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As Project Managers, we often get consulted on all aspects regarding our favorite area of expertise. This is true in my passion of Business Intelligence or Analytics.
I was flattered when I was recently asked how I would build up an Analytics capability within a medium-sized business that was just about to kick off operations. This particular organization had nothing in place. Whoever oversaw putting this organization together was truly starting from scratch. I have often wondered how I would handle the scenario of creating an analytics organization from nothing. Naturally, I loved every minute of the conversation that was to follow.
I jumped eagerly into this conversation. I started asking questions like:
- Who will be consuming the information?
- How will it be used to move the organization’s strategy forward?
- What are individuals (or even outside organizations) requiring in the reporting?
As I got deeper into the conversation, it became clear that the organization was starting off with an evaluation of technology before answering these very People types of questions. I suggested that more of a focus needs to be placed on acquiring the answers to these questions before deciding what technology to acquire. People will always trump Technology, especially when planning for an Analytics capability. When I talk about People in this article, I am referring specifically to both Customers and Talent.
Customers for the analytics organization dictate how the technology is going to be used. For example:
- Top leadership define KPI’s that show a true reflection of the success of the organization.
- Outside organizations impose regulatory or contractual reporting requirements on the organization.
Customers ultimately drive what the output will be of your analytics engine. It is important to garner a high-level understanding of your customers wants and needs before starting an analytics organization. It is a simple concept, maybe an obvious one. But this can get overlooked, particularly when an organization is looking to achieve the quick result of having a software package installed vs the long-term result of knowing what is needed.
Talent acquisition should next be considered now that you understand your customer’s wants and needs. You should not assume that the talent you will need will be cheap. For instance, do your customer’s demand the results of complex statistical models? If so, you will likely need to hire a data scientist and the market currently pays data scientists a median salary of $123,461 (salary.com).
But what if you don’t have a clear understanding of what types of skillsets should be needed on your Analytics team? I would have the following roles / skillsets scoped into your three-year roadmap. Think of each percentage shown below as a weighting or importance for each role. If you have all of these things covered in your talent pool, then you are 100% covered.
- Business Engagement (35%): Those with this skillset manage the expectations for the output of the analytics organization. They communicate what is needed within the organization and they communicate the constraints and future deliverables outside of the organization.
- Technical Architecture (45%): The individuals know how to mine the data, transform it according to customer requirements, and stage it for consumption. These are the people that turn analytics requirements into capabilities that can be harvested.
- Data Management (20%): These are both the librarians and the referees of the data world. They are primarily there to explain what the data is, where it comes from, and how it can be used. They are also there to determine if a use case or requirement is valid based on the organization’s regulatory or contractual requirements. I find this to be the most overlooked skillset in any Analytics organization.
What about your application portfolio?
Now that you have considered the People (Customer and Talent) associated with your Analytics organization, you can move on to consider your applications side. There are a couple of high-level things to consider when considering any procurement decision.
First, not all tools are created equal. Some have a one size fits all approach and others have a very niche capability. Consider the requirements provided by your customers and then do your research via Gartner, vendor demos, and interviews with other organizations who have used the same tool.
Next, make sure that your purchasing decision lends itself to an “Easy” user experience for your customers. This means your organization’s analytics solution needs to quickly provide your customers with exactly what they want most of the time. It would be detrimental to your organization if the constraints your applications portfolio operate under repeatedly force you to tell your customers that things cannot get done quickly.
So, there you have it. It is conceptually simple to start to define your analytics strategy. Start first with your customer’s requirements, evaluate the talent you have available, and then build your applications portfolio. Follow these steps and you will be well on your way to building a robust Analytics Organization.
Tell me your thoughts in the comments and let’s open a dialog. I would be excited to hear other opinions on this topic.
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