Setting Goals for Analytics Leaders

For the last couple of years, I’ve shared a post recommending a system for setting goals and achieving them. However, a few conversations with insight leaders have reminded me that advice remains generic. What about which goals to set? As this blog aims to support customer insight leaders, I want to also offer more specific advice. Given the context of common challenges and potential future trends, which goals would I advise? Well, far be it from me to second guess your priorities and specific context, but I hope these thoughts help. They are intended to simply act as a checklist, to prompt your own thinking. Topics for your specific goals Business priorities My first encouragement is to be guided by your context. Do not start with fashionable technology trends or the most passionate speaker at that conference. What does your business need? What do your customers want? Start by taking some time out to consider the most important challenges for your business. Here are a few potential issues to seed your review:

• Do you need to address customer experience irritants?
• Do you need to improve your marketing effectiveness to reach more new customers?
• Do you need to retain your existing customers better and even deepen usage/trust?
• Do you need to differentiate from the competition by innovating products or services?
• Do you need to reimagine how technology could reinvent how to meet your customers’ “job to get done”?
• Do you need to clarify a customer strategy for your organization?

Identifying the highest business priority that customer insight can guide is a great place to start. As I advised when sharing experience of how to influence "top table" executive committees, start with their need. Even if other improvements are possible and more interesting, start with how analytics or research can help the wider business. That will build the firmest foundation for influence. Having said that, many of today’s insight leaders have to build a capability, whether it be improved data usage, analytics or data science. So, which goals make sense for them? Capability building goals Data Management First, because almost no business has yet achieved full compliance, I must stress the importance of GDPR compliance. To help identify which specific goals you need to set regarding GDPR, a review of these previous posts should help identify gaps:

• Aspects to consider when preparing for GDPR (part 1)
• Aspects to consider when preparing for GDPR (part 2)
• How to embed GDPR in your business by communication
• A set of helpful GDPR resources to identify potential gaps
• Data quality implications of GDPR
• Guidance for improving your data quality reporting

See also: How to Keep Goals From Blowing Up   For now, I would suggest that goals with regard to using more big data (unless it is to improve your data quality) should be postponed. Until you clearly understand how you will achieve compliance with GDPR and can evidence a plan, that should be your data priority, not least because, once you fully understand your responsibilities, less may be more, for data usage. Data science capability The single most popular capability that today’s leaders are piloting is data science (including AI). That makes sense, as even the more advanced leaders are still exploring potential applications. Some new products and services have been developed. Existing processes have been refined and automated. But the business case for most organization is still far from proven. My personal view is that is most companies do not yet need data scientists; rather, better analytics would add more value. However, as coding languages become simpler and the most popular algorithms prove their relevance, that may change. So, even if you are not a tech disruptor, if you can secure sufficient budget, now is a good time to experiment. I would simply caution to set a goal regarding proving business applications and ROI, at low cost and low risk for now. Here are some posts to help guide where you might focus a goal to pilot a data science capability in your business:

• How to approach betting on a data science capability?
• How to get the best out of your data science team?

Analytics capability For many businesses, the capability with greatest potential to change how they operate is analytics. Unfortunately, the term has too often been misunderstood and either watered down or hijacked. By watered down, I mean conflating business intelligence (BI) with analytics. Because of the widespread, vague use of the term, I come across many businesses that believe they have an analytics team. Upon closer inspection, I find this team are only skilled in producing BI reporting. If educating your business on the difference between analytics and BI is one of your challenges, consider presenting a continuum. I’ve used a number of infographics, over the years, to show a maturity journey from simple data reporting through to data science. This can help show the difference compared with descriptive, predictive or prescriptive analytics. Do you need to set a goal to expand your analytics capability toolkit? With so much hyperbole surrounding data science, it is all too often allowed to subsume all analytics. I’ve met a number of leaders who assume any statistical modeling is now part of a data science capability. For one goal, I’d suggest identifying where your analytics capability can most rapidly improve ROI. These posts may help guide your goal setting:

• Experience of where analytics delivers biggest bang for buck
• Tips for getting the most out of your analytics team
• Advice on why to design applied analytics projects

People capability People are key. Often, the biggest predictor of impact is not the sophistication or even the relevance of analytics work, but the analyst. All too often, I find that analysts lack any training beyond technical skills. It is as if they are simply to be programmed with coding/software/stats skills and left to get on with it. When I see what a difference strong softer skills can make to individual analysts and teams, this is such a missed opportunity. So, I encourage you, consider the people skills that you should target with relevant goals. For developing individual analysts, I suggest considering:

• Need for softer skills in analysts
• A nine-step model for softer skills in analysis process
• Storytelling skills for analysts

For designing and developing better teams, I suggest:

• Need for and use of competency framework
• Internal events for sharing analytics knowledge

Leadership capability Last, but definitely not least, don’t neglect yourself as a leader. Rather than letting a personal development plan be a burden or afterthought, how about seeing it as a chance to invest in yourself? I’ve written previously about the need for improved leadership capability among insight leaders. More organizations are waking up to this development need. See also: 3 Steps to Succeed at Open Innovation   Two regular conversations remind me of the continued importance of setting goals in this area. First, I meet (and sometimes coach) leaders who have technical expertise but lack experience operating at a senior level. Second, busy insight leaders tell me they cannot spare the time for coaching or mentoring despite obvious challenges. If you recognize that you’d benefit from more investment in your leadership development this year, here are some posts on leadership development that should prompt your thinking, to craft a goal that is right for you:

• Professional development resources for insight leaders (different types)
• Another list of resources
• How to improve the reputation of your team
• Why coaching can help you develop as a leader
• Which do you need, a coach or a mentor?

What will your specific goals be? Did you find those suggestions useful? Which were relevant to the goals you need to set? What specific goals are your top priorities?