Self-service business intelligence (BI) is flourishing. As businesses require more flexibility and increased efficiency, relying entirely on IT is no longer a probable solution. In order to manage large amounts of data at a minimal cost, self-service methods are necessary; however, this doesn’t come without risk.
As a way to mitigate this risk, companies are increasingly leaning towards well-governed, self-service models that combine the necessary speed and flexibility of self-service models with the required accuracy and security that comes from governance.
What is a governed model and what is a self-service model?
It is first important to discuss both the self-service model and governed models as distinct entities before going on to the governed, self-service model. In the 100% self-service model, the organization gets direct access to complex technical tools and datasets on-premise. They use these tools to extract data, creating reports and gleaning information from them. The advantages are that it’s affordable, allowing users to avoid the past IT enterprises that were previously necessary; and it allows businesses to access their own data directly and quickly.
With the 100% governed model, the data is fed to an IT team where they construct data pipelines from the data source and put it all into a central data warehouse. While there are advantages to this (it’s more secure, for example), there are also disadvantages: The delivery cycle is slow and completely controlled by IT, with even minor changes possibly taking months to implement.
Because of the delay inherent with 100% governed solutions, organizations are increasingly opting for the self-service route. But self-service methods will not work without diligent governance.
Why is the governed, self-service model important?
Customer experience is increasingly digitally-driven and demanding, requiring more and more insight to deliver personalized experiences and services. In order to achieve this, information derived from data must be acquired quickly. This means that businesses cannot rely on sluggish and inflexible IT solutions. They must employ self-service BI tactics to accommodate the increased need for information and data on demand.
Of course, a system that is exclusively self-reporting is difficult to remediate, often resulting in reporting issues. There are risks common with self-service analytics, with some of the most common being the following:
- Data security issues.
- Flawed logic or metrics can make the data models useless.
- Serious maintenance difficulties.
- Incorrect or flawed data can lead to poor business decisions.
- Reporting errors, which can then diminish credibility.
- Possible compliance failures.
Due to the specific challenges associated with self-service BI, organizations must employ adequate levels of governance without relying on the rigid restrictions present in models that are 100% governed. The best way to balance governance and self-service is through a data integration hub. These hubs facilitate easier interaction, which leads to faster insights and higher levels of productivity. There are innovative tools allowing businesses to move past traditional ETL data pipelines and towards more progressive means of data exploration and analysis.
Lyftron, for example, poses a unique solution by allowing users to build data sets for their BI tools and data science in one place. It serves as a data hub and semantic data layer for BI tools. It also eliminates the need to manually build data pipelines, because BI users are able to collaboratively build data models that use real-time data or replicate data only on demand. Fivetran also makes it easier to minimize data pipeline maintenance. Its cloud-based ELT data pipeline allows users to consolidate all data into one data warehouse in just minutes. This zero configuration method reduces risk, conserves analytical and technical resources for strategic projects, and allows for greater business agility to deploy new tools and scale across an organization. Ultimately, this allows for businesses to make quick and actionable business decisions from readable, ready-to-go data.
When the technical limitations are removed, businesses and analysts have more time and a greater capability to go more in-depth with their data. They can solve problems faster and more creatively. All involved teams in the organizations are able to engage more fully with the data, moving the business forward in ways that are both positive and secure.
Pacific Wealth Solutions (PWC) relies on huge amounts of cloud-data to optimize its clients’ insurance investment strategies. Because of a highly effective governed, self-service data management model, the company is able to analyze their data quickly and accurately with a high level of security. They can view solutions in real-time for a truly enhanced client-experience.
The most effective tools contributing to governed, self-service BI are those that make it easy to migrate data securely and effectively. With this model, organizations can merge self-service BI with governed data sources. This will allow businesses greater agility and, at the same time, risk mitigation. A company’s internal data is perhaps the most vital asset in its possession; however, if it cannot leverage that data quickly and accurately, it is virtually useless. Truly beneficial insights come from an ability to leverage and control data without compromising it. This can be accomplished only through a governed, self-service BI strategy.
