reconcile these two worlds into a holistic organizational and data architecture. It is not easy reconciling opposites, but it’s required for success.
- Most people think that there is only one BI team, but in reality, there are two—one in the top down world and one in the bottom up world. Top Down Team BI/DW developers comprise what most people think of as the BI team. They most often sit within the IT department and serve as a centralized shared service responsible for building and maintaining the data warehouse and building enterprise reports and dashboards. These folks build the “top down” BI environment by gathering requirements that get baked into data models and reports consumed by casual users. BI/DW developers are hired for their technical skills to fill a variety of roles. Although most BI/DW teams start with two people (a director/architect and developer/administrator), as the number of concurrent projects grows, the teams rapidly expand. The team hires workers to specialize in a particular technology, tool, or BI/DW discipline, creating an assembly line in a data and report factory. Bottom up Team Business analysts, on the other hand, operate in the “bottom up” BI environment, answering ad hoc questions by exploring and analyzing data in a variety of systems, not just the data warehouse. Analysts are nearly always (or ideally) embedded in business units and departments where they gain an intimate knowledge of the business rules, systems, and data that drive core processes in a given business domain. The title “analyst” is loosely defined in most organizations and applied to a variety of positions in many departments. For our purposes, however, an analyst is someone who is paid to analyze data for a living. These “power users” are comprised of business analysts, statisticians, data scientists, and data analysts.
- It’s important to give users access not just to the right BI tools, but to the right data. Here is a high-level breakdown of those structures. We’ve touched on this already in our description of top-down vs bottom-up. Top down The ideal data structure for casual users is the performance dashboard, which itself is a sandbox consisting of about a dozen metrics and 20 dimensions. Within the dashboard, there are three levels of data accessible to users: 1) graphical metrics 2) summary or dimensional data 3) transaction data. The core design principle for a performance dashboard is three clicks to any data. Bottom up In the bottom up world, the data structures are data sets. In a poorly organized environment, the IT department simply provides analysts with data dumps upon request. In a highly organized environment, the IT department creates a series of analytical sandboxes tailored to the needs and skills of different classes of analysts. For example, data scientists access staged data—which today is increasingly Hadoop. Other analysts will access the data warehouse via a DW partition or outboard DW replica, both of which allow analysts to upload their own and mix it with corporate data without affecting the performance of the regular DW for performance dashboards. Report analysts and less trained analysts will access data marts, cubes or visual discovery semantics via a visual analysis or query tool.
- It’s important to classify users before assigning them a BI tool. This requires taking an inventory of users and classifying them by the kinds of tasks they want/need to perform with data for different roles they play in the organization. This 80/20 matrix is one way to help facilitate that classification and mapping. The 80/20 matrix says that most business users play two roles in an organization, one that is more casual user oriented, and one that is more power user oriented. (There are other ways to make this division.) And 80% of the time they are playing one role while 20% of the time the other role. In this case, 80% of the time, casual users simply want to monitor, analyze, and drill to detail, in which case a MAD dashboard fulfills their needs. But the other 20% of the time, they want to act like power users, submitting ad hoc queries. In that case, they will rely on report and business analysts to meet their needs, although in the future, with search-based BI tools, they might be able to interrogate data directly. Power users are the reverse, with 80% of their time consumed performing ad hoc analysis using a variety of tools, and 20% of their consuming packaged information in a MAD dashboard. You can adapt this 80/20 matrix to reflect your organizational requirements.
- Live Office – Excel access to DBMS
- This chart shows how to map users to data structures. This is a key requirement for making it easy for users to navigate data sets while giving them self-service to get data they need without IT intervention. Casual viewers – those users who only view and navigate reports and dashboards are perfectly content with access to a report or dashboard. Casual users—users who want to create new reports but lack SQL knowledge will access a drag-and-drop semantic layer. Report analysts know rudimentary SQL and the BI tool and can access data marts designed for their department. Business analysts access wider range of data from the data warehouse, while data scientists access the raw data from a staging area (i.e. relational or Hadoop). Data analysts are SQL and data access experts who profile, obtain, or purchase data on behalf of data scientists. Each group of users provides first line of support to those in sequence before them.
- This is a segmentation of the BI tools market. 10 years ago, each oval would be distinct and non-overlapping. As the market has matured, vendors have expanded the functionality of their tools to meet business requirements and achieve market penetration. Today, the two most competitive segments are relational OLAP tools and visual discovery tools. ROLAP tools are represented by enterprise BI vendors, SAP Business Objects, IBM Cognos, MicroStrategy, SAS and Information Builders. Designed for enterprisewide deployments, these tools can support a myriad set of business requirements. They primarily deliver reports and dashboards to casual users (i.e. top down.) But they are generally expensive to get up and running. Visual discovery tools are bottom-up tools, geared primarily to business analysts, at least today. This segment has grown substantially in the past 10 years and threatens to overtake ROLAP tools for market dominance. These bottom-up tools now generate dashboards and reports so they straddle the line, making it possible for companies to meet both top-down and bottom-up needs with a single toolset. However, in general, these tools lack true enterprise capabilities that many organizations need.
- There is no longer one intelligence, there are four. We’ve talked about business intelligence (top down) and analytics intelligence (bottom up), but there is also continuous intelligence and content intelligence. All four dimensions seek to turn dat into insight and action, or more tactically, support reporting, monitoring, analysis, exploration, and mining applications. These four dimensions will make up BI by 2020. Maybe even today. At the intersection of the four intelligences are areas ripe for innovation and opportunity. For example, between analytics intelligence and continuous intelligence is decision automation—operationalizing analytical models. The question in most organizations is who is going to manage these four intelligences? Ideally, it all falls under one group or rolls up to one manager. In reality, these areas are often managed by different groups. If so, it’s imperative that the groups work closely together to align their initiatives.
- The blue elements in this logical architecture represent the traditional BI/DW environment, which, except for a data mining mart, offers very little in the way of usable data sources to power users. The elements in pink represent new data structures that organizations can/should implement to create a more holistic BI environment that supports the four intelligences and better meets the needs of power users.
- We are starting to see data governance start to emerge in the bottom up world—which by definition is anti-model/schema. In the top down world, the Data Governance Office defines data elements and manages data consistency and quality using MDM and the BI/DW team defines data elements used exclusively in the DW. In the bottom up world, we are creating an informal assembly line of modeling activities performed by different types of analysts using different types of corporate sandboxes. Data scientists access Hadoop using languages and once they find relevant data they create Hive tables that business analysts access. Report analysts access SQL tables in the DW/DM and create semantic layers for casual users.
- Governance comes in where the two worlds intersect. This is when power users publish data to decisions makers in the top-down world. There needs to be a gate between the two worlds. The gate consists of an organizational handoff between business analysts and report analysts, a data governance check that the published report adheres to published data standards, and a technology check, ideally, in which the report or data structure is presented to appropriate BI/IT professionals via a BI collaboration workflow. Below the line (bottom up) is spreadmart heaven, silo city, the wild west, and buyer beware environment. How do we keep this environment from spreading into the top down world where data is certified, locked down, modeled? Governance gates! How we handle the transition of data objects from bottom-up to top-down worlds determines the success of our BI/DW programs.
- There are numerous challenges facing BI directors who aspire to running a successful BI and analytics program. All can be summarized by the Zen-like pursuit of reconciling opposites. The goal is not to choose one side in a debate but to embrace both, synthesizing opposites to gain the best of both worlds. BI directors must reconcile the following four polar opposites: Top down versus bottom up As already mentioned, the key challenge facing BI teams is to resolve the tension between top down and bottom up intelligence, which involve different users, architectures, and tools. Creating an analytical ecosystem with multiple power user sandboxes is the key to creating synergy between these two approaches. Enterprise versus department Closely related, is the conflict between enterprise and departmental intelligence initiatives. How can enterprise BI teams meet the BI needs of departments and lines of business with a standardized service and platform? The key here is to federate both the organization and the architecture. Speed versus standards The classic tension between business and technical teams rages in the world of BI and analytics, where business teams require speed and agility to meet customer and market requirements, while IT enforces standards, security, and stability, all of which tend to put the brakes on business initiatives. The key is to adopt methods and approaches that enable IT to move fast without compromising data consistency and integrity. Business versus IT department The above challenges are encompassed by the final challenge which involves reconciling the tension between business and IT. The key here is for IT to acquire greater awareness of business dynamics, while the business develops greater appreciation for the process of developing data-driven applications and weighs benefits and costs of short- and long-term approaches. The best way to do this is hire business analysts to work on the BI/analytics team.
- Hi, my name is __________________ and I’m today to talk to you about Birst’s cloud BI platform and how Birst can help you run your company in a better and more profitable way.
- Every company wants to become data driven. But leveraging your data assets to improve business performance is becoming harder all the time. Why? Because: There’s been an explosion in data and data sources. Most of your data assets don’t live in your enterprise data warehouse. There’s been an explosion in end user demand for data. The speed of business is increasing so business leaders need more data and they need it faster. And finally, buying BI licenses for every person in a company is cost prohibitive for most organizations.
- - Too much data and too many data sources. No time to bring it all together and prepare it for analysis. - Too many different tools to support: ETL tools, reporting tools, dashboards, visual discovery tools. - IT acts as a reporting factory, spending their time creating new reports or responding to change requests. - End users going rogue. They won’t adopt legacy BI tool, instead downloading their own desktop-based visualization products. - No consistent view of the business. Each user and/or department has their own definitions (different definitions of customer, revenue, etc.)
- Here’s what a typical company looks like today. IT is continuing to use a data warehouse as the core of their effort to share information with the business. IT wants to control the company’s data and make sure it’s accurate, consistent, and secure. End users, however, have broken free, they are using their own tools on their own data to do discovery, although not in a great way. And these two separate approaches have created a chasm between end users and IT. It’s almost as if two separate worlds have evolved, and this situation creates a lot of problems for business leaders and for IT leaders as well. So what you have is a growing battle between Control and Freedom, with two separate approaches and two different sets of tools. And BTW, two sets of tools that don’t work well together. Both of these approaches are needed in today’s world. And they need to work together, because this is the key to unlocking the value of your data and making your company a data driven organization.
- So what have we built at Birst? We’ve built a 2 TIER business intelligence and analytics platform that creates what we call a USER DATA TIER. How does it work? Birst connects to your enterprise data, meaning “raw” data that lives in your source systems. We can also connect to data that lives in your data warehouse. These might include representations of objects or things in your company such as your customers, orders, people, financials, invoices, or parts. Birst then extracts data from any of the sources that you choose across your business. Next, Birst combines your source data and organizes it into a “user data store,” which makes it “analytic ready”. It does this in an automated way allowing you to create business-ready data quickly and easily, unlike the massive “ETL” work needed to create a traditional data warehouse from scratch. If you have data that is already analytic ready, like data in a data warehouse, then great, Birst does not need to prep that data for user queries, reports and analyses or replicate it in the cloud. So the benefit here is that Birst only processes the data that needs processing and creates and manages a unified and consistent view of all of your data, regardless of physical location or how the data is stored. And finally, we put a consistent set of business rules and definitions on top, or what we call a “semantic layer.” This semantic layer provides a single and consistent view of your data for everyone, and shields regular business users from having to understand how data is physically stored. Our USER DATA TIER sits on top of your current data sources, and connects to your data, puts it together in a consistent way, and makes it ready for business people to use and analyze. And best of all, it fits in with your current data management tools and can extend the value of your current data warehouse by combining data warehouse data with other sources and making it accessible to a broader audience. There’s no need to rip and replace anything that you are currently using! This back end technology is a big part of our secret sauce at Birst. No one else has this. No one else can do this. Our Two Tier solution is a breakthrough that we believe is going to revolutionize the business intelligence market, because it fits the way that companies and the associated data are organized and managed today.
- Clearly, one of the huge barriers to the rapid deployment of analytics and business intelligence is the complexity of the existing data inside your Enterprise The reason we can create this user data tier is that Birst’s technology, called ADR (“Automated Data Refinement”) allows you to extract and combine data in an automated way. This is really hard. Think about this for a moment. Your company’s data lives in multiple systems and is not generally structured in a way that’s conducive to doing reporting and analytics. We need to both reorganize the data in each your source systems, then give you a holistic view of this disparate data across a process or across an entire enterprise. Again, reformatting your data, and dealing with conflicting data elements and definitions, making it all make sense, …this is really hard. It’s something that’s generally done manually and takes a really talented team of data-centric people a long time to turn the diagram on the left into something that looks like the diagram on the right, something’s that ready for end users to do analysis on. But we’ve literally broken the code on how to do this. ADR is a patent pending data automation technology that is the key to our success at Birst. We let a computer do the hard work of combining your data and creating the end user data tier which is at the core of our capabilities and product differentiation. There’s no free lunch here. You can’t do BI without preparing your data for analysis. And you need to move from the data structure on the right to the data structure on the left. Only Birst has ADR and can move you from left to right in an automated way. Since no one else has ADR, their approach to BI either means someone else has to deal with the back end data preparation issues or they have to do this work manually, which is time consuming, expensive, and error prone. This is a huge differentiator for Birst, one that make our combination of capability and ability possible.
- With our unique user data tier in place, Birst then integrates end-user tools into that data model, unlocking the full breadth of your enterprise data regardless of how end-users need to consume it. This is critical as other products have separate end-user tools that require duplicate data modeling efforts or worse, are unaware of any enterprise data view and create data silos. Birst enables the full range of end-user access models, including : Enterprise reporting Predictive analytics Interactive dashboards Visual discovery Design Mobile Analytics And again, if you want to use Excel or other discovery tools like Tableau to access or update data in the Birst user data tier, this is fine. While we’re proud of the backend we’ve build, we’re equally product of our front end. We have the great combination of tools targeted a specific users and needs. Nobody else delivers a set of BI tools that has the blend of power and ease of use that we offer at Birst.
- If you’re an IT person, what’s in it for you? Why should you care about Birst’s two tier solution? Because we can plug into your current portfolio of databases, data warehouses, applications and discovery tools products in order to help you solve a number of enterprise-caliber business problems. With Birst you can deploy a scalable BI platform with powerful, centralized governance, so that you and your business users can always be assured of enterprise-caliber data integrity and security. Birst allows you to have robust control like you had in older enterprise platforms all while allowing your users to self-serve the analytics and reports needed to support every decision, every day. Birst also enables you to achieve, unprecedented reductions in total cost of ownership though a software model that can be deployed as an on-premise appliance, or in any cloud configuration.
- Now that we’ve had a chance to describe our product, let’s talk a little bit about how we engage with our prospective customers and how they are thinking about additional BI investments. Today, BI products are purchased with to solve specific problems and this is how many customers want to start with Birst: They want to deploy Birst to address a specific use case or business problem. And once they have the first problem solved, they move on from there. We’ve got a great track record at address problems in each of these areas: Sales Marketing Operations Finance Digital marketing Supply chain and HR And now that we have hundreds of customers using Birst, we can leverage the experience we’ve acquired from helping solve analytics problems in each of these areas. Each use case drives adoption and business value. And architecture is a perfect fit for a “land and expand strategy” because of how well our user data tier scales across an enterprise.
- At Birst, we have some fantastic customers using our platform in many different ways. These are just some of the examples from customers across different industry categories that use Birst of a variety of line of business process challenges.
- At Birst, We believe that the ultimate proof of concept of our claims lies in the success and happiness of our customers. Across different surveys of business intelligence users, Birst consistently ranks at the top when compared will all other leading Bi and analytical platforms. Combined with strong endorsement from leading industry analysts such as Gartner Group, we are proud of the measurable business results that our customers receive from Birst. The chart on the left shows the results of work done by Garner Group in a survey of business intelligence customers in Q3 of 2014 across all of the leading industry players. As you can see, Birst customers are extremely happy with their choice, with 0% of surveyed customers reporting a negative review of Birst. On the right, this December 2014 survey of business intelligence “full stack” customers by Trust Radius again showed that Birst ranked highest of all mainstream solutions. When it comes to customer satisfaction, Birst is second to none. Note to presenter: The reason that Tableau was not mentioned in the chart on the right was that they were not considered a “full stack” solution by the analyst firm of Trust Radius.
- So in conclusion, we think Birst is DIFFERENT and BETTER for three key reasons: 1. Our point of view is different: Need to combine capability, governance, agility, and the cloud. Data is the critical component of enterprise caliber business intelligence. 2. Our technology is different: Our two tier solution is revolutionary, ADR leverages patented automation technology to operationalize more data faster and push to more users, with a consistent and secure data, every time. 3. Our business value is different: Two Tier Analytics and BI means one platform, closing the gap between users and IT and putting the power of BI into the hands of people on the front lines while preserving investments in existing data, tools and processes. At Birst we’re solving one of the biggest problems that companies face today: Leveraging data to run your business in a fundamentally better and more profitably way. And helping you win in the marketplace. We’re delivering on the promise of BI. And we’re doing it in a way that no one else can. So now let’s change gears and talk about how we can put the power of Birst to work for your company.