On the journey from data to analysis to insight, companies are shifting from a traditional approach and leaping forward into new ways of delivering actionable business intelligence.
While the core goals remain the same — enabling data-driven decisions, optimizing cost efficiencies, and driving revenue growth — new tactics demand new skills. The pivot to a use-case model and good governance throughout the data lifecycle meets those challenges while also delivering faster time to insight.
Connecting the business to fit-for-purpose data
The new data mindset is purpose-driven. Based on specific use-cases generated by the business, today’s data teams build, deploy, and configure purpose-built data assets that meet the organization’s needs fast.
This streamlined process represents a significant shift from the status quo for traditional data teams, but the streamlined workflow pays off. To generate fit-for-purpose data, start here:
- Solicit use cases from the business
- Understand and analyze the characteristics and dynamics of the use case
- Assess your existing data portfolio and identify information that might meet the need
- Consider the appropriate technology to synthesize data sets and deliver actionable insights
Establishing a data asset creation workflow pays off in efficiency and value for IT and the business units involved.
Learn more about how to develop data as an asset >>
Speeding time to insight
Traditional data warehousing models impose a high cost for integrating disparate data sets. A legacy workflow might include:
- Amending data architecture
- Creating a semantic model
- Time-consuming extract, transform, and load (ETL) processes for all data sets involved
- Preparing the data
- Making the data available for analysis
Today’s businesses don’t have that long to wait for insights.
Modern data technologies like Hadoop make it possible to stage data in a platform for immediate access. To structure your data and technology architecture toward a use-case driven model that fosters speed to insight key considerations include:
- A prioritized list of problems that need solutions
- Any characteristics or constraints that might impact time to value
- Available data assets and technologies, like data virtualization, that would enable you to access and analyze data in place
Once you adopt methods for analyzing data in place, your team can deliver value on a much shorter timeline.
Learn more about data architecture and integration >>
Improving data literacy
The demand for data-driven insights continues to accelerate. Companies at the forefront of the shift from volume to velocity use analytics pervasively throughout their organization and have the technology and agility to act on insights quickly.
To become a competitive, speed-driven organization, your business must excel throughout the analytics lifecycle:
- Acquire: Harvest data quickly by exploring evolving big data technologies and optimizing first-party data strategies
- Analyze: Identify the most impactful insights
- Act: Implement the insights iteratively and strategically
That final step involves your organization’s data literacy. Providing insights is one thing, but training your people to take the next right action on the data they see might require new skills. Upskilling the workforce to better understand and use data pays off richly in transformative accuracy, speed, and confidence.
Learn more about building data literacy within your organization >>
Implementing harvest-to-delivery data governance
As the volume of available data continues to increase, businesses are building complementary abilities to understand and use it. But implementing tools and technology to harvest, integrate, and analyze data without robust governance frameworks opens companies up to significant risk.
Building strong governance into your data asset creation and management workflows from the start can help.
Learn more about how to implement good data governance >>
Elevating data leaders
As the world becomes more digital, and more customer behaviors move to a mobile context, businesses are changing to meet and match digital footprints with geospatial dimensions. Leadership must keep pace
The need for a Chief Data Officer (CDO) at the table isn’t really a question anymore. Today, leading companies are asking where analytics and digital belong in the leadership playbook. To get the most value out of your data management, the right team members — with the right support and authority in place — could not be more important.
Learn more about the importance of empowering your CDO >>
Data management can be complex. A strategic viewpoint can help. Find out more about Fusion’s approach to strategic data management, or ask us your questions. Wherever you are on your data journey, we can help you keep moving forward.