Business Intelligence and Analytics

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 The value of Business Intelligence is that is is what is needed to produce Analytics. You have to have effective collection, standardization, organization, and centralization of data to drive good Analytics. The value of Data Analytics is realized in using the insights produced to make better decisions and create improvements in the processes or initiatives of the specific departments or business units that the insights are created for.Then to ensure effective utilization of those insights,  you must partner with those departments or business units that you are creating insights for to make sure they see and understand the patterns that exist and the story that is being told by that data. This also means knowing your audience, you may present your data and information to a marketing creative team differently than you do a finance team.

How does one measure good Business Intelligence and Analytics?

There is business data and there is Business Intelligence and Insights, The later doesn’t come from numbers and data it comes from ideas, and creativity. Good Business Intelligence produces Analytical Insights that tells a story with the data, using a narrative and visualizations. It allows data analysts to take large amounts of complex data from various data points and create rich stories that are easier to assimilate and consume, yet far more informative to business leaders.

How does one make sure that insights derived out of data are impactful and create business value?

This goes right back to the beginning of the answer to the previous question. The value comes from the effective utilization of those insights. But first.

1. You must have the right data to analyze, cataloged and organized correctly.
2. You must have an effective solution to gather, store and analyze the data
3. You need to be able to pull that data into visualizations designed and presented in a way that allows its consumers to assimilate large amounts of data in a small amount of time and in a way that is easy to understand.

Then to ensure effective utilization of those insights,  you must partner with those departments or business units that you are creating insights for to make sure they see and understand the patterns that exist and the story that is being told by that data. This also means knowing your audience, you may present your data and information to a marketing creative team differently than you do a finance team.

What are my top 3 recommendations to drive the creation of a data culture in an organization?
From the viewpoint of getting buy in for the new culture, I would recommend

1. Start the conversations and make sure they include of more than just top leadership. Buy in for a solution rest just as much if not more in the acceptance from the employees closest to it and most affect it by it as it does the Top executives. Articulate to them the purpose and the benefits of the work and changes that will be coming. Explain to them the intangible values of the solution that benefit them.
2. Initiatives to start understanding what data is valuable and important to the different business units. Start showing the varying units how the elimination of siloed data can allow access to more data that can improve their individual departments.
3. Initiatives to create data literacy.

From the viewpoint of implementing solutions that will support the culture

1. Initiate projects to start evaluating your data, how it’s being captured ,what governance is in place and what you need to improve data quality and standardization. Also to understand the data and how it needs to be used to support business goals.
2. Once you have an inventory of your data, some standardization and understanding start projects that focus on what data will be needed to support critical business priorities and initiatives.
3. Start projects that will prototype the collection, storage, analysis and visualization of this data to prove the value of the data and its analytics. Focus on designs that are scalable, that can support your varying data and that can produce self-service analytics.
4. Architect the data platform solutions that will support the data engineering and science that will support your analytics needs.