BI Project Plan 2.5/5 (2)

The development of the BI Business Intelligence Project roadmap provides direction to … to the delivery of projects, but also a high standard of BICC reporting strategy, training
Business intelligence Strategy and Roadmap 2018
Business intelligence Strategy and Roadmap 2018

Business intelligence Strategy and Roadmap 2018

Here are 5 steps you can follow when starting your BI journey.
  1. Structure your BI implementation project.
  2. Identify the requirements for your BI platform.
  3. Choose your BI platform.
  4. Implement your BI platform.
  5. Measure the value of your BI solution

Why BI Strategy & Roadmap?

Business Intelligence (BI) is a way of exploring data to improve business performance, whether to drive profitability or manage costs. It is not only technology you implement and then put in maintenance mode. Whether you are implementing Business Intelligence for the first time or expanding an existing implementation, it’s important to be clear about the goals of your deployment.
You maybe implementing BI system as part of an IT effort or as part of a specific line of business initiative. The goals of these two groups can be quite difference and complex. The goals of the two groups can be quite different . The goals may be driven by the following:

a Sales Analytics Improve customer loyalty, manage products prices, increase market share;
b Supply Chain Analytics On-time delivery, low freight costs;
c Finance Analytics Reduce aging of accounts receivable, reduce budget variance, improve profitability   
d HR Analytics Reduce employee turnover provide competitive pay

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Tip 1: Calibrate the role of IT to fit self-service BI requirements

In traditional enterprise BI environments, most users consume the data, applications and visualizations that IT produces. The self-service trend requires business and IT leadership to be more flexible and calibrate the amount of IT involvement to fit what users are trying to do. The main objective 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 helping to scale up applications.

Tip 2: Update governance to embrace self-service BI and analytics

Users seeking new sources for data discovery and analytics don’t like waiting for new data to be incorporated into the existing data warehouse. Big data lakes and cloud-based data sources 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 quality and consistency. Organizations will need to examine current BI governance strategies and make sure they account for the expanding data environment.

Tip 3: Revise the semantic layer to support self-service interactive reporting

One of the advantages of mature enterprise BI 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, diverse and distributed self-service technology can make development and maintenance of a semantic layer challenging and complex. Organizations should evaluate their existing enterprise BI and data warehousing semantic layer to ensure it can extend to ad hoc, self-service BI and analytics use cases.

Tip 4: Balance enterprise BI standardization with user agility

When decentralized and not well coordinated, each self-service technology implementation can become its own data silo. Organizations struggle with balancing user agility and BI standardization. TDWI recommends three steps:

  • Provide managed self-service that offers guidance.
  • Create 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 BI governance and self-service capabilities for users. To avoid the pitfalls of self-service data preparation, TDWI recommends that 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 open architecture to match workloads with technologies

Open source and cloud technologies require organizations to take a fresh look at their enterprise BI 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 BI and data warehousing. The strategy must have flexibility and openness to take advantage of the potential of new technologies and methods.

Tip 7: Refresh training to fit diverse user needs

Even though BI and analytics tools 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 recommends mentoring through BI teams and encourages collaboration and tip sharing to help users learn from each other.

Questions ?

mondy.holten@mr-data.nl

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