How Can Leaders Ensure Their Analytics Projects Are Successful?
Businesses are implementing analytics and trying to use data to uncover new insights about their operations, customers, suppliers, employees, and so on. Even though the idea of using analytics is exciting, these types of projects are not for the faint-hearted — at least if you’re trying to implement analytics across the entire enterprise. Businesses today are faced with a myriad of challenges around implementing analytics. There is a combination of technical, process, and people challenges that must be overcome. Some of these challenges are more difficult than others. Thus, what are some of the things that leaders can do to improve the chances of success and perhaps have fewer headaches in the process? How can you ensure they are successful? In this Advisor, I will attempt to answer these questions, based on some of the research that I have done, along with ideas and frameworks that have worked for others in the past.
If you’re focused on delivering self-service analytics to your customers (end users and internal or external customers), here are two things to remember: analytics solutions must be both easy to use and useful to those who are going to use them. For your end users to adopt any new technology, keep these two things in mind. It may also be helpful (and there’s even research to support this) to include some of your power users in the implementation process. Create prototypes and sample visualizations or models that your power users will be using in their own work and have them provide early and frequent feedback. With early feedback, you’ll be able to improve what you are delivering to them. Many teams use an Agile methodology to deliver projects — and your analytics projects can benefit from adopting the same type of methodology.
It’s important to think about defining what success means for your project as early as possible. Establish the main goals for the project, business justification, and organize the analytics work as a project. Effective project management during each phase of the project is equally as important as the results and deliverables. Defining your “version” of success will be unique to your organization, so it’s best practice to attempt to define your success factors as you begin your analytics journey. Consider rolling out analytics to smaller groups of people first, as a pilot test, before attempting to deploy your solutions across the entire business. You might want to consider the best organizational structure for your analytics work as well — do you have primarily a centralized structure, or highly decentralized, or some hybrid structure?
Alignment between the technology stack that you choose and the people who are going to be using it is something to consider as well. For example, the different data visualization tools all have their relative strengths and weaknesses, and some are certainly “easier” to use than others for your typical end user. You will likely have to make a judgment call and even ask your end users to test out some of these projects before you completely commit to one of them. Consider the ease to use factor when rolling these data visualization tools out to your business unit staff. If your self-service analytics aren’t easy to use, your users won’t use them. You should be thinking about the learning curve for these tools.
Have you decided what your goals should be for implementing analytics in your business? Establishing some goals will help you and the team focus their energy and help constrain the scope of a project. If you’re lucky enough to have a project manager on your team, that person can help facilitate discussions around this, and help formalize your analytics projects. Even if you don’t have the luxury of having a dedicated project manager, you want to make sure you have clear goals for your project, list out your success criteria, and develop a short-term roadmap for implementing analytics across the business. Your roadmap can begin with offering some type of data visualization capability to some power users, give them a few visualizations to start with, and help them get started on the right foot.
Have your power users literally play around with the software and data, to slice and dice the data in different ways. If your company is completely new to analytics, it can be very helpful to get a data visualization tool, like Tableau or PowerBI, into the hands of some power users and let them at it. Choose a data visualization tool that is easy to use and has a relatively low learning curve. I’ve seen this tactic be hugely successful as people get to experiment with data in ways that they may never have seen before. Some of my colleagues whom I've done this with in the past have been instantly hooked. After showing them a few visualizations and showing them how to use a tool like Tableau, they start to uncover some interesting insights for their departments or the company.
Speaking of getting “hooked,” as leaders we need to help create a “buzz” and sense of excitement about what we can learn through analytics. A perspective that has worked well in many of my roles has been an attitude of, “If I don’t get excited about this, how can I expect anyone else to get excited about it?” The excitement and buzz that can be created by really being a cheerleader and champion can make or break these projects. They are unique projects and can feel foreign to some of your staff. Being a leader who is visible and part of the success of the project can go a long way. As a leader and data geek with well over 20 years of data-related experience, it’s easy for me to go deep and have people look at me like I have 10 heads. I’ve learned that I must focus intently on what’s in it for my customers (internal and external). What do they seek to gain from using the data visualization tool? My customers nearly always hear my excitement when I talk about the value and insights gained from the visualizations.
To summarize, here are some key points for improving the likelihood of project success:
- Get excited about it! It’s catchy!
- Allow users time to explore and “play” with the data visualization software after you’ve provided them with some basic training.
- Establish appropriate goals for the analytics projects, control scope, and be sure to establish success measures at the beginning of the project.
- Involve your users in the implementation process.
- Solutions should be both easy to use and perceived as useful to your end users. Without these two factors, it is likely your users won’t adopt the new technology.