Allow data to guide the way

Ensure that the technical capabilities of the organization are up to date, can collect and analyse data.

Data and analytics have become modern weapons for competitive differentiation. With the advent of big data companies can now leverage their own data, publicly available data and anything acquired from a third party to gain deep insights into any field being explored. However, most firms struggle to get the most of their data after it’s been collected, the majority of short comings stem from week analytical techniques. Introducing better analytical methods provides clearer avenues to scaling data capabilities with the aim to bolster revenue and reduce costs.

The quality of data and data capabilities of a company will become the differentiating factors to competitive success. Finsys has endeavoured to clearly map out the route for organizations to transform their data capabilities. Four pillars of data governance: data structures, data policies, data tools, and the organization’s participants.

THE FOUR BUILDING BLOCKS OF DATA GOVERNANCE

Data Structures

To best organize and synchronize your organizations data, breaking them down into classifications can help. Primarily there are glossaries, domains, families, models, dictionaries, and flows. Yet, the most relevant structures are data glossaries and data domains, they aid in the organization and classification of data.

A Data glossary is a terminology list used to organize an organizations data. It uses pre set terms to aid in the categorization and internal alignment for meanings and uses of key terms.

Data domains is obsessed with the specific location of data within the glossary.

These tools provide and create the primary backbone for the organization and categorization for an organizations data. These elements serve a vital role in forging a solid foundation that both aid in internal efficiency and enabling the future scalability of data capabilities.

Data Policies

Setting out clear policies around data will help to streamline the way people work with data. These processes, roles, and actions should be for organized data to make it as simple as possible for the talent involved. Vitally these systems should be aimed at increasing efficiency and not bogging down work within bureaucratic systems that serve no function despite reducing the value that can be generated from the data.

Finsys believes the simplest form of executing good data policies is to set up systems for clearly defining data quality, key roles, responsibilities, and ways to measurement metrics for success.

Data Tools

Implementing the right tools will enable the best people to reach their full potential when working with data. Tools can be broken down into two categories; 1. Data Hygiene Tools, and 2. Advanced Data Tools.

1. Data hygiene tools are targeted tools used by organizations to ensure the continuous viability and quality of data glossaries, dictionaries, and data flows.

2. Advanced tools converse the litany of purpose-built tools used to deal with targeted needs. These typically perform sophisticated tasks such as specific fields, single purpose, and multipurpose.

Good data governance is reliant on managers that can merge people with IT.

The Data Organization’s Participants

Once the data infrastructure is in place it becomes important to consider the roles of the people that will inevitably interact with the data. These roles can be broken down into a data governance council (DGC), data owners, data stewards, and data custodians.

• Data Governance Council. The overarching deliberating body responsible for all data related inquires. Additionally, this council defines a company’s data strategy, ownership, and outlines data objectives for governance to meet.

• Chief Data Officer. The individual in charge of the data governance of an organization. With the power to take receptibility over aligning data objectives within an organization with the aim to use frameworks and systems to turn data into value assets.

• Data Owners. Have responsibility over a data domain, with the role to maintain the quality of the data as well as keep all the practices in line with the organizations data policies.

• Data Stewards. Reporting to the data owner a data stewards’ roles is to conglomerate data, report on the data being collected and enact the directions of the data owner to ensure that data quality standards are being met.

• Data Custodians. Working alongside data stewards the role of a data custodian is to implement data quality measures.