BI_StrategyDM2

Analytical Data Repository and
Business Intelligence
Strategic Architecture and
Organizational Framework
For Delivering BI Excellence
Prepared by Dan McDonald
214.995.####

Analytical Data Repository & Business Intelligence 2
INTRODUCTION
This summary is an extrapolation of my understanding of Vertex’s Analytical Data Repository and Business
Intelligence needs as explained to me during the interview process. Most of the analysis and recommenda-
tions referenced in this document are based on interview conversations and a culmination of all of my experi-
ence in Business Intelligence and Analytical Data Warehousing.
My Goals are to provide Architecture Strategy to enable Vertex to identify client opportunities and facilitate an
organizational structure that supports this strategy.
As a background, the past two decades have produced significant technology advances. Back office and front
office software and systems have created an environment in which information worker’s daily activities are
more than 40% reliant on electronic information processing in most companies (study by the US dept of la-
bor). The ease at which one can create and manage this information in an operational world has established
a level of productivity beyond any time prior to the information age. As software and systems become more
sophisticated, and use thereof proliferates, we find ourselves drowning in a sea of information.
Most companies are beyond the automation and are now challenged with how the sea of information may be
used to build a smarter business and how they can utilize this data to create new service offerings.
Background
Vertex’s Analytical Data Repository and BI Program Strategy should be a product of a collaborative effort be-
tween the Information Technology group and Vertex business units.
This strategy is based on my understanding of the current environment and the desire to extract value from
the information silos within Vertex to help develop additional products and services.
BUSINESS DRIVERS
Strategic initiatives require top down support and enterprise-wide commitment, and this strategy is no differ-
ent. A technology strategy, as this is, must encourage the input of both IT and Business leaders and must be
executed with the cooperation of the entire IT and relative Business areas of the organization. Input should
be evaluated objectively to ensure the resulting strategic initiatives support business objectives through tech-
nology. This Strategy is based on this general philosophy.
Technology strategies, in general, have experienced evolutionary changes in approach due to rapidly chang-
ing business needs and technologies that support those needs. In the 70’s, 80’s, and 90’s technology strate-
gies evolved from long running strategic initiatives to twenty first century “adaptive” technology strategies.
The overarching concept is derived from the fact that Technology and Business move too rapidly for long
term planning and implementation initiatives to remain valid.
Key Characteristics
In order to ensure that the Analytical Data Repository and BI Program supports the concept of adaptation;
The Strategy is designed with the following characteristics:
 Adaptive (Key and most important quality) – The Strategy must be designed to embrace change.
Therefore, the underlying approach should follow short cycles of define-design-deliver-review. With
this approach, Vertex can progress with conviction, identify problems early, adapt the approach if it
becomes necessary, and thus ensure success.
 Progressive – Time does not stand still. As the Analytical Data Repository and BI Program progress,
Business initiatives must progress to meet short term objectives. Key Business initiatives need to be

ANALYTICAL DATA REPOSITORY & BUSINESS INTELLIGENCE
identified and this Strategy will strive to help move those initiatives forward. This approach requires ex-
tensive coordination in both planning and execution between Business and IT and should become an
active part of a project portfolio.
 Realistic – It is important that we keep the Strategy implementation in perspective. There is always
high risk in change and the Strategy should not be construed as recommending rapid, wholesale
change. The change should occur incrementally over time with a focus on achieving the overall vision.
Key Principles
Any sound Strategy requires a set of guiding standards or principles that serve to keep the Strategy in check
with the vision clearly in sight. A principle may be visualized as an imaginary lens that provides clarity to a
strategic solution or approach. Without principle, a solution cannot be validated and is therefore unfounded.
The following principles, referred to throughout this document as Key Principles will be used to validate all
proposals and recommendations contained in this document.
The solution must…
 Contribute to the success of corporate goals
 Support and adopt corporate policies and standards
 Adapt to change
 Mature and improve over time
The Strategy sets a baseline in concepts and thinking representing sound fundamentals with built for adapta-
bility.
DISCOVERY AND CURRENT STATE (AS-IS)
In order to achieve any objective it is imperative that you know where you starting from. Typically, I would
conduct discovery sessions involving both business and IT entities to identify the As-is state. Since this doc-
ument is an exercise for the interviewing process I will base my Discovery and Current State on my experi-
ence with other organizations and information obtained during the interviewing process. That being said,
what I have typically find in organizations with silos of information are the following:
 Users spend an inordinate amount of time gathering data.
 Typically few employees understand the data. Those employees are well protected by their managers
because the managers know how important they are to their organization.
 Some of the source of the information is too complicated so business units have to engage IT
 While we have similar systems, each client has made modifications to the source data rendering uniformi-
ty in data collection virtually impossible.
 I have little access to historical information so I have to store the information on my PC
 I would like to analyze the data more frequently but can’t because it takes to long to complete the analy-
sis
 We have to continually correct data problems because we don’t have time to perform the necessary data
profiling or quality analysis.

These statements boil down business challenges in 4 different areas represented in the figure to the right.
1. Data acquisition
a. Highly manual, inconsistent across the organization and error prone
b. Business users do not have direct access, nor do they understand the structure of data and
that forces IT into the infor-
mation value chain which ul-
timately increases the acqui-
sition time
2. Analysis information sources
a. The primary source of analy-
sis tends to reside in desktop
applications such as MS Ac-
cess or Excel files which pro-
duces inconsistent, and in-
complete analysis that, in
some cases, lack good histor-
ical information
b. IT resources or data analysts
use tools such as SQL and
SAS to extract the infor-
mation into flat files
3. Analysis tools
a. Because of its easy of use,
the most commonly used
analysis tool is probably Ex-
cel.
b. BI tools such as Cognos and
Business Objects exist how-
ever there are probably no
central or organizational gov-
erning policies in place to es-
tablish control or usage
standards
4. Business users
a. Currently spend their time providing extracts and gathering data to analyze what has already
happened as opposed to trying to look for trends or emerging market opportunities.
FUTURE STATE (TO-BE)
The primary objective of the future state is to remove the constraints imposed over time by the systems we
rely on for our success. This section explains how I would try to accomplish this and the rationale for doing
so.
1. Information acquisition
a. People: Establish an information integration discipline responsible for consistency in the data
acquisition process. The discipline is responsible for ensuring data profiling, quality analysis,
quality monitoring and auditing, transforming and cleansing, harmonization, and active col-
laboration with the corporate data quality team.
b. Process: Establish a delivery life cycle to ensure consistent and repeatable processes for re-
quirements, design, development, and deployment. Create a standard SLA that applies to
every project as part of requirements so proper expectations are set and use the SLA as a

form of measure. Individuals playing a role in this discipline must be educated on best prac-
tices for the discipline and applying the practices to a business solution.
c. Technology: Establish a standard, preferred technology solution framework that can be lev-
eraged across business units and departments in order to facilitate sharing of hardware and
software resources. Require special approval before deviating from the standard. Ensure the
chosen technology possesses all the characteristics needed to ensure data quality.
2. Analysis information sources
a. People: Establish an Analysis Information Repository discipline and provide competent re-
sources skilled in business information architecture, data architecture, and analysis data
modeling. This discipline is responsible for modeling business information according to best
practices and in a way that enables business users to analyze information from different per-
spectives.
quirements, design, development, and deployment. Ensure this process coordinates with up-
per and lower levels of the architecture (Analysis tools and Information acquisition). Individ-
uals playing a role in this discipline must be educated on best practices for the discipline and
applying the practices to a business solution.
c. Technology: Establish a single analytics database technology standard capable of housing da-
ta from across business units while providing logical isolation for security and regulatory pur-
poses. The solution must be capable of sharing hardware and software resources across
business units and departments, be scalable, and offer a single source for business analysis
information to prevent tedious manual searches.
3. Analysis tools
a. People: Establish an Analysis (BI) Tools discipline with the ability to apply the chosen tech-
nology to the analytic needs of the business. This discipline is responsible for performing
business analysis, understanding business metrics, and applying the technology for an adapt-
able solution. Individuals playing a role in this discipline must be educated on best practices
for the discipline and applying the practices to a business solution.
quirements, design, development, and deployment. Ensure this process coordinates with
lower levels of the architectures and helps meet business goals.
c. Technology: Establish common framework with a preferred technology for business analysis
and advanced modeling. The analysis tools are intended to use the Analysis information
source (above) for a complete business analysis solution that empowers the users.

Figure E1b – The future state should remove the inefficiencies and inadequacies in the current environment
by providing a single analytics framework that provides a one-stop-shop for analytics information.
STRATEGY
Vertex captures vast quantities of data in production systems and provides customer defined extracts. The
goal would be to maximize the value of the data we collect by analyzing the uses of the data and create sus-
tainable and uniform processes to turn our data into vital Subject Matter Areas of Valuable Business infor-
mation. Organizing our data into re-usable information assets that allow our customers a better understand-
ing of their businesses will provide another product in our service catalog that would distinguish us from our
competitors. This directly affects the ability to provide timely insight into the risks, performance, and poten-
tial growth for our customers. Ultimately, the Strategy is to move Vertex from a data constrained organiza-
tion to an Information Enabled organization.
The best approach to information enablement is to first understand where you sit in the maturity model. The
maturity model that addresses both business and technology maturity combines concepts from the Gartner BI
maturity model as well as the well respected Capability Maturity Model Integration (CMMI) originally estab-
lished by the Software Engineering Institute. The CMMI model is widely accepted standard for measuring ca-
pabilities in software development. Based on our conversations I am assuming Vertex is currently at the low-
est level of maturity where the business is reactionary and information is primarily inaccessible.
Table E1b, below, provides further insight into the business intelligence maturity characteristics for each lev-
el. The maturity model is useful for determining business insight capabilities (row 2) based on the peo-
ple/process/technology capability qualifications (row 3).

Figure E1c – The Strategy will transition you from its current state of a data constrained organization (red) to
an Information Enabled organization (green)
Table E1b – Business Intelligence Maturity Model (business model derived from Gartner and technical model
derived from Software Engineering Institute’s Capability Maturity Model Integration [CMMI]).
Reacting Aware Enabling Managing Performing Optimizing
Little to no ability
to analyze data
Ability to measure
historical facts
Ability to analyze
why things happen
Ability to analyze
what is happening
Ability to predict and
forecast
Ability to influence
what will happen
 No identified
vision
 No BI strategy
 Limited use of
reporting tools
 Incapable of
historical analy-
sis
 Value of formal
metrics not un-
derstood
 Some invest-
ments in BI
 Data for tactical
decisions
 Tools, applica-
tions in silos
 No true sponsor
or team
 Data incon-
sistency
 Investments
focused on spe-
cific projects
 Limited metrics
formally defined
 No central busi-
ness model
 No formal link to
enterprise objec-
tives
 Limited data
integration
 Defined business
strategy and ex-
ecutive sponsor
 Information avail-
able across de-
partments
 Framework of
metrics links appli-
cations
 Modeled business
information con-
solidating
 Data integration
formalized
 Quality auditing
and monitoring
 Metrics and busi-
ness model de-
fined and managed
 Consistency across
departments
 Information con-
solidated
 Governance and
quality policies de-
fined and moni-
tored
 Data trusted for
strategic change
 Problems fixed at
source
 Pervasive across
business; part of the
culture
 Users at many levels
 Scope includes sup-
pliers, partners, and
customers
 Integrated into busi-
ness processes
 Agility built into sys-
tem
 Information a strate-
gic asset
 Proactive, dynamic
BICC

To facilitate incremental value, I would recommend implementing a standard business intelligence framework
consisting of standards, people/skills, processes, and technologies and will be referred to it as the BI Founda-
tion (“Foundation”). The BI Foundation is the future-state platform that will support current and future busi-
ness analysis needs. The Foundation will also support more mature, strategic analytic capabilities necessary
to proactively manage the business and continue to reach corporate objectives.
The Foundation will provide a standard platform with the ability to scale as organizational adoption expands
(see Figure E1c, below). Short term value is obtained by addressing current business needs. The future-state
will incrementally continue to deploy more advanced analytic capabilities without requiring platform modifica-
tions. Ultimately the Foundation will provide a means to evolve from a data constrained organization to an
Information Enabled organization.
Figure E1c – The Business Intelligence Foundation combines people process and technology to evolve a data
constrained organization to a mature, information enabled organization
It is widely accepted that successful strategies and strong leadership go hand-in-hand. Analytical Data Re-
pository and Business Intelligence (BI) Strategy, to be successful and maximize the BI investment, must also
have strong leadership which is typically facilitated through
BI Governance.
BI Governance is a topic that cuts across all layers of the
BI strategy. It is considered a “soft” subject in that it deals
with many intangible and long-range subjects, but it is an
indispensable part of a successful BI program. Governance
is defined as a method or system of government or man-
agement; an exercise of authority or control. To BI, gov-
ernance can be defined as established management and
control over the use and proliferation of the BI technology.
BI Governance is best implemented through a BI Compe-
tency Center (BICC), a governing body with ties to execu-
tive oversight, business leadership, IT leadership, and oth-
er governing bodies. Its functions are strategic, tactical,
and operational. In other words, the BICC supports the BI

Program end-to-end. It is responsible for the successful execution of the BI Strategy and is accountable for
overall effectiveness of the BI platform.
The Organization of Business Intelligence study by BARC Institute in 2008 determined that companies with
dedicated BICCs outperformed those that didn’t. Furthermore, the study revealed that corporate alignment
and user adoption were more successful. Ultimately the BICC is imperative to achieving a maximum return on
the BI investment so it is the focal point of the BI Governance Strategy.
BI Governance Key Concepts
 Governance… Governance is the set of decisions and accountabilities that encourage the right behav-
ior in the use of information technology. Simply put, governance is the forum you create to resolve
ongoing Analytical Data Repository and Business Intelligence decisions. Effective governance is de-
signed in advance by anticipating the types of decisions you will need to make, and ensuring that the
right people and processes are in place to make them quickly and effectively. Governance describes
the assignments made to resources to make and carry out those decisions and to be accountable for
their results.
 Enablement… Many of the governance mechanisms outlined here are primarily intended as a means
to support and enable various individuals and teams to quickly make the most of Analytical Data Re-
pository and Business Intelligence technologies. This is mostly achieved through mentoring and train-
ing, but also in capturing internal best practices and documenting standards that work. Keep in mind
that in order to make the most of technical enablement, Analytical Data Repository/Business Intelli-
gence sponsors must provide clear and timely business direction so that the foundation and strategy
are correct in the first place.
 Accountability... Like many of the initiatives and recommendations set forth in this strategy, estab-
lishing Governance will require time and energy to develop and operate. To make this investment
worthwhile, accountability must be clearly defined for each role and function in the form of perfor-
mance goals and service levels.
 Coordination… In some cases, some businesses have historically operated as a fairly loose and “si-
loed” federation of business units, subsidiaries, and departments. The Governance models include
some degree of centralized authority, but at this point favor more coordination of efforts and decision-
making. Actively engaging the business units and leveraging their project management techniques is a
requirement for putting this concept to work.
 Temporary… While these models are focused on a narrow segment of the entire environment, they
are necessary in order to face the special risks and the strategic options that new technologies bring to
the business. Analytical Data Repository and Business Intelligence implementation will mature and
evolve (as many other organizations’ implementations do) over an 18 to 24 month period. The roles
and functions outlined in the models will require a much larger effort up front to establish the basic
foundation but should trail off as Analytical Data Repository environments and practices stabilize
through evolution.
 Multiple levels… A working governance model ensures smooth integration of IT and business by
working at three essential levels. The strategic level links the goals of the business with a technical
strategy and architecture for all Analytical Data Repository implementations, and encompasses a pro-
gram of many related projects to implement. The tactical level combines individual Analytical Data
Repository and Business Intelligence owners with a focused technical team, and encompasses a single
implementation project. The operational level works at the day-to-day processes of executing and
administering the entire Analytical Data Repository and Business Intelligence environment.

BI GOVERNANCE FUTURE STATE
Companies that implement dedicated BICCs to govern a BI Shared Services model are proven to have more
successful BI programs. The BI Governance Strategy will implement a best-in-class BI Governance model that
incorporates a BICC. The BICC will grow and mature with the BI program and will be accountable for the suc-
cessful execution of the BI Strategy.
This section identifies a future state that aligns with the BI Governance Vision. There are two sections that
help define the future state:
1. The conceptual BICC Governance Model – a model that identifies high level, best practice concepts
and characteristics that will serve as boundaries to guides implementation. Model characteristics are
available below the diagram.
2. The target maturity model - The minimum recommended maturity to achieve the future state con-
ceptual model and necessary to ensure overall BI Program success.
Figure GOV-1a: Target BICC Conceptual Model
Table GOV-1c: Governance model description

Number Description
1 Governance Committee – consists of a diverse group of executive sponsors (CxOs and SVPs), IT
managers, and line-of-business managers. This committee sets strategic objectives, provide
common definitions, rules, and standards – these are then applied to data structures, access,
and use across the entire enterprise.
2 Enterprise Architecture Solutions Board (EASB) - The purpose of the EASB is to bring together
resources and people to deliver valued products and services that accelerate delivery of new or
changed business capabilities. Goals are attained by establishing and complying with standards
pertaining to technologies, products, solutions and process.
3 BICC Director – interacts with other governing bodies to ensure strategic compliance for the
BICC. They set the overall vision in conjunction with the executive and line of business champi-
ons.
4 BI Independent Delivery Organization (BI IDO) – a dedicated BI project delivery team responsi-
ble for tactical project delivery and timely response to business project needs. This is a critical
component of BI success and overall customer satisfaction.
5 Core Team – various technical and domain expert role that support the domain stewards with
metric definitions and their ongoing analytic requirements (constructing new capabilities or ex-
ploiting existing analytic assets). The Core team will define policies and standards for business
intelligence, data warehousing/integration/quality where they relate to the BI platform.
6 Domain BI Stewards – line-of-business representatives educated on BI meta-data standards and
policies but also have intimated knowledge of the business and metrics. These stewards provide
active participation and business influence during the early stages of BI adoption, provide pro-
ject level oversight to ensure BI solutions are in the best interest of the business, and provide
day-to-day governance oversight at the business level to ensure that users do not misuse the BI
tools ability to create dynamic filters and measures in a way that results in erroneous analyses.
7 Enterprise Extended Teams – enterprise level entities that govern their own IT domains and re-
lated policies, etc. that must influence strategic, tactical, and operational decisions made within
the BICC.

BI Governance as defined above in the static model will operate in conformance with best practices as de-
picted below in figure GOV-1b.
Figure GOV-1b: Target BICC high level process model

ANALYTIC DATA REPOSITORY
INTRODUCTION
The Analytic Data Repository is the data store that will directly support the BI and analytic requirements.
This repository will contain conformed dimensions and facts organized in a user-friendly Star Schema to pro-
mote simplicity and efficient query performance. In addition, it will contain various data structures that will
provide for ease of use when performing data modeling and statistical analysis (the repository will leverage
all database-specific performance optimizations).
ANALYTIC DATA REPOSITORY VISION
The vision is to create a single data warehouse solution framework, the Analytic Information Store, using the
enterprise standard database and data warehouse technology to serve as the primary repository supporting
the Business Intelligence Foundation. The repository will share hardware and software resources yet provide
logical isolation for each business unit. Standard policies, skills, and processes will facilitate mature efficient
implementation as well as quality solutions.
Figure AIS-1a – The Analytic Data Repository Vision
The Analytic Information Integration Service indicated in the above diagram is the core data processing infra-
structure necessary to efficiently cleanse and transform both master and raw transaction data. This platform
facilitates automated data process and helps bring consistency to data quality. Data Quality Reporting metrics

are captured in the same way core metrics are – promoting this valuable metadata to core analytic subject
areas (e.g. data quality confidence indexes alongside KPIs or their driving measures).
ANALYTIC INFORMATION INTEGRATION VISION
The Analytics Information Integration (AII) Platform will decrease the amount of time it takes for business to
make fact-based decisions by eliminating or greatly decreasing the manual time and effort pertaining to data
acquisition and manipulation. The AII Platform will work with the BI and Analytic Warehouse platforms to
improve business confidence in the quality of data by removing risky manual processes and providing the
means to audit for data quality and trace quality problems.
The vision is to create a single solution platform consisting of hardware, software, and governance (peo-
ple/process) to standardize and consolidate the method of analytic data acquisition into a consistent, repeat-
able solution that can be leveraged across the enterprise.
Figure AII-1a – The Analytic Information Integration Vision

ORGANIZATIONAL STRUCTURE
Implementing the recommendations within this document will require the development of critical teams to
lead and develop the projects identified by the BICC. Such teams will typically make up an organization simi-
lar to the following:
Technology Sponsor
Lead ArchitectProject Manager
Lead Infrastructure
DBA – Prod Only
System
Administrator
Lead Developer
Sr. ETL Developer
ETL Developer
Solution Architect
Data Analyst
Quality Analyst
Business Sponsor
Business Owner
Business Analyst
PMO
BI App Support
Lead
Sr. BI Developer
BI Developer
DBA
Dev/QA onlyBusiness Analyst
Security
Administrator
Business SME
BI Steward
Note, that synergistic affects can be obtained over time however, the ultimate size of this organization will be
predicate by the number of BI projects expected to be completed in a given time frame.

ROLES AND RESPONSIBILITIES
The BICC is responsible for project sponsorship, key decision-making, final approval on project direc-
tion/deliverables, and Business-specific subject matter expertise.
The following outline the roles of participants within this organization:
 Sponsor – Responsible for the overall engagement. The Executive Sponsor is responsible for setting
and approving the direction of the program, ensuring that the initiative is given the appropriate pri-
ority and attention across the all parts of the organization that the program touches. The executive
Sponsor is ultimately responsible for the engagements success.
 Project Manager – Responsible for project oversight and approval from a budget, staffing, and
schedule perspective. This person will serve as the primary point of contact and is responsible for
driving decisions, activities, and staff, as well as communicating project status and escalating issues
upward and outward within the organization.
 Solution Architect – Responsible for determining the project functional requirements and working
with the technical architect to determine the optimum solution design and will lead the Elaboration
phase of the project. The solution architect answers the questions “What does the system do? How
does the system interact with users?” The Solution Architect ensures that the project meets the busi-
ness goals identified by the customer.
 BI Steward – Respected business representatives with a firm grasp on the analytical needs of the
business and an understanding of Business Intelligence and how it applies to the needs. The BI
Steward will work with the Business Analyst, the Solution Architect, and Architectural Oversight per-
sonnel to ensure the business solution meets business needs both short and long term. Furthermore,
the BI Steward plays an operational role of providing oversight to run-time, production metrics and
meta-data management to ensure users adhere to best practices and go through proper quality pro-
cedures to prevent errant analyses.
 Business Subject Matter Experts – Responsible for describing and defining the current business
objectives, processes and system. SME’s will be responsible for execution of user acceptance testing.
 BI Architect – Responsible for determining the technical requirements and architecture for the en-
gagement, including Data Integration & Warehousing, governance, and Business Intelligence tech-
nical direction and implementation. This is a senior-level resource that ensures the technical envi-
ronment and tools are in accordance with best practices and the right fit for the environment at
hand.
 Business Analyst – Works with the Solution Architect to analyze business requirements, interview
business users and document all functional and non-functional requirements, use cases, traceability
matrix and features matrix that will be used by the Architects to design various components of the BI
solution. The Business Analysts also interacts with developers and QA during construction and test-
ing phase.
 BI Developer(s) – Responsible for construction of BI reporting and analytics content, including the
meta-models as well as customer facing reports and prompts. This resource will be involved
throughout the project, with a primary focus on gaining adequate experience and knowledge transfer
to continue the program once the release has been delivered.
 ETL Developer(s) – Responsible for building data interface and ETL code to enable data integration
from the source systems and data stores into the BI reporting repository (data warehouse). This re-
source will be involved throughout the project, with a primary focus on gaining adequate experience
and knowledge transfer to continue the program once the release has been delivered.

 Sr. ETL Developer – Responsible for building data interface and ETL code to enable data integration
from the source systems and data stores into the BI reporting repository (data warehouse).
 Sr. BI Developer – Responsible for construction of Business Intelligence meta models, processes,
code, cubes and reports.
 QA Analyst – Responsible for developing the strategic QA plan, test cases/scripts and test data to
enable functional, technical, performance and process testing. The QA Analyst is also responsible for
implementation/execution of QA plans and coordinating the testing process, including UAT coordina-
tion.
 System Administrator – Responsible for installation and configuration of application software re-
quired by the solution. Also responsible for providing server-side support and ability to address any
development related issues that may arise.
 Database Administrator (DBA) – Responsible for installation, configuration, and ongoing admin-
istration and management of the DBMS and database schemas used throughout the course of the
project. The DBA will set database permissions, created indexes, perform table loading, etc.
Having the proper resources available and engaged at the appropriate times are key to the overall success of
any project and ongoing program.

BI_StrategyDM2

Recomendados

Recomendados

Más contenido relacionado

La actualidad más candente

La actualidad más candente (20)

Destacado

Destacado (17)

Similar a BI_StrategyDM2

Similar a BI_StrategyDM2 (20)

BI_StrategyDM2