Data innovation is flourishing, driven by the confluence of exploding data production, a lowered barrier to entry for big data, as well as advanced analytics, artificial intelligence and machine learning.
Additionally, the ability to access and analyze all of this information has given rise to the “citizen analyst” – a business-oriented problem-solver with enough technical knowledge to understand how to apply analytical techniques to collections of massive data sets to identify business opportunities.
Empowering the citizen analyst relies on, or rather demands, data democratization – making shared enterprise assets available to a set of data consumer communities in a governed way.
This idea of democratizing data has become increasingly popular as more organizations realize that data is everyone’s business in a data-driven organization. Those that embrace digital transformation, regardless of industry, experience new levels of relevance and success.
Securing the Asset
Consumers and businesses alike have started to view data as an asset they must take steps to secure. It’s both a lucrative target for cyber criminals and a combustible spark for PR fires.
However, siloing data can be just as costly.
For some perspective, we can draw parallels between a data pipeline and a factory production line.
In the latter example, not being able to get the right parts to the right people at the right time leads to bottlenecks that stall both production and potential profits.
The exact same logic can be applied to data. To ensure efficient processes, organizations need to make the right data available to the right people at the right time.
In essence, this is data democratization. And the importance of democratized data governance cannot be stressed enough. Data security is imperative, so organizations need both technology and personnel to achieve it.
And in regard to the human element, organizations need to ensure the relevant parties understand what particular data assets can be used and for what. Assuming that employees know when, what and how to use data can make otherwise extremely valuable data resources useless due to not understanding its potential.
The objectives of governed data democratization include:
- Raising data awareness among the different data consumer communities to increase awareness of the data assets that can be used for reporting and analysis,
- Improving data literacy so that individuals will understand how the different data assets can be used,
- Supporting observance of data policies to support regulatory compliance, and
- Simplifying data accessibility and use to support citizen analysts’ needs.
Democratizing Data: Introducing Democratized Data
To successfully introduce and oversee the idea of democratized data, organizations must ensure that information about data assets is accumulated, documented and published for context-rich use across the organization.
This knowledge and understanding are a huge part of data intelligence.
Data intelligence is produced by coordinated processes to survey the data landscape to collect, collate and publish critical information, namely:
- Reconnaissance: Understanding the data environment and the corresponding business contexts and collecting as much information as possible;
- Surveillance: Monitoring the environment for changes to data sources;
- Logistics and Planning: Mapping the collected information production flows and mapping how data moves across the enterprise
- Impact Assessment: Using what you have learned to assess how external changes impact the environment
- Synthesis: Empowering data consumers by providing a holistic perspective associated with specific business terms
- Sustainability: Embracing automation to always provide up-to-date and correct intelligence; and
- Auditability: Providing oversight and being able to explain what you have learned and why
erwin recently sponsored a white paper about data intelligence and democratizing data.
Written by David Loshin of Knowledge Integrity, Inc., it take a deep dive into this topic and includes crucial advice on how organizations should evaluate data intelligence software prior to investment.