If you ask any elite athlete to describe what success looks like to them, they’ll paint you a picture of success that’s very concrete. They’ll not only describe a very specific picture of success, but also who they’ll have to beat, the performance results they have to achieve, and how they’re going to win. If you ask a business leader what success looks like for their big data and analytics program, you probably won’t get as concrete an answer. And it shows in the results.
According to a recent Harvard Business Review article, less than half of the analytics programs they surveyed met their initial return-on-investment (ROI) goals.(1) Other studies have estimated this number to be less than 40%. Consider also that in 2015 alone companies spent $122 billion on big data and analytics.(2) Clearly lack of investment isn’t the issue.
Just like an elite athlete, winning at big data & analytics requires a concrete picture of success. Unfortunately, business leaders frequently view success only in technical terms and fail to appreciate that changes will be required not only in their businesses but perhaps even themselves. The reality is that you may get answers from big data & analytics that point to solutions that are either counter to past experiences or even a leader’s personal interests. If left unmanaged, this particular dynamic will undermine the results of your big data & analytics program.
As any organizational change expert will tell you, successful change requires a compelling picture of the future. A view of how life will be better than it is today. A reason to make the change. So how do you create a picture like this for your big data & analytics program? It requires a leadership discussion about what will be different, what will need to change, and what it might be worth to your organization.
The process of creating your picture doesn’t have to be complicated. It will, however, require a bit of courage.
According to a recent Harvard Business Review article, less than half of the analytics programs they surveyed met their initial return-on-investment (ROI) goals.
Business Outcomes – How you want analytics to change your business
There is a really important concept to understand about big data & analytics. Not everything you try will work out. Failure is an important part of learning. That’s why success should be defined in terms of program level outcomes rather than individual projects. For example you may want to use analytics to improve gross margins through better pricing. You don’t have to know exactly how to accomplish this goal in order to decide its work doing.
This is where many get stuck. Leaders often want to evaluate the chances of success before they agree to the goal. Unfortunately that’s chicken and egg thinking. If you knew the answer, then you wouldn’t need analytics. Instead leaders should determine if the goal is worth pursuing, evaluate progress, and adjust as necessary. That is the essence of leadership, setting a course in the face of adversity and uncertainty. This is also where you’ll find big data and analytics results.
To help jump start the creation of your own target business outcomes list, I’ve put together this starter list. It includes a few generic examples, but your own list should be specific to your industry and aligned with your organization’s strategic goals.
- Return on investment of xx% over x years for analytic related investments
- An agreed upon list of target business outcomes to be enabled by analytics. Some examples include:
- Grow by xx% over x years through better segmentation of markets and more targeted campaigns
- Improved customer retention of xx% by targeting at risk customers
- Improved business performance of xx% by embedding metrics to monitor operations and forecast performance
- Improved gross margins by xx% over x years by dynamically pricing products and services
- XX% of analytic projects produce qualitative impacts
- XX% of analytic projects produce hard dollar impacts
Winning at big data & analytics requires a concrete picture of success
Organizational Outcomes – How the Organization Works Together to Use Analytics
When you consider the business outcomes above, it’s easier to see all the different skill sets that success requires. Big data & analytics is a ‘horizontal’ function, meaning it cuts across traditional business functions. Results require a highly coordinated mix of technical, analytic, leadership, and follow through activities. Coordinating all these activities can also be one of the major barriers to achieving big data & analytics results.
This is why its important to create a concrete picture of how your organization will be working together. To help paint your picture, I’ve included a starter list of specific organizational success criteria.
Results Focus is the ability to identify high value business problems, make fact-based decisions, and follow through to achieve tangible business results. Success in this area looks like:
- Effort is expended on business problems with the greatest value potential
- Analytics are used to develop decision options that provide a fact based case for action
- Benefits realization plans are tracked and results are measured
- Business leaders champion the business change needed to deliver results
Analytic Agility includes the delivery of credible analysis, acceleration of business decision making, and consistent identification of new business insights
- Analytic resources find actionable business insights independently
- Business analysis is primarily prescriptive (showing the implications of future decision options)
- Analysis results in decision makers seeing business problems differently and making decisions faster
- Analysts are the trusted analytic authority for the organization
Technology Delivery delivers flexible views of data with tools to access and manipulate those views. Success in this area looks like:
- Delivery of solutions happens in a transparent and predictable manner
- Technology investments generate leverage and synergy (e.g. reusable data views and tools)
- Business, analytic, and technology teams work seamlessly together to implement creative solutions
- Performance and reliability of the analytic platform meets business needs
- Technology investments are tightly linked to specific business-analytic opportunities
Data Management involves transforming data into assets that create value for the organization. Success in this area looks like:
- The organization trusts that data can be used for decision making
- Standard definitions of key elements are agreed to and used for all analysis
- Standardized quality metrics are produced and used to guide investments in enhancing data
- Data related investments are tightly linked to specific business-analytic opportunities
- Effective security is in place to manage access to information
Having success criteria defined up-front will not only help define what business success looks like, but also clarify who should be playing what role to help to make it happen. This process will also allow for discussion about specific concerns leaders may have so they can be addressed early. Quantifying these opportunity areas will also guide your big data & analytic project priorities as well as your technology investments.
What other criteria have you used to define success for your big data & analytics program?
I’m always publishing articles about how to get more value from data and analytics. If you found this article helpful, you can find more at www.ipowerconsult.com or you can sign up to get articles delivered directly to your inbox. (Click Here to Sign Up)
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