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Statistically, you’ve got a 45% chance to succeed with your business analytics project. What do you need to know to put yourself in the successful minority? 

Business analytics technology has become more mainstream and accessible, but that hasn’t made the implementation of analytics initiatives a guaranteed success. A recent study conducted by Info Chimps found that 55% of Big Data projects fail. Considering how much time, money, and energy goes into conducting a data analysis project, this high of a failure rate is daunting.
 
The study shows us there is a huge gap in the current process that isn’t being addressed. Decision makers don’t want to miss out on “hidden insights,” and their IT staff is more than happy to show them interesting things they’ve found, but the dots aren’t being connected and the right questions aren’t being asked. So what do you need to do to avoid an incomplete data analysis project or an all out failed project?
 
You need to have an idea of what you’re looking for

Often we think of business analytics efforts as finding a needle in a haystack or finding a hidden gem. This magic nugget of information will then direct us to something positive for our business. Instead, it’s more efficient to start with your desired outcome. Do you want to be more efficient? Do you want to improve customer satisfaction? Know what your desired goal is and then you’ll have a better sense of what you need to do to get you there.  This way your IT team knows what exactly they’re looking for and they will have a better sense of what they need to show you. Consider taking a lean data approach and work efficiently around the questions you’re trying to answer instead of relying on digging through data sets to find something useful.

You need to think about size, flexibility and experimentation

You might not be dealing with Big Data, but that doesn’t mean your data isn’t worth analyzing. Small amounts of data can still contain meaningful insights. It’s all in how you look at it and this is where visualizations and user interfaces are important features that can either impede or ensure your success.

You need to be flexible in your approach to data analysis because one finding leads to another finding and so on. This needs to be the case with your interface as well. The technology you use needs to have an interface that is intuitive and promotes data discovery. It should feel seamless. Additionally, the way your discoveries are visualized has a huge impact on project success rates. Your work needs to be displayed in a way that makes your discoveries easy to understand.

Starting small, on a project-by-project basis will give you the foundation you need to grow from and will start you on a path to best practices. Your ability to be experimental with your data will help you find more valuable results and how your data is visualized will help decision makers understand what your data findings are showing them. Overall, you need to find a technology that you feel comfortable using and you need to be able to scale projects and deliver results in a way that works for you and your organization.
 
You need to be aware of the skills gap

In the McKinsey Global Institute report, Big Data: The next frontier for innovation, competition, and productivity, the authors state:

                “There will be a shortage of talent necessary for organizations to take advantage of big data. By 
                2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep 
                analytical skills as well as 1.5 million managers and analysts with the know-how to use the 
                analysis of big data to make effective decisions.”

This is a scary fact, especially when you realize the majority of data analysis work is actually done by people, not algorithms. The most time consuming part of the data analysis process, and the biggest hurdle to achieving actionable insights, is the data cleansing process or what is referred to as janitorial work or data wrangling.
 
You need to be aware of the talent you have access to and where your organization needs to invest both time and money. Be honest about what you’re capable of achieving on your own and where you may need outside help. You don’t necessarily need to invest in hiring data science professionals. You can achieve a lot by planning properly and utilizing the right resources and bringing in consultants. As mentioned above, you want the ability to be flexible and agile.
 
Taking advantage of an experienced data scientist on a project-by-project basis can be hugely helpful because they bring years of experience, knowledge, and best practices to your projects. Look for vendors who will partner with you instead of just delivering you a technology and be leery of anyone that claims data science is easy. A vendor that provides you the technology, the experienced talent, the project vision, and visualization tools will be able to get you to your end results quickly, efficiently, and at a lower cost than a vendor who just provides the technology.

You need to focus on the last mile

The “last mile” refers to the decision making phase of the data analysis process. How do you present the analysis to the decision makers and what do they need to do with it? This is where the baton gets dropped and where major project failures happen. So much time and energy goes into getting the analysis up and running, but not enough energy is focused on delivering findings in a way that inspires action. This is why having an end goal from the beginning is so important. Decision makers need to be part of the process from the beginning. The “why are we doing this” question needs to be addressed and everyone needs to be on board before you move on to “how are we going to do this?”
This is also why being agile and having a flexible solution is highly valuable. You can ask the right questions and get the insights you are looking for, but if the results aren’t easily shared, there is a breakdown in communication. Presentation makes all the difference and an interface that allows you to show your results and how you arrived at them will do a lot to helping you avoid tripping up in the last mile. Look for interfaces that help you achieve the balance of data discovery and knowledge sharing. Having this built into your solution will be hugely beneficial to you.

Plan to be successful

Knowing what your goals are, having the flexibility to learn as you build and grow your data analysis solution and having access to the right people with the right talent to get you to the insights you are looking for will improve your chances of completing your analysis project successfully.  Starting small and building on little successes as you go is a smart way to get your organization on the path to a well planned data analysis solution. Don’t be afraid to try a data analysis project because you’re afraid of wasting money on something that is statistically going to fail. If you are strategic in your approach, utilize resources as you need them and know what you’re trying to achieve from the onset, you’ll be on the right track and you’ll have paved the path for better outcomes and more successful projects down the road.

Summary

Based on current numbers, your data analysis project is likely going to fail, but with the right planning you can greatly improve your odds. By having an idea of what you want to get out of your data, being flexible in your approach, and by utilizing the right resources, you increase your chances of success.  Additionally, when you partner with experienced data analysis professionals you get to focus on what the data shows you and what you need to do next, getting you to positive results more quickly.