How to Architect the Bi and 265003

G00265003

How to Architect the BI and Analytics PlatformPublished: 11 August 2014

Analyst(s): Joao Tapadinhas

Many organizations have a business intelligence (BI) and analytics platformthat doesn't deliver according to expectations. Learn how to rearchitect it toimprove the impact on the organization and increase users' satisfaction.

Key Challenges In many organizations, the BI and analytics platform centrally provisioned by the BI team

is not fulfilling business users' demands, does not clearly correlate with business decisions andis overly focused on BI's technical aspects. Due to this, business units have started deployingsiloed solutions such as data discovery tools, vertical analytic applications, open-sourceanalytic workbenches and cloud BI.

BI teams, especially if located in IT, believe that the business analytics platform must be atightly integrated solution with as few components as possible preferably from a singlevendor to deliver a "single version of truth."

The capabilities typically offered are limited, including reports and dashboards, OLAP tools, adhoc access to data through SQL and eventually a data mining environment. There are gaps ininformation exploration and analytics accessible to business users.

BI and analytics data sources are usually stored in a data warehouse and domain-specific datamarts, containing structured data from the organization's business applications and provided toend users with access restrictions (often through BI reports).

Recommendations Build a BI and analytics platform composed of three tiers the information portal, analytics

workbench and data science laboratory with varying levels of information trust, analyticscapabilities and information access.

Provision the analytics workbench and the data science laboratory with broader access to moredata sources, according to the needs and skills of the users leveraging each tier.

Define information governance rules and supporting metadata to manage an integrated BI andanalytics portfolio, promote content between tiers and ensure information consistency.

Allow business users to explore the right tier for their needs and skill level, formalizing andsupporting the roles of the information analyst and data scientist as primary users of theanalytics workbench and data science laboratory.

Table of Contents

Introduction............................................................................................................................................2

Analysis..................................................................................................................................................3

Acknowledge Misperceptions, Evolve Beyond the Monolithic Mindset.............................................. 3

Build Upon the Gartner Business Analytics Foundational Tools.........................................................4

Follow the Business Analytics Framework...................................................................................4

Leverage the Spectrum of Analytics Capabilities......................................................................... 5

Apply a Pace Layer Model Approach to the BI and Analytics Platform........................................ 7

Rearchitect the BI and Analytics Platform......................................................................................... 8

Information Portal..................................................................................................................... 10

Analytics Workbench................................................................................................................ 11

Data Science Laboratory.......................................................................................................... 12

Understand the Characteristics of a Tiered BI and Analytics Platform............................................. 13

Gartner Recommended Reading.......................................................................................................... 15

List of Tables

Table 1. Platform Tiers: Similarities and Differences.............................................................................. 10

Table 2. Characteristics of a Tiered BI and Analytics Platform...............................................................14

List of Figures

Figure 1. Gartner's Business Analytics Framework................................................................................. 5

Figure 2. Analytics Spectrum..................................................................................................................6

Figure 3. Pace Layer Model....................................................................................................................7

Figure 4. Tiered BI and Analytics Platform...............................................................................................9

IntroductionIn many organizations, the BI and analytics platform is not fulfilling business users' demands and isinsufficient to achieve business objectives. The typical platform capabilities offered include reportsand dashboards, online analytical processing (OLAP) tools in some cases, ad hoc access to data

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through SQL for a limited number of users, and eventually a data mining environment oftenmanaged as an isolated solution.

An organization's established BI and analytics strategy and deployed tools are seldom enough tomeet users' needs because they are generally focused on systems of record reporting as opposedto delivering a range of capabilities to a range of users. Enforcing them without additional optionswill only worsen the existing problems.

To overcome limitations, business units have started deploying siloed solutions such as datadiscovery tools, vertical analytic applications, open-source analytic workbenches, or subscribing toalternative cloud BI platforms without IT's support or approval.

Due to these constraints, there is often a pressing need to rearchitect the BI and analytics platform(covered in this document), review the user skills and responsibilities, and reorganize the process ofhow content gets created and deployed (to be covered in a forthcoming note).

Analysis

Acknowledge Misperceptions, Evolve Beyond the Monolithic Mindset

There are a number of ingrained flaws in most organizations' BI and analytics platforms, as well as amisperception of their objectives and how to manage them. BI teams, especially if located in IT,believe that:

The business analytics platform must be a tightly integrated solution with as few componentsas possible preferably from a single vendor to deliver a single version of truth to theorganization.

Information can only be trusted if stored in the corporate data warehouse and delivered to theinformation consumer using BI artifacts, such as reports or dashboards.

Information created or manipulated by business users will inevitably produce discrepanciesthrough different analysis, leading to wrong decisions and generating chaos in the organizationover time.

IT's responsibility for information management stops at the BI semantic layer and IT-drivencontent. Business-driven analytic processes are out of scope and not supported by IT.

There are plenty of documented information-driven problems in the BI and analytics world thatcompel BI leaders to follow these beliefs and deploy a monolithic, centralized BI environment, whichends up being enforced on users regardless of its fitness to needs. Incumbent BI vendors, in favorof their own platforms, will encourage this approach too.

Gartner believes that a successful BI and analytics platform needs to evolve beyond the monolithicmindset. A transformation must occur to offer different solutions for the disparate needs of users,with a diverse set of integration levels striking a balance between trust and agility. The purpose is

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to help users achieve their business objectives through the use of the proper technology, not toeradicate the user-driven BI solutions that partially solve their problems today.

The resulting BI and analytics environment will also require changes in analytics and informationgovernance processes, as well as attribute new responsibilities to different personas in theorganization.

Build Upon the Gartner Business Analytics Foundational Tools

To support the BI and analytics platform transformation, Gartner has three foundational and highlycorrelated tools to consider:

Business Analytics Framework

Analytics Spectrum

Pace Layer Model

These tools are to be applied in conjunction, but let's start by analyzing them in isolation.

Follow the Business Analytics Framework

Figure 1 presents Gartner's Business Analytics Framework. See "Gartner's Business AnalyticsFramework" for a thorough review of this tool.


Figure 1. Gartner's Business Analytics Framework

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Consume

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Analytic Processes

Information GovernanceProcesses

Decision Processes

Pro

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esDecision Capabilities

Collaboration, Decision Making, Intelligent Decision Automation, Applications

Analytic CapabilitiesDescriptive, Diagnostic, Predictive, Prescriptive

Information Capabilities Describe, Organize, Integrate,

Share, Govern, Implement

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Business Models, Business Strategy, and Enterprise Metrics

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InformationTextImage MobileTransactionsSocial Audio VideoIT/OT Search

EngineDocuments

Source: Gartner (August 2014)

The Gartner Business Analytics Framework identifies the people, process and platform componentsthat support the transformation of information into better performance of the organization. The useof this tool is done by reading it from the top down, starting with the business outcomes and thenfiguring out the supporting analytic compositions and information required to achieve them.According to users' needs, the platform must be rearchitected with a broader set of technicalcapabilities (filling the gaps), new responsibilities and organization. Focusing on tools or vendorstandardization alone is not the answer.

The Framework is also very useful for defining current and future architecture states. The differencebetween them is the road map and includes changes in people and processes. The organizationwill, most likely, also need to reorganize and retrain the providers and users of BI and analytics. Thebusiness users must gain access to the proper analytic tools according to their goals and skills and a comprehensive range of data sources with varied data types, suitable granularity andappropriate access.

Leverage the Spectrum of Analytics Capabilities

Focusing on the platforms pillar of the Gartner Business Analytics Framework, in particular theanalytic capabilities component, we see four analytic styles that are further detailed in Figure 2. See"Extend Your Portfolio of Analytics Capabilities" for a thorough review of the Analytics Spectrum.


Figure 2. Analytics Spectrum

Decision Action

Decision Support

PredictiveWhat will happen?

DiagnosticWhy did it happen?

DescriptiveWhat happened?

Decision Automation

Human InputAnalytics

PrescriptiveWhat should I do?

Data


The analytic capabilities deployed in organizations are often limited to descriptive analytics, throughbasic reports and dashboards. With this capability, the question, "What happened?" can beanswered. After knowing "what," users will most likely also ask, "Why did it happen?" Properlyaddressing this requires far more agility and more advanced information exploration capabilities.Traditional BI deployments tend to have gaps in this area but IT usually overlooks the problematicimpact of this and continues to push the standard vendor and its unfit-for-purpose tools. As aconsequence, users will resort to Excel, ad hoc queries, data extractions and shadow IT teams toachieve their analysis goals.

BI leaders must extend the BI and analytics platform to diagnostic analytics to complementdescriptive analytics. This is where OLAP and in-memory data models are used to provide easy andspeed-of-thought navigation of data without a predefined query. Leveraging enhancements to thedata access level, we also see the need for improved semantic layers abstracting the complexity ofthe underlying physical model. This can make it much easier for self-service discovery without theaccompanying IT bottleneck found in a typical BI team.

Beyond the data layer, we see the introduction of newer data visualization tools, and this is wherethe focus of the rapid increase of data discovery tools is concentrated. But traditional tools can alsoprovide improvements with a greater focus on more comprehensive reporting (such as varianceanalysis), integrated planning, dashboards and KPI reporting.

Over time, with an increasingly higher analytics maturity level, the organization should move intopredictive and prescriptive analytics. These require a significant increase in the skill levels of thebusiness analysts. Predictive models require development and maintenance with complex logic andbusiness rules. They incorporate sophisticated methods that can also require a deep understandingof statistical or operational research.


Furthermore, organizations must realize that there is a need to blend all these different techniquesinto comprehensive solutions rather than leave them as discrete silos.

Apply a Pace Layer Model Approach to the BI and Analytics Platform

Figure 3 presents the Pace Layer Model. See "Applying Gartner's Pace Layer Model to BusinessAnalytics" for a thorough review of this tool.

Figure 3. Pace Layer Model

Systems of Innovation

Systems of Record

Systems of Differentiation

Levels of Integration Between People, Processes and Platforms

AnalyticalAgility

High Low

Dynamic/Ad Hoc

Configurable/Autonomous

Structured/Repeatable


Going into further detail on how to architect the BI and analytics platform, the Pace Layer Modeldifferentiates systems according to their integration level and analytical agility. It outlines threeapproaches to systems and information:

Systems of record: Usually the tightly integrated, sanctioned legacy application andinfrastructure systems in an organization, these typically support administrative and transactionprocessing activities such as finance, HR, asset management or procurement. In BI they largelycorrespond to standardized reports and dashboards developed and managed by IT.

Systems of differentiation: As applications that support processes unique to the organizationor its industry, systems of differentiation can be seen as more agile BI platforms. Products inthis category may come from traditional IT-modeled BI architectures with semantic layers, but


with extra capabilities. They are often offered by smaller independent vendors in the form ofdata discovery. The capabilities could be described as easier for end users, fostering self-service adoption, but also as capable of providing the sophisticated analysis for a broaderrange of users.

Systems of innovation: These are applications built to support new, innovative businessactivities and constructed quickly to enable enterprises to take advantage of these new ideasand opportunities. Systems of innovation represent the more fragmented and decoupledsolutions more tactical, agile and sometimes forward-looking BI applications from vendorsproviding domain-specific analytic applications and capabilities such as predictive modeling.

Most of the existing BI and analytics deployments run by IT are focused on the systems of recordreporting (such as a data warehouse, extraction, transformation and loading [ETL], and BI platformcombination). The systems of differentiation and innovation are usually disconnected and spreadacross the organization in line-of-business silos far from IT's control. Furthermore, there is littleability and no processes to leverage business-user-generated content as the basis of requirementsto enhance the systems of record.

Rearchitect the BI and Analytics Platform

BI leaders should follow the foundational tools described above to successfully rearchitect the BIand analytics platform. Gartner recommends the setup of a tiered architecture composed of:

Information portal

Analytics workbench

Data science laboratory

This is the representation of the tiered BI and analytics platform (see Figure 4) use it as a genericguideline that can be tuned according the organization's specific characteristics.


Figure 4. Tiered BI and Analytics Platform


To realize the vision of the three tiers (according to the Pace Layer Model) and be able to maximizetheir strengths, BI leaders need to deploy new technical capabilities to provide missing analyticstyles, improve the usage of existing tools through a better overall integration, and provide commonmetadata and governance.

Processes and people roles and responsibilities, although not detailed in this note, are of utmostimportance for success. They must be addressed in conjunction with the technical platform asdescribed in the Gartner Business Analytics Framework.

As depicted by the arrows in Figure 4, some of the key processes to address are:


The ability to promote content built by business users in the analytics workbench or datascience laboratory to the information portal

Tight cooperation between the information analysts and data scientists

It must also be noticed that the different analytic styles (from the Analytics Spectrum) tend to alignbetter with certain tiers, as represented in the top part of Figure 4 ("Typical analytic styles usage").However, they may be pervasive across the whole platform for example, descriptive dashboardsare more relevant in the information portal but may also be used in the analytics workbench.

The platform tiers must work in conjunction. Table 1 shows the characteristics they share that helpcreate a coherent global BI and analytics platform and, at the same time, the disparate strengthsthey have to support a diverse set of needs and audiences.

Table 1. Platform Tiers: Similarities and Differences

Similarities Differences

Belong to the same overall BI and analyticslandscape and work in conjunction towardcommon goals.

Share a base set of information. Support the execution of integrated analytic

processes.

Share BI and analytics content, metadataand processes.

Follow a single set of encompassinginformation governance rules.

Are supported by a common BI and analyticsteam and follow a single, integrated strategy.

Serve a different set of needs. Support different user audiences with

different skills, although the ultimateinformation consumer may be the same.

Add disparate data sources to each tier,according to user needs.

Use different technical capabilities andtools, eventually from different vendors.

Require different access levels toinformation.

Produce distinct outputs and impact thebusiness differently.


We'll expand on each tier to understand how to integrate and leverage them in conjunction.

Information Portal

Closely follows the characteristics of systems of record from the Pace Layer Model.

The information portal is the workspace where business users can quickly and easily find the keytrusted metrics with which the organization measures its performance. It is usually made ofreporting and dashboard capabilities that provide content to information consumers.

Its outputs are the result of a formal development process that includes a business userestablishing requirements and a technical specialist (typically from IT but increasingly from the


business) implementing them. This can take days or months depending on complexity andworkload. The information can be trusted and is used across the organization, but has low flexibilityand reduced associated interactivity capabilities.

Typical platform capabilities:

Reporting

Dashboards

Microsoft Office integration

Mobile BI

Embeddable analytics

Sample tools and vendors:

SAP BusinessObjects

IBM Cognos

Oracle BI

Microsoft Reporting Services

MicroStrategy

Information Builders WebFocus

Analytics Workbench

Closely follows the characteristics of systems of differentiation from the Pace Layer Model.

The analytics workbench is the workspace used to investigate trends in trusted metrics or to detectpatterns in other datasets from multiple sources that may turn into opportunities or risks. It isan agile tier to explore information and has access to a broad range of data sources, with limited tono support from technical experts. Toolsets should include a data discovery tool and a number ofother capabilities to help business users extract value from information autonomously.

In the Analytics Spectrum, the workbench is able to provide descriptive analytics but will usuallyfocus on diagnostic analytics. In some cases namely through the use of more analytics-focuseddata discovery tools it can extend to a basic level of predictive analytics and will gain datamodeling and more advanced analytic capabilities going forward.


Data discovery

Ad hoc reporting/querying


Geospatial and location intelligence

Embedded advanced analytics

OLAP

Business user data mashup and modeling

Collaboration

Data filtering and manipulation


Tableau Software

Qlik

Tibco Spotfire

SAS Visual Analytics

SAP Lumira

Oracle Endeca Information Discovery

MicroStrategy Visual Insight

Alteryx

Microsoft SQL Server Analysis Services and Power BI

Data Science Laboratory

Closely follows the characteristics of systems of innovation from the Pace Layer Model.

The data science laboratory is the workspace where advanced analytics takes place and is the idealincubator for big data initiatives. It is a flexible environment where experimentation with trial anderror is actually encouraged to generate impactful insights for the organization.

A broad set of technical capabilities is expected and often provided by specialized tools withminimal IT integration, meant to deliver agility and the ability to answer unforeseen questions. This iswhy IT tends to overlook this area in favor of investing in the information portal.

Users are skilled and experienced, often more than the technical experts in IT. Their toolsets includedata mining capabilities, forecasting and other complex statistical and analysis tools.


Advanced data access

Support for big data sources


Advanced descriptive analytics

Predictive analytics

Forecasting

Optimization

Simulation

Further advanced analytics

Although not BI capabilities, Hadoop and other NoSQL databases must also be referenced here


SAS Enterprise Miner

IBM SPSS

SAP InfiniteInsight

Revolution Analytics and R

RapidMiner

Knime

Alteryx

FICO

Dell StatSoft

Cloudera

Hortonworks

MapR and other Hadoop distributions

Understand the Characteristics of a Tiered BI and Analytics Platform

The following table summarizes the characteristics of the three tiers. BI leaders should try tounderstand the gaps in their current BI and analytics deployment and change their strategyaccordingly.


Table 2. Characteristics of a Tiered BI and Analytics Platform

Characteristic Information Portal Analytics Workbench Data Science Laboratory

Key Objective Deliver standardized andtrustable information to theorganization.

Provide information explorationcapabilities to a broad range ofbusiness users.

Allow the production ofadvanced analyticprocesses.

Audience Technical specialistsproduce the BI content;business users (decisionmakers) consume it.

Business information analystsproduce content for decisionmakers, as in the informationportal.

Data scientists produce thecontent for consumption bybusiness operations (such ascall center staff) throughembedded analytics inbusiness applications. Maybe consumed directly bycustomers (e.g., viawebsites).

Data Sources Structured information fromthe enterprise datawarehouse (EDW) ordomain-specific data mart.

Mostly structured informationfrom the EDW, domain-specificdata marts, user-generatedinformation (often in Excel),business applications, andexternal and open data. Startingto use unstructured datasources. Inputs from theinformation portal.

Structured and unstructuredinformation from all availableinternal and external datasources. Inputs from theanalytics workbench.

Trust vs. Agility Reliable and structuredinformation, but static andinflexible.

Interactive, agile andcustomizable according to userneeds, but may showdiscrepancies in user-generatedinformation.

Reliable, in-depth and fact-driven. Customizable toanswer or solve a singleissue and inflexiblethereafter.

Time to DeliverContent

Can take days to monthsfor development, but canbe consumed in seconds.

Minutes to hours. Days to months.

Level of SkillsRequired

Intermediate to advancedtechnical skills for contentdevelopment. No particularBI skills for informationconsumption.

Basic to advanced datamanipulation capabilities at abusiness-user level. No majortechnical or statistical skillsrequired.

Advanced technical andmathematical skills.

InformationAccess Required

Narrow access toinformation forconsumption, according touser role and profile.

Broad access to multiple datasources, according to areas ofresponsibility.

Very broad access toinformation, according toareas of responsibility.

IT SupportRequired

High contentdevelopment.

Medium data sourceavailability and contentvalidation.

Low data sourceavailability.


Characteristic Information Portal Analytics Workbench Data Science Laboratory

Typical AnalyticCapabilitiesProduced

Descriptive analytics andoutput of predictive andprescriptive analytics.

Descriptive, diagnostic andbasic components of predictiveanalytics (such as forecasting).

Advanced descriptive,diagnostic, predictive andprescriptive analytics.


Gartner Recommended ReadingSome documents may not be available as part of your current Gartner subscription.

"Gartner Business Analytics Framework"

"Extend Your Portfolio of Analytic Capabilities"

"Applying Gartner's Pace Layer Model to Business Analytics"


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IntroductionAnalysisAcknowledge Misperceptions, Evolve Beyond the Monolithic MindsetBuild Upon the Gartner Business Analytics Foundational ToolsFollow the Business Analytics FrameworkLeverage the Spectrum of Analytics CapabilitiesApply a Pace Layer Model Approach to the BI and Analytics Platform

Rearchitect the BI and Analytics PlatformInformation PortalAnalytics WorkbenchData Science Laboratory

Understand the Characteristics of a Tiered BI and Analytics Platform

Gartner Recommended ReadingList of TablesTable 1. Platform Tiers: Similarities and DifferencesTable 2. Characteristics of a Tiered BI and Analytics Platform

List of FiguresFigure 1. Gartner's Business Analytics FrameworkFigure 2. Analytics SpectrumFigure 3. Pace Layer ModelFigure 4. Tiered BI and Analytics Platform

How to Architect the Bi and 265003

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