Analytics tools and Self-service BI excite users with easy ways to get data-driven answers to business questions. Then again, IT-led enterprise systems have become a hallmark of business intelligence because they solve important issues like consistency, data quality and improved governance.
Today’s Analytics tools and self-service BI are easier than ever to use. Data visualization has given more users power and control to interact with data and those who have experienced the freedom of exploring data on their own and constantly asking new questions often BI enterprise solutions as inflexible and restrictive.
So is there a way to bring IT-led and self-service BI enterprise solutions together? TDWI offers seven ideas your organization should consider as it strategies to meld user productivity and enterprise business intelligence governance into a peaceful coexistence.
Tip 1: Calibrate the role of IT-Led to fit self-service BI requirements
In traditional enterprise business intelligence environments, most users consume the data, applications and visualizations that IT produces. The self-service trend requires IT leadership and business to be more flexible and calibrate the amount of IT involvement to fit what users are trying to do.
The main target for IT should be to adopt an enabler role and help users achieve their goals by guiding them to the right data, advising how they can get the most out of BI tools and helps to boost your applications.
Tip 2: Update BI governance to embrace self-service BI and analytics
Users seeking new sources for analytics and data discovery don’t like waiting for new data to be incorporated into the existing data warehouse. Cloud-based data sources and Big data lakes are growing in part because users need access to a wider variety of data. Unfortunately, these sources are often not adequately governed, much less vetted for the consistency and quality.
Tip 3: Revise the semantic layer to support self-service for interactive reporting
One of the advantages of mature enterprise business intelligence and data warehouse architectures is having a coherent and up-to-date semantic layer, from which self-service BI and analytics can also benefit. However, distributed and diverse the self service technology can make development and maintenance of a semantic layer challenging and complex.
Organizations should have to evaluate their existing enterprise BI and data warehousing semantic layer to ensure it can extend to self-service BI, ad-hoc, and analytics use cases.
Tip 4: Balance enterprise business intelligence standardization with user agility
When not well coordinated and decentralized, each self service technology implementation becomes its own data silo. Organizations will always struggle with balancing user agility and BI standardization. TDWI recommends the three steps:
- Provide managed self-service that offers guidance.
- Create an self-service applications that offer standard choices within them.
- Aim for less obtrusive IT management and governance.
Tip 5: Introduce self-service data prep carefully
Data preparation is a key concern for those trying to balance self-service BI governance and self-service capabilities for users. To avoid the pitfalls of self-service BI data preparation, TDWI recommends that the organizations centrally monitor metadata, integrate data prep with governance and aim for higher levels of repeatability using automation and web-based administration technologies.
Tip 6: Develop an architecture to match workloads with technologies
Open source and cloud technologies require organizations to take a fresh look at their enterprise business intelligence and data warehousing architectures.
It may be time for a hybrid approach. Not all use cases and workloads will need the rigorous governance and structure of a traditional single architecture for enterprise business intelligence and data warehousing. The strategy should have the openness and flexibility to take advantage of the potential of new technologies and methods.
Tip 7: Refresh training to fit diverse for user needs
Even though analytics tools and BI are becoming easier to use, it is not necessarily straightforward to understand and apply BI and analytics techniques, particularly for nontechnical users. Among other strategies, this report feature recommends mentoring through BI teams and encourages collaboration and tips sharing to help users learn from each other.
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