DERIVING VALUE FROM DATA: Building A Data Strategy

Data is rapidly becoming a strategic asset with just as much value as a company's finances.

Five ways to leverage data to develop a strategy

1.   Use data to create your brand...

2.   Craft presentation and reports that inform and impress. ...

3.   Incorporate visual content strategy in your PR/marketing efforts. ...

4.   Make data-based decisions. ...

5.   Sponsor a campaign by making your data open and free.

Organisations have been looking to take advantage of available data to improve business operations and customer initiatives for ages. What’s changed today is the vast amount of data types and volume, their entry points, and the velocity at which organisations seek to acquire and analyse this data. What often gets lost through the fervour is the understanding that data is not just about collecting and managing data sets. Rather, data must encompass analytics – the ability to assess and identify outcomes and trends to make more informed, impactful business decisions. To reap the benefits of analytics, organisations need to weave their strategies around the efficient acquisition and leveraging of data.

Outline the Business Use Case

The data and analytics journey starts with a pertinent, clearly defined and well-understood business use case. Establishing a set of prioritised use cases at the functional-area level will ensure clarity and strategic guidance for each initiative and provide success criteria for measuring accomplishments. Use cases help create business cases that provide both IT and the business a common framework to address the ROI questions expected from organisational leadership to investors.

How to do Extract Data

The accepted data extraction process involves six steps:

1.   Strong Business understanding

Forming the goals of the project and how data extraction can help, a plan is developed to include timelines, actions, and role assignments.

2.   Clear Data understanding

Data is collected from all applicable data sources. Using Data visualization tools to explore the properties of the data.

3.   Data research

Data is cleansed, and missing data is included to ensure it is ready to be scraped. Data processing can take enormous amounts of time depending on the amount of data analyzed and the amount of data sources.

4.   Data Modeling

Applying Mathematical models to find patterns in the data

5.   Assessment / Analytics

The findings are evaluated and compared to business objectives to determine if they should be deployed across the organization.

6.   Deployment

The data analytics findings are shared across everyday business operations.