Baseline’s Competencies, Skills, & Knowledge

Having the right mix of skill sets at the right time is as important to success as the implementation approach when it comes to planning, delivering, and managing the cornerstones of your enterprise information strategy. Baseline’s professional consultants are equipped with broad business knowledge and deep technical skills in each of the four areas – business analytics, data warehousing, data management, and data integration.

We leverage breadth and depth of experience and expertise to keep our consulting teams small. We believe this gives our clients a more personalized experience and enables us to transfer the knowledge that makes you self-sufficient after we leave.

Our Professional Consultants

Our consultants average 15 years experience working side-by-side with clients or in senior positions within an industry.

Our experience brings context to engagements – insight and know-how relevant to the interconnections of business processes, data, and technology.

Our insight enables us to ask the right questions, paint the big picture of where you need to go, and instill new skills and expertise among your staff.

Our highly specialized business, data management, and technical skills equip us to understand your challenges, model best practices, and execute sustainable solutions.

Baseline’s Capabilities and Consulting Skills

Whether your need is for a custom engagement or packaged service offering, Baseline has the skills and capabilities to deliver both advisory and implementation services.
 

» Business Analytics Skills & Capabilities

» BI Application Portfolio Development
» Business Requirements
» Stakeholder Interviewing
» Business Analysis
» Business Intelligence (BI) Tool Delivery

» Data Warehousing Skills & Capabilities

» Data Warehouse Planning
» Architecture Design
» Data Warehouse Development
» Systems Integration
» Production Support

» Data Management Skills & Capabilities

» Data Modeling
» Metadata Management
» Data Analysis
» Data Requirements
» Data Quality
» Business Rules and Process Definition
» Data Stewardship

» Data Integration Skills & Capabilities

» Extract, Transform, and Load (ETL)
» Customer Data Integration (CDI)
» Master Data Management (MDM)
» Enterprise Information Integration (EII)
» Data Profiling
» Error Detection and Correction

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Business Analytics: Capabilities and Skills

Capability – BI Application Portfolio Development

Portfolio development creates a long-range plan for the incremental and continuous deployment of business analytics applications. It includes a definition of business applications to be prioritized and is used to build out an integrated data source, delivering measurable business value in short increments.

A portfolio keeps data and infrastructure development scoped and focused on business value. It helps avoid over-investment in IT tools, infrastructure, and unusable data because its use and definition do not reflect business operations or because of a lack of business accountability for development outcomes.

Capability – Business Requirements

Business requirements are a core development component of both Business Analytics and Data Warehouse development. It ensures the engagement of business users and informs what BI capabilities should be deployed. Business requirements identify the business questions and actions requiring information to answer or execute, and the business tolerance thresholds for availability, accessibility and quality.

Business requirements form the foundation for Baseline’s data development and delivery methodology because they 1) articulate shared accountability for outcomes between IT and the business sponsor, 2) provide the business context needed to develop the data so that it aligns and scales with business usage, and 3) support measuring business value of the information asset.

Capability – Stakeholder Interviewing

Stakeholder interviewing is a formal and structured procedure for securing information from stakeholders about information needs. Stakeholder interviewing also identifies current information gathering and data management practices, as well as opportunities to capture economies of scale, improve productivity, reduce operating costs, and improve the customer experience.

Stakeholder interviewing is the foundation for establishing business requirements across all Baseline services.

Capability – Business Analysis

Baseline provides business analysis skills and experience that are both industry specific and application specific. Baseline understands the business processes, job functions, data needs, and supporting technologies for a variety of business analysis capabilities. These include customer relationship management (CRM), sales force automation (SFA), supplier relationship management (SRM), business performance management (BPM), and compliance. Our skills range from database marketing, to building analytic models for customer segmentation and propensity analysis, to market basket analysis.

Capability – Business Intelligence (BI) Tool Delivery

BI Tool Delivery comprises the configuration, installation and deployment of off-the-shelf BI applications and tools. It is an alternative to custom development, and is generally recommended by Baseline as a best practice.

Required skills, knowledge and experience include:

• Site survey techniques
• Experience implementing leading market BI tools (e.g., Cognos,
• Business Objects, Crystal Reports) and the aptitude to quickly learn about configuring and installing new BI products
• Configuration techniques
• Application integration techniques
• Unit, system and acceptance testing methods
• Technical writing skills

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Data Warehousing: Capabilities and Skills

Capability – Data Warehouse Planning

Data warehouses require careful, rigorous, requirements-driven planning. This not only includes intimacy with the data warehouse development process, but an ability to engage people on both the business and IT sides with the appropriate skills and accountability as stakeholders.

Capability – Architecture Design

Data warehouse architecture design relies on a number of factors, chief among them, a fundamental knowledge of IT architecture conventions and structures. Understanding the features of an incumbent IT architecture informs the adoption, design, and installation decisions and processes inevitable in data warehouse acquisition.

Required skills, knowledge and experience include:

• Data migration and movement solutions
• Architectural standards (e.g., Service Oriented Architecture)
• Ability to translate IT architecture blueprints
• Understanding of end-user facing solutions (portals, dashboards, etc.)
• Intimate knowledge of data warehouse development methodology (requirements, design, ETL, etc.)

Capability – Data Warehouse Development

Data warehouse development is actually comprised of a range of skill sets and expertise across the organization. For instance, an ETL programmer is likely to be a specialist who knows a variety of tools and vendor solutions, but may be less familiar with data requirements gathering concepts. From initial business requirements through data acquisition and cleansing to report deployment, specialized data warehouse development skills continue to be in demand.

Capability – Systems Integration

Many Baseline clients need help acquiring one or more data warehouse enabling technologies and integrating them into their existing IT infrastructures. Baseline consultants provide an objective assessment of functions and features, as well as accurate estimates of adoption timeframes and projected workloads.

Capability – Production Support

Following the implementation of a data warehouse, Baseline works with clients to ensure they have the necessary skills and processes to be self-sufficient in a production environment.

Required skills, knowledge, and experience include:

• Database administration (DBA)
• Performance tuning
• Operational management processes
• Capacity planning and forecasting techniques

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Data Management

Capability – Data Modeling

Data modeling comprises logical modeling of the business by mutually-exclusive data subject areas before generating the physical table structures. A conceptual data model may precede the creation of the logical data model (LDM). The conceptual data model represents the highest layer of subject areas (e.g., customer, product, employee) and is used to help business stakeholders grasp the concept and purpose of a logical data model.

Second only to good business requirements, the logical data model is central to building and managing a comprehensive, scalable and completely integrated “single source of the truth.” It is the link between how the business operates and the physical storage of enterprise data.

Required knowledge, skills and experience include:

• Business and data definition
• Entity relationship identification and schema design
• Entity definition (i.e., attributes, keys, etc.)
• Data normalization, de-normalization techniques
• Methods of assessing and ensuring referential integrity
• Competence in commercially-available data modeling tools such as Visio, ERwin and ERStudio
• Competence with at least one data modeling convention such as IDEF or UML
• Model validation and sign-off

Capability – Metadata Management

Metadata management is the organization and ongoing maintenance and support of information that describes the meaning of enterprise data and delivers the processes and technologies to create and maintain it.

A commitment to developing and managing metadata is the key to sustaining the quality of integrated data and deriving optimal business value from it over time. Without such commitment, Baseline knows that a customer’s problems with data are likely to recur no matter how extensive and robust their investment in hardware, software, and establishing a “single version of the truth.”

Capability – Data Analysis

Data analysis is the investigation of data for the purpose of revealing its properties and quality in reference to meeting a business need. The quality of the data analysis activity is a critical success factor in establishing the system of record and which elements will be used to satisfy the data requirements and, ultimately, the business requirements.

Capability – Data Requirements

Data requirements are derived from business requirements. The requirements gathering process identifies the data elements required to answer business questions and to enable business outcomes or activities as identified by business stakeholders. Depending on business need, data requirements include the elements, potential sources, periodicity, latency, history, accuracy and other relevant aspects of quality.

Data requirements define the scope of data development activities for a data integration, warehouse or application implementation engagement.

Capability – Data Quality

Data quality is defined by the business as part of business requirements, and reflects thresholds of acceptance for data characteristics including accuracy, completeness, relevance and timeliness. The higher the quality requirements, the more expensive are data development and maintenance; hence, the need for the business to establish the threshold of acceptable quality for each application.

One of the most important challenges Baseline consultants face is helping the customer understand that quality is a continuum, not a static end state. More importantly, the cost of quality should be factored into the business value of every data-enabled solution. In a way, getting the investment to match value over time is one of the most important missions of a data management function.

Required skills, knowledge and experience include:

• Requirements development (particularly interviewing and facilitation)
• Data profiling and analysis methods and tools
• Performance benchmarking and monitoring
• Root cause analysis methods
• Error correction and audit techniques

Capability – Business Rules and Process Definition

Business rules are initially defined and documented as part of business requirements definition and institutionalized as part of implementation projects. However, ongoing responsibility for business rules are data management functions that tie into metadata management activities. These functions include assuring accountability for business rules, establishing common procedures and tools that allow the business to update and maintain business rules they “own,” and auditing compliance with those procedures

Business rules are a point of accountability where IT has traditionally had to accept responsibility because rules were embedded in application code. Baseline believes that accountability needs to rest with the business folks who define and understand the business rules. Establishing business accountability requires an end user-friendly interface, procedures that protect the integrity of shared data, and a way for the data management function to monitor compliance.

Capability – Data Stewardship

Data stewardship is a data management role that formally assigns responsibility for protecting, correcting, extending, and facilitating the appropriate use of data for which the steward has expert knowledge. Business data stewards tend to be subject matter experts and source data stewards tend to be source system experts.

Because data stewardship is one of the most fundamental data management functions and can have such a positive effect on the perceived usability and accuracy of the shared data asset, Baseline encourages customers to begin incremental development of data management with the role.

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Data Integration

Capability – Extract, Transform, and Load (ETL)

ETL entails the design, writing, testing, and deployment of code that automates the selection, cleansing, reformatting, application of relevant business rules, and delivery of data from the source to the target. Essentially, an ETL process:

• Extracts data from the source system.
• Transforms to cleanse, and possibly change, the data to meet a defined business need.
• Loads the transformed data into a data warehouse, mart or other repository where users and applications can access it.

ETL is the heart of data integration—what goes into the ETL process is disparate data and what comes out is normalized for use with other data from other sources that have also been normalized.

Required skills, knowledge and experience include:

• Data mapping
• Competence with, and the ability to quickly learn new, commercially-available ETL tools from vendors such as IBM/Ascential, Informatica, and Microsoft
• Ability to code in C, C++, and develop scripts in languages like VB and Perl
• Data verification techniques
• Test case design and execution
• Documentation

Capability – Customer Data Integration (CDI)

CDI is the set of processes, controls, automation, and skills necessary to standardize and integrate customer data originating from different source systems. CDI reconciles data from across silos in a sustained and dynamic way, making enterprise data available to the systems and users that need it. The approach can be applied to master data from across the enterprise to deliver a unified view of customers, products, suppliers, or locations.

Integration of a company’s master data often involves hub technologies that can access, cleanse, standardize, match, integrate, and propagate master data. Knowledge of master data management, the vendor options, and the architectures that support them are key to enabling CDI.

Capability – Master Data Management (MDM)

Master Data Management is the set of disciplines and methods to ensure the currency, meaning, and quality of a company’s reference data that is shared across various systems and organizations. MDM applies to the data management functions and activities Baseline has always recommended as best practices for business analytics, data warehousing, and data integration. Yet MDM transcends them by applying specific technology and architectural solutions to automating the integration of key master data.

Capability – Enterprise Information Integration (EII)

Enterprise Information Integration (EII) is an integration technology that enables applications to access data directly from disparate sources via a standard interface. EII is typically used to abstract the data and XML is commonly used to transport the data between source and target.

Just one of many technical options for integrating data, Baseline is more likely to use EII in an application development engagement since the data only need to be integrated for that one purpose rather than leveraged across many current and future applications. A typical example is using EII as a common portal interface for presenting data to end users via the Web.

Capability – Data Profiling

Data profiling is a data quality activity that examines metadata to evaluate and summarize the structure and consistency of database content. Typical output is a statistical description of the database structure and content, including data domain, type, format, and value ranges as well as frequency and redundant record counts. The process helps a data analyst understand dependencies and risks in selecting the “system of record,” and what ETL or CDI solutions need to “fix.”

Data profiling is a standard feature of all Baseline integration, development, and data management engagements.

Capability – Error Detection and Correction

Error monitoring, trapping and correction represent a process designed to sustain the quality of integrated data once it moves into a production environment. Some of the activity is automated; for example, data validation routines built into applications and ETL, and routine data profiling audits. Some of the activity requires human intervention; for example, the disposition of data rejected by ETL and data validation routines. Accountability for identifying and correcting the root problem in the source is clearly defined.

All of Baseline’s assessment and development activities include a closed loop error correction component. Without it, the client puts the investment in data integration and the credibility of the warehouse or application at risk.

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