Business analytics is a booming industry, expecting significant growth in 2018 and looking to break through the US$25B mark. Many companies are investing heavily in analytics, with the promise of stronger revenue, improved compliance, greater efficiency, and increased profitability.
However, a good number of businesses do not see the desired outcome. The analytics don’t really work. They do not provide any significant business value.
Why?
There are a number of contributing factors in analytics failure, including poor data quality, lack of executive sponsorship, an immutable business culture, and a shortage of technical expertise.
However, the number one reason that analytics fail to bring a great result is this:
When you ask the wrong questions, you always get the wrong answers.
There is a lot of hubbub in B.I. circles about a dearth of capable tools and great talent. However, the tools are becoming more intuitive, and citizen data scientists more prolific, yet the results are often sub-optimal. Many companies that have employed terrific analytics talent and utilized powerful data tools have failed miserably.
Analytics is all about a business result. It is never about pretty charts or clever science. Sure, you won’t get a business result without charts or science, but these are a means, not and end. Data analysis and visualization must lead to accurate insights that generate meaningful action.
Here is my simple formula for success:
Business Acumen + Effective Analytics = Intelligent Insights
Let me give you a real-world example to illustrate my point. My previous employer, a wealth management business, was spending a large chunk of change every month on marketing events at hospitals. The existing analytics showed that one particular hospital was producing a high number of leads, and appeared to be a profitable lead source. As a result of this data, InFusion360 continued to invest considerable time and money in events at this hospital. The question was, “Is this hospital producing a large number of leads?” The answer was, “Yes.”
However, the analytics were only presenting part of the picture. Using Einstein Analytics, I built this simple chart (details obscured) that included not only the number of leads produced per hospital, but also the quality of leads:
The Y axis shows lead conversion %; the X axis shows the lead source; the size of the bubble shows the number of leads produced per source.
When this chart was displayed at a leadership team meeting, it gave powerful insight into the marketing aspect of the business. Marketing time and dollars were immediately shifted from the hospital producing a high-volume of low-quality leads, to a hospital that had produced a small number of leads with a much higher conversion rate. Did this decision yield a business result? You could say so — their sales pipeline quadrupled in 18 months.
Again:
Business Acumen + Effective Analytics = Intelligent Insights
If you are going to build effective, actionable analytics, you must establish the key questions that you answer using data. This is like a guided process of self-discovery. Some questions you might ask yourself as a part of this process are:
- Why does our business exist?
- What are our current pain points?
- What are our business goals for the next 12 months and 3 years?
- What top metrics are currently used in making key business decisions?
- What numbers come up regularly for discussion at leadership meetings?
As you consider these strategic questions, you should be able to come up with some meaningful questions that you want your data to answer for you. Examples include:
- How does our sales pipeline look for this month and next month?
- Are our team members hitting sales targets?
- Are we meeting compliance and regulatory requirements?
- Are we generating enough leads?
- How effective are we at converting leads?
- What are our team members spending their time on?
- What is our ROI on marketing $ spent?
- How well are we building client relationships?
- What is the historical trending of our sales and revenue?
- What overall shape is our business in?
Once you have built analytics to process and visualise your data, you will get a clearer picture of that data, and a clearer understanding of your business. This will enable you to ask further questions that will empower decision making and facilitate business insight.
On a closing, practical note, once you have the right questions, how do you ensure that you get a great business result from your investment in analytics?
- Focus more on the result than the process. Technology is simply a tool. Talent is just a resource. The right tool and great talent will help you get a result, but they certainly don’t guarantee a result.
- Start small and maintain a sharp focus. Digital transformation initiatives too often they try to transform a whole business all at once. This kind of shotgun approach, especially within a large organization, is a prescription for failure. Digital transformation starts with one department or business unit, executes with focus and resolve, gets tangible results, and then uses that momentum to spread to other divisions.
- Begin with the business in mind, and maintain this focus all the way through the analytics build process. The analytics engineering team (whether internal or internal) must maintain constant collaboration with the relevant managers, executives and team members right throughout the analytics project. It is especially helpful to have an employee or consultant who is fluent in both the language of business and analytics.
- Successful analytics projects are never turn-key builds. Rather, they are built on a philosophy of continuous development and improvement. The more powerful and effective your analytics becomes, the great the result it delivers, and the more you will want to develop it.
- Consider utilizing an agile external team of analytics engineers that understand both business and technology. “…true digital transformation will almost always fail if executed from within the organization. Why? Because the change is so disruptive that the existing organization chokes it off. For GE Digital to have succeeded, it needed to be separate from GE. Making GE Digital its own business unit was a step in the right direction, but it also inherited the roles and responsibilities of GE Software. Digital transformation initiatives don’t need thousands of people. They need a small team with very little time and very little money.” (“Why GE Digital Failed” by Moazed)
Building serious business analytics and intelligence into your company is never cheap, easy or quick. However, it can be one of the best investments you have ever made, yielding improved efficiency and increased profitability, and resulting in sustained growth. Or, it can be a very expensive and painful failed experiment. What makes the difference? Asking the right questions.
A Most Unlikely Trailblazer 