Data-driven organisations are better prepared to protect brand reputation and deal with challenging issues around data privacy, data security models, intrusions or changing data legislations. Working towards common goals can only be achieved with trained staff, data-savvy managers and a good data governance programme.
Our data team have completed a vast amount of data projects in small and large enterprises. What is the data issue in your company? Well, we have good news; datatunnel helps you discover and resolve many data issues.
Our data projects are implemented through a data governance approach. First, we assess the state of your data governance. You have an opportunity to assess the quality of our work.
Then, we breakdown the data issues in several prioritised knowledge areas.
Next, we assess and share the benefits and costs of implementing data solutions and improvements in knowledge areas.
Finally, your team and our team set common project goals with the support of a data management programme and the results are measured with periodic programme metrics.
As our mission states our goal: Implement a successful data governance programme with improved knowledge areas and efficient data operations that control the quality, the costs and the value of your data assets.
Data Governance: Identify the state of your data governance and provide a list of improvements throughout data management knowledge areas.
Data Integration: Connects your enterprise data. Looks at how to efficiently exchange data throughout your organisation.
Data Security: Assess the level of protection and data privacy with regards to data legislation, company policies and architecture.
Data Infrastructure: This knowledge area delivers solutions to setup a central data architecture framework to connect your enterprise data.
Schema Design: This knowledge area looks at how data sources are linking data between systems and delivers solutions for a unified corporate schema model.
Master Data Consolidation: Your organisation consists of many databases and this knowledge area looks at consolidating all master data and reference data models under one central master data framework.
Data Life Cycle Management: Data has a life cycle; data is current, historical or data can deprecate over time. This knowledge area delivers solutions to manage your data life cycle.
Data Quality Management: You can have good data and you can have unreliable data. This knowledge area delivers solutions to monitor the quality of your data.
Metadata Integration: You may deal with structured databases or unstructured data. You are trying to link all that data, but your data asset is not documented with metadata. This knowledge area delivers a central metadata framework to link and search through all your data assets.
Data Warehousing: This knowledge area looks at your data assets and delivers the most suitable data repository framework to manage all your structured or unstructured data sources. We deliver solutions such as data warehouses, data marts and data lakes.
Business Intelligence: Your data is in a good state or bad state and a data visualisation framework provides metrics to retrieve business value behind your data. We deliver a set of visualisation solutions at all levels of your organisation.
Data Science: Data science is a key knowledge area to design data value metrics against your good data. We have data scientists that support your organisation with advanced analytics such as predictive models and machine learning solutions.
We work towards common goals to discover, share and transfer knowledge. Some knowledge areas turn into an uphill battle, but our communication strategy, managed service programme and activities ease the pain to reach stated goals.
When you run a large data governance project, you will need to appoint the right data-savvy team and partners to lead a successful managed services programme.
Metrics: We share programme management metrics on size, effort, time, cost, quality, effectiveness, productivity, success and business value.
Knowledge strategy: We provide documented procedures and retain knowledge in the customer organisation to capitalise on data management knowledge areas.
Data governance board: We help you build a data governance team, trained and empowered with well-defined responsibilities for an efficient decision-making process.
The critical success factors help all teams to be aligned towards a common data project.
Change management operations: To drive communication strategy and develop data roles that have desired attitudes and beliefs.
A data governance programme sets several goals across various knowledge areas. You can expect three types of metrics:
Measure the overall success of the data governance programme. Usually, programme directors share these metrics with company stakeholders and get feedback.
Monitors how bad data over time improves to good data. Usually, data-savvy managers oversee such data improvements.
Insights on measures that contribute to the overall business value. These metrics are provided by data scientists to R&D, Sales, Marketing or Finance departments.
Skill and capacity to manage teams
Sourcing capacity to hire data teams
Capacity to lead managed services of data governance programmes
Capacity to support communication strategy at various levels of the organisation.
Development of programme metrics, business metrics and data value metrics.
Knowledge transfer cycle to deliver updated documentation and procedures.