September 2017
By Brian Chalifour

 

Being the sole data champion within your organization can present difficulties when you’re vying for limited company resources and attention from the “powers that be.” No doubt, you may find the role to be frustrating at times. Yet you may also find the role to be extremely rewarding, because it gives you a great deal of responsibility and offers you with the opportunity to achieve the goal that every data champion aspires to: Gaining user buy-in of the data insights you’ve unlocked.

finances

This task is made easier by the fact that your company attracts intelligent and motivated employees, encourages constant innovation, and may even have a beer fridge. But there are also challenges at your company that may serve as roadblocks if not navigated correctly. The biggest limitation you must contend with is lack of resources, such as inadequate funding, which can have a trickle-down impact on your data team and inhibit their ability to make data discoveries. This lack of funding generally leads to under-staffing of your data team, stretches employees’ time, and gives them fewer hours to devote to the data tasks you’ve envisioned for them. All while leaving you with less money for access to business intelligence tools and software.

How do you overcome these obstacles that you and your data analysts face on a daily basis? Here are four ways to set yourself up for success and hit the ground running like the number-crunching data champion that you are:

1. Get Buy-in for Your Data

You’ve probably found that getting buy-in for your data insights from executives and employees is the most important and most difficult piece of your job. There are two main reasons for this: First, your colleagues have only limited time to ingest the data analysis you’ve prepared. And secondly, they may not understand your explanation of your data methods or insights, or may simply not be interested. Either way, it’s important for you to communicate your unlocked data knowledge in-person, in a small setting in which only the necessary stakeholders are present. This limited attendance will create a comfortable environment where your data consumers can feel confident as they ask clarifying questions, and where you’ll feel free to ask them your questions.

In turn, the face-to-face interactions will help lay the foundation for a collaborative effort—the key element in all attempts to create buy-in for your data insights. Collaboration with colleagues is absolutely vital. It’s a two-way street that rewards you, no matter which side of the street you’re on. It allows you, as the data analyst, to learn more about the available data from all departments—no small matter, because even though you have your company’s data at the click of a mouse, you don’t necessarily understand all the various inner workings of your company. And, as you gather information from your colleagues, you’ll be simultaneously clueing them in to your full capabilities.

In my experience, the golden nugget from this type of cross-functional learning is that attendees leave the meetings with a better grasp on what the other departments aim to accomplish, and on what pain points each department is experiencing. Ideally, members of the other teams will gain newfound knowledge of my abilities, and I’ll be able to conclude the meeting by asking a question along the lines of:  “Now that you know what I can do, what problems are you experiencing that I can help you solve?” Once you’ve established this bond with your data consumers, and perhaps assisted them with a particular issue, you’ve demonstrated your worth and can subsequently secure buy-in from fellow employees.

At my previous company, these cross-functional meetings provided me with a data epiphany when I learned that I could combine product data sets to generate user-behavior insights that were helpful to the marketing team. When I took this knowledge to the marketing team, I was able to show them that although our target demographic was middle-aged women, the users of the app were mostly men in their 20’s. As a result, our marketing team pivoted the campaign to include tech-savvy males as the early adopters of our product.

 

2. Enable Others

While successfully creating buy-in for data analysis is a necessary component towards achieving company-wide insights, you also need to take this a step further and enable others to work with company metrics. By making data analysis available to co-workers, you’ll be able to tap into other schools of thought as to what the metrics mean for your company. In doing so, you’ll want to work closely with the data architects and employees in charge of data security in order to formulate a plan for providing the necessary data and the tools to colleagues without jeopardizing security.

formulate a plan

Collaborating with data consumers outside of the analytics team can give a rich context to the data your company has gathered. In particular, this collaboration is especially important in startups. In the same way that a startup is working toward bringing a product or service to market, they are also working toward defining their metrics and industry standards. When I previously worked at a tech startup and supplied data to my colleagues, I quickly realized these data consumers had different definitions for the same metrics, prompting me to work towards creating organization-wide definitions that provided a finite internal standard.

Specifically, the organization I worked for was a mobile payments company that allowed users to transact at retailers via their smartphones. We defined the people making transactions as “users”—seemingly simple enough—but we experienced difficulties in defining the frequently-used term “active wallets”. To some, active wallets represented a user; to others active wallets meant a login for the app. To throw another monkey-wrench into defining the term, there was a separate discussion on how to interpret the word, “active”. The word “active” referenced the element of time, but since our startup was working in a field that was still in its infancy, we were unable to reference any industry standard terminology and therefore had to define it internally. After meeting with several data stakeholders, we were able to reach an agreement as to the definition of active wallets, something that would not have been possible if the users weren’t initially enabled with the data.

 

3. Champion Your Needs

Once buy-in and enablement have been established, it’s time to champion your needs in order to increase the value of your analytics. Needs will vary between industries and companies, but chances are, if you’re responsible for data analysis, you’ll face constant needs such as organizing team training to increase your analysts’ prowess with current tools, hiring more staff, securing funding for additional software capabilities, and finding tools for presenting data learnings in ways that maximize their impact on the business.

To champion your needs, you must first determine the correct audience at whom to direct your efforts. You may be targeting a direct superior, a COO who is also an inner data nerd, or a broader group such as an entire business unit. In every instance, your audience should have the ability to make the business decisions that result in resolving your current resource needs. They also need to be persuaded: At all times, you must demonstrate to your audience members that the company will experience distinct benefits when sufficient resources are allocated to the needs you’ve specified.

 

4. Demonstrate Value

Now that you’ve obtained the resources necessary to remove roadblocks and meet your needs, it’s essential to demonstrate further value. The process can work cyclically. Once your data team is able to display value and disseminate increased technical insights throughout the company, you’ve proven yourself and the process can begin anew. But please note that when you’re demonstrating newer data capabilities, it’s crucial how you communicate these capabilities to colleagues at your company. Make sure these capabilities are not just presented to executives within the company—instead, aim towards having the entire company gain some benefit and/or knowledge.

businessThis company-wide reach doesn’t just involve “what” is being presented; it’s critical you focus on “how” as well. That means you need to employ proper visualization and knowledge transfer of the newly gained insights as part of your efforts to obtain further buy-in from internal users. Proper visualization isn’t just limited to attractive and easy-to-understand presentation methods, but can also be taken a step further as you explain your definitions of industry metrics and how certain internal metrics came to be.

In my past experience, I demonstrated this value by using my latest resource (in this case, Microsoft Power BI) to create live dashboards that addressed daily data needs for our executive team. Not only did I make sure the data was useful for this audience, I made sure to include the “wow factor” by creating stunning visualizations that were interactive and easily accessible through each executive’s mobile phone or tablet. By creating a useful tool that important stakeholders could use on a daily basis, I was able to repeat the process on an ongoing basis and gain further resources when needed.

It can be difficult to shake things up with creative insights in an environment where time is a limited commodity. However, when there’s a top-down recognition that data teams can take a step back and analyze internal shortcomings, you can get the go-ahead to generate additional insights that lead to better-informed business decisions.

 

Conclusion

It’s no longer a valid excuse to say, “I could do much more with my company’s data if I only had such-and-such a capability…” This type of rationale creates a stale environment within the data team and inhibits it from the outset. Rather, the challenge to the reader is to highlight an important company-wide need that could benefit greatly from the expertise of your analytics team—and then to champion your ability to meet this need through increased data analysis and a deeper allocation of data resources.

 

About Ironside

Ironside was founded in 1999 as an enterprise data and analytics solution provider and system integrator. Our clients hire us to acquire, enrich and measure their data so they can make smarter, better decisions about their business. No matter your industry or specific business challenges, Ironside has the experience, perspective and agility to help transform your analytic environment.