Mark Flaherty (MF): We have talked quite a bit about how critical for the success of BI program a BI strategy is. We have also talked about what’s part of the scope of BI strategy. So we know it's critical; we know what’s part of it; and what you need to do to have a whole one, a complete one.
Yet, many companies can't figure it out. Why is that?
Well, first of all, they didn’t begin by looking BI at a strategic distance. Most organizations solve BI issues tactically. They have a specific need, specific application they want more information about, and they solve that problem. They do it in a one-off fashion, and there is no holistic view. If you want to have a strategic BI program, to have a developed the strategy, you can do so.
You can get there, as we said, before by linking it to goals and objectives that the organization has. If you don’t have that view of the whole process, then you simply can't have strategic business intelligence. And just to give you one example of what I have seen, a marketing team started to develop a beautiful warehouse.
Reporting Around Marketing Activities
They were doing all kinds of reporting around marketing activities, but then ultimately of course, they wanted to know it from a funnel perspective, the leads, how do they convert in the funnel and what’s happening from a sales follow-up perspective? And then, ultimately, what are the actual results?
What’s the revenue that’s coming out of that funnel so you can see the opportunities but also the closed opportunities? And as usual, the scope of the marketing team’s activities tactically has limited it to controlling just the marketing data. But now, they need the sales data. Now, they need the finance data. And if you tackle it tactically, you are probably not going to get all that.
This leads us to are talking about methodology here. There are commonalities to solving these problems, and one of the key things that we do as a methodology in business intelligence projects is begin by understanding where is all the information we need. And so having that rigor, it's part of the strategy. You develop the notion that we have a methodology.
We are going to apply it to these problems as they come up. And a methodology, that just is a great key for me because a lot of teams, they don’t have a methodology to figure out the BI strategy. They don’t have an approach that’s repeatable. They don’t have an approach that they can use to go out to the different lines of business and make them all think about and understand what good it will do for them individually as a department and at an enterprise level.
Center of Excellence for BI
So, having a methodology for whoever owns IT, that center of excellence for BI hopefully, whoever owns that, they need a methodology, they need a way of going around, figuring it out, documenting it, articulating it because then they have something that can be approved, can be bought into and can be invested into.
Now, we talked about clearly having to have a methodology for how you go about it, and we also talked about – I think what’s also very important – the more strategic approach to BI, so that it's not just a tactical departmental thing.
There is one more key point to make. It's really critical that somebody owns this business intelligence function. If you have a strategy, and you have set of methodologies for achieving it, then the question is when something comes up, who ensures that that strategy is thought of and that that methodology is applied.
So if somebody in the organization actually has ownership of that strategy, then we can ensure that new projects, when they arise, are actually going to be executed in conformance with that strategy and that we will propagate that methodology. We talked about a center of excellence. That’s obviously the place where this can live. But even if you don’t have a formal competency center, a center of excellence, you need an owner for the strategy, and that owner needs to be involved every time you have a project.
Members of a Center of Excellence for BI
Comprised of a diverse team of skilled professionals, each member plays a crucial role in harnessing the power of data to drive business success. Let's delve into the key members of such a CoE and their respective roles and activities:
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Data Architect: At the helm of the CoE, the Data Architect designs the blueprint for the organization's data infrastructure. They oversee the development of data models, ensuring data integrity, security, and scalability. Collaborating closely with IT teams, they implement best practices for data governance and establish standards for data integration and management.
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BI Analyst: Armed with analytical prowess, the BI Analyst is tasked with transforming raw data into actionable insights. They utilize BI tools and techniques to conduct in-depth analysis, identify trends, and uncover patterns within the data. By generating reports and visualizations, they empower stakeholders across the organization to make informed decisions and drive performance improvements.
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Data Scientist: Bringing a blend of statistical expertise and programming skills to the table, the Data Scientist delves into the realm of predictive analytics and machine learning. They develop sophisticated algorithms to forecast future trends, detect anomalies, and optimize business processes. Leveraging advanced statistical models, they unlock the predictive power of data to anticipate market dynamics and gain a competitive edge.
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ETL Developer: Charged with the critical task of extracting, transforming, and loading data from disparate sources, the ETL Developer ensures the smooth flow of information within the BI ecosystem. They design and maintain ETL processes, integrating data from diverse sources such as databases, APIs, and flat files. By streamlining data workflows, they facilitate real-time access to accurate and reliable information for decision-makers.
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Business Analyst: Bridging the gap between IT and business stakeholders, the Business Analyst serves as a conduit for translating business requirements into technical solutions. They conduct thorough needs assessments, gather user feedback, and define functional specifications for BI projects. By championing user adoption and conducting training sessions, they empower business users to leverage BI tools effectively in their day-to-day operations.
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Data Quality Manager: Upholding the highest standards of data quality and integrity, the Data Quality Manager oversees data cleansing, validation, and enrichment processes. They implement data quality frameworks and establish metrics to monitor the accuracy and completeness of data assets. By proactively addressing data anomalies and inconsistencies, they ensure that decision-makers can rely on trustworthy information to drive strategic initiatives.
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Project Manager: Orchestrating the execution of BI initiatives from conception to delivery, the Project Manager oversees project timelines, budgets, and resources. They collaborate with cross-functional teams to define project scope, objectives, and success criteria. By employing agile methodologies and fostering collaboration, they drive continuous improvement and ensure the successful implementation of BI solutions that align with organizational goals.
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Visualization Specialist: With a keen eye for design and a knack for storytelling, the Visualization Specialist crafts compelling data visualizations that resonate with stakeholders. They leverage intuitive dashboards, charts, and graphs to communicate complex insights in a digestible format. By enhancing data visualization techniques and staying abreast of industry trends, they elevate the user experience and drive engagement with BI solutions.