| |December 20199· Current & future reporting & analytics require-ments (demand side)· Source Data & information dependencies (inter-nal as well as external)· Appetite for Technology disruptors in the BI area (supply side from IT)· Maturity of the organization in using agile methodologies· Chalking out a budget for the programInternally, IT needs to look at below areas to infuse further inputs into the BI strategy:· Alternatives to end-to-end architecture support-ing the BI strategy i.e. data warehouses / data marts, big data ecosystems, in-memory, self-serve, mobility, portal enablement· Cloud considerations i.e. analytics & reporting engine could be on cloud vs on premise · Metadata to drive BI i.e. security, user access, data taxonomies including metrics definitions, re-port inventory· Customization options using additional pro-gramming such as Python or SDK· BI tools capabilities required for the organiza-tion in the medium to longer termBelow, I have identified certain show stoppers typical in such journeys & tried highlighting here as lessons learnt. There could be others based on your re-spective organizational situations.Based on the size, business corporate structure, and complexity of data & reporting in organizations one or more BI tools can be suggested. Organizations are moving away from a lock in with single BI ven-dors especially given the flexibility available with licensing, scalability with cloud, reusable security models & metadata. However, a word of caution, if or-ganizations do choose to go down multiple BI tools: Ensure these tools go through a single security/user access management module, reuse common data re-positories and have tight governance on report & data change management processes (guard rails). It is never a terrible strategy to own two BI tools as long as the guard rails mentioned above are adhered to.Pull up market research from trusted sourc-es to validate the options on the table e.g. Forrest-er waves, Gartner magic quadrants et al. Always map the requirements of the organization (current & future) to the capabilities you will look for in a BI tool. Never over engineer the solution as the cost of carrying redundant components could be high in longer term. Focus must be on empowering the end user in a federated model wherein self-serve capa-bilities are provided out of the box. Of course, this will be effective only when the users are given ade-quate training on the tools. Ensure training sessions are in house, where possible, with a boot strap proj-ect where instant gratification will be realized by the users.When Analytics is a requirement in the BI spec-ifications, ensure there is a Data Science capability readily available with IT. The data scientists need to be subject matter experts with a clear understanding of the data environment in the organization. First few use cases for each business unit could be a joint development with the data scientists & the business unit where ample opportunities for data preparation & modeling discoveries are possible. Any advanced Analytics needs will need a separate program within the organization to stay successful.Finally, define metrics that will help the organi-zation track success. A key metric I like to track is the percentage of data / information used for decisions delivered completely out of the BI eco-system. In conclusion, a BI makeover will be effective if the BI strategists keep the approach simple to begin with and ensure maximum buy-in from the stake-holders. Of course, speed to value will be another key metric to declare the program a success. Good luck with your BI makeover! A BI MAKEOVER WILL BE EFFECTIVE IF THE BI STRATEGISTS KEEP THE APPROACH SIMPLE TO BEGIN WITH AND ENSURE MAXIMUM BUY-IN FROM THE STAKEHOLDERS
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