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Data management scope

Data Architecture

We are specialists in data modelling and database design. We can also assist you with selecting the most appropriate database and ETL (extraction, transformation and loading) tools for your organisation.

Our strength lies in our ability to create flexible and adaptable data structures that:

• address current business needs
• allow for future change
• ensure efficient and appropriate distribution of data throughout your organisation.

We develop databases incrementally to meet business needs quickly. We can then refactor the database design to accommodate changing business requirements. This involves making small changes to your database schemas that improve the design while preserving the behavioural and informational semantics.

Metadata Management

Data Agility implements metadata repositories that integrate with your organisation’s disparate systems using your existing infrastructure. The outcomes for you are:

• easy access to business knowledge—clearly defined enterprise-wide data
• the ability to easily identify the impacts of system change.

This delivers:

• significant cost-savings to your organisation—the risk of overlooking system impacts during the decision-making process is removed
• governance over system change—a standardised approach to system change can be adopted, even in the most complex of enterprises.

Effective metadata management supports your decision-making, taking the guesswork out of business and system change.

Data Quality

The Importance of Data Quality

Information is the result of processing, manipulating and organizing data in a way that adds to the knowledge of the person receiving it – poor data quality means poor information. As data increasingly becomes the organisation’s most valuable asset, so data quality is becoming one of the highest priorities.

The Data Warehousing Institute estimates that data quality problems in the US alone cost businesses billions of dollar each year, due to factors as simple as unnecessary mailings, postage, printing and staff overhead. Of greater concern are the secondary costs of lost customers and unsatisfactory client relationships.

The most common factors that affect data quality are:

• Failure to manage data quality at the point of entry:
• The data quality problem starts with data entry. Ineffective or insufficient business processes and real-time validation in operational systems create an opportunity for inaccurate, invalid and inconsistent customer data from the outset. Every contact a customer has with an organisation is an opportunity to validate, confirm or improve the quality of customer information, yet many firms lack the processes and technology to capitalise on such opportunities.
• Invalid, incomplete or inaccurate operational systems data:
• Most major organisations are still heavily reliant upon legacy systems whose underlying data architecture is based upon often outdated historical requirements. This is complicated by the countless modifications and transformations that have occurred over time as business demands change and grow.
• Operational data being used by analytical systems:
• Legacy system data that is of “sufficient” quality at the operational level can quickly become problematic when fed into strategic or analytical applications such as a CRM system, data warehouse or business intelligence environment.
• Integration issues:
• The data problem is magnified when an organisation decides to merge data or processes from two or more systems. Structural and definitional differences between systems lay the groundwork for magnified data quality problems, introducing the additional issues of overlapping, inconsistent and redundant data across systems. The often-sought “single version of the truth” becomes significantly more difficult yet far more important to realise.
• Lack of an ongoing data quality management process:
• Effective data quality management is not a project but an ongoing process. Organisations that invest in one-time data quality analysis and rectification projects without ongoing data management support risk a rapid return to poor data quality. Manual checking of data quality is resource intensive, inaccurate, and difficult.

Addressing Data Quality

• Data Agility’s approach to addressing data quality is both focused and pragmatic. The tools Data Agility apply to the assessment are determined with the client prior to commencement. We offer an engagement that enables an organisation:
• To assess and review the current state of data quality and supporting management processes.
• To quantify the impact of quality issues and identify and prioritise quality “hotspots”.
• To have an implementation plan for cleansing existing data.
• To implement forward-looking processes and technology to ensure ongoing maintenance and protection of the quality of organisational data.
• Deliverables
• A data quality assessment report, quantifying the quality of data in the enterprise and the impact on the business
• An implementation plan for rectification of current data quality issues with expected quantifiable benefits
• An implementation strategy for recommended processes and technology to protect and enhance data quality in future

Data Modeling

Data Agility is proficient at:

• modelling existing data structures
• analysing existing data structures in light of your business goals and making recommendations for improvements
• modelling your ideal or target data structures.

Operational Data Store

The prerequisites for designing and building effective operational data stores (ODSs) are:

• significant experience in both operational and non-operational systems
• clear understanding of business goals

Data Agility’s experience and business insight ensures we design and deliver ODSs that enhance your customer relationships, leading to improved business performance.

Data Warehouses

We design and deliver time-variant data warehouses that enable your organisation to monitor and understand changes within your business over time.

We are proficient in the following technologies:

• ETL tools—Ascential, Cognos, ETI, Informatica and Microsoft DTS
• databases—Microsoft, Teradata and IBM.

Data Mart

Data Agility designs with adaptability in mind. Our first step is to understand your business goals so we can tailor the design and delivery of your data mart or cube to achieve those goals.

We deliver flexible solutions. Our focus on your business goals and agile methods means that when changes occur within your business, our designs can be adapted in a fraction of the time typically expected.