Data Democratization Through Self-Service Analytics

Being a data-powered organization means that everyone – no matter what their role or team – should have appropriate access to the data they need to do their jobs and make decisions based on that data. Many of the world’s top enterprises are moving to a more self-service model when it comes to data access, by empowering people to use data in new and creative ways through self-service analytics.

SELF-SERVICE ANALYTICS (SSA) sɛlf sərvəs ænəlɪtɪks | (n)

The system by which line-of-business professionals or analysts can access and work with data to generate insights – predictive or not – and data visualization with little direct support from data scientists, IT, or larger data team (though the SSA platform itself should be supported by these personas).

Why Self-Service Analytics (SSA) Matters

Any company that wants to make any impactful change – whether that’s decreasing costs or risks, increasing revenue, creating innovative new products, or making employees and the organization more efficient overall – has the opportunity to do so using today’s not-so-secret weapon: data. More specifically, the massive amounts of data available can be used to gain insights at scale vis-à-vis processes like reuse and automation.

This is easier said than done – transformation at this level doesn’t simply mean slapping data on top of existing processes; it involves fundamental organizational change, weaving data into the fabric of the company. By integrating SSA into their core business strategy, innovative data companies can build self-service systems that serve their specific needs and requirements and that allow them to use real-time data at scale to make better and faster decisions throughout the organization.

SSA empowers organizations to:

• Give the ability for everyone (with proper access rights) to discover and use data, prepare that data, and create a data product.
• Allow data product creators to share their work with other colleagues across teams and departments.
• Help non-data teams get access to better data insights, understand key metrics, and streamline processes.
• Support the need for quick answers to ad-hoc questions to augment daily, individual decisions.
• Facilitate the deployment of data pipelines in production.

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SSA and Operationalization (O16n) Go Hand in Hand

It’s not uncommon for organizations to implement only SSA and stop there. This is perhaps the result of several years back (circa 2015) where industry leaders and analysts thought that business intelligence (BI) platforms were the be-all and end-all of data-driven transformation.

In fact, in order to become a truly data-powered company and deliver actionable business value from data, companies need operationalization (o16n) as well as SSA. Operationalization simply means getting advanced AI and data projects out of the lab and into a production environment where there is real, business impact, thus aligning them with actual business value.

Despite their seeming opposition, SSA and o16n actually drive each other forward and work best when using a common platform for data governance and communication, built on a philosophy of breaking down silos to work with data across teams throughout the enterprise.

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Build Self-Service Data Systems with Dataiku 

Leading AI-powered businesses use Dataiku to power self-service analytics while also ensuring the operationalization of machine learning models in production. Here are some common ways these enterprises structure their SSA and/or o16n efforts:

• Self-service analytics with data lakes. With Dataiku, projects are developed at the unit level using centralized tools, opportunistic centralized expertise, mixing central data lake data and opportunistic local data. Deployment and productionalization are handled centrally and deployed in decentralized business processes. As new data is used and prepared by the SSA users, the central operations are able to identify which new data sources are the most relevant to ingest into the data lake for easier and broader consumption.

• Center of Excellence (CoE). By providing a collaborative platform where technical and business teams can unite to understand their data analytics, Dataiku provides a scalable CoE. In order to achieve AI Maturity and a pervasive data-driven culture, cross-team understanding and continued engagement are critical.

• Operationalization made easy with a Model API Deployer. Dataiku’s Model API helps automate connections between custom models and global services, so organizations don’t have to reinvent the wheel. Keep on top of critical metrics with predictions on previously unseen records automatically generated in real-time.