The 9 Organizational Practices for Maximizing Analytics Potential
Data as a commodity has increasingly become more available and sought after, as many organizations have realized the vast competitive advantage that can be unlocked through becoming data-driven. As a result, many agile and nimble organizations have fundamentally transformed themselves to utilize analytics on all levels of operations and decision-making, showing an example to larger organizations and creating an urgency for their digitalization processes.
However, shifting the practices and culture of large organizations towards analytics is no easy feat, and it most certainly does not happen overnight. Companies wishing to make the leap need to take a holistic approach to ensure that they don’t end up simply making small incremental improvements that bring little competitive advantage. To better understand where organizations should focus on and what practices they should adopt to maximize their analytics capabilities, I interviewed analytics executives from 16 different companies for my master’s thesis to better understand what they thought to be vital in improving the analytics capabilities of their organizations.
The Analytics Capability Framework
The core finding from examining the interviews was that business executives wanting to improve the analytics capabilities of their organizations need to approach the subject on three dimensions — processes, leadership and structure. Taking an approach where one or two dimensions are ignored can bring severe hindrances in analytics advancement. My research indicates that organizations should carry out analytics improvement in an all-encompassing fashion to ensure that all “features” of the organization are on the same page in terms of analytics. The chapters below will outline the nine key implications for business executives based on the research I did regarding each of the dimensions.
Implication 1: Organizations should seek communicative analytics employees
Almost all analytics executives agreed that they found “softer” attributes such as personality and motivation significantly more beneficial to focus on in recruiting rather than hard skills. Good communication skills were the most sought after skill, as good communicators were able to aid less data literate employees working under other business units in understanding analytics and its implications.
Implication 2: Analytics team members need to have diversity in their work
A common pitfall for analytics teams was “locking in” each member to specific tasks — this caused team members to lack an understanding of the big picture and slowed down individual development.
Implication 3: Other business units have to feel included in analytics
Another common pitfall for executives was being “pushy” with analytics towards other business units. Many noted that they had achieved success once they began focusing on collaboration with business units instead of forcing them to adopt new practices.
Implication 4: Executives need to be ready to scale (and kill) projects quickly
While almost all executives had difficulties prioritizing analytics projects, some noted that using iterative prioritization had brought fruitful results. In practice, this meant rapidly testing multiple projects and only continuing to invest in those projects that were proven to be successful and scaleable.
Implication 5: Analytics employees should be led by removing obstacles instead of giving strong guidance
As analytics professionals are usually driven and intelligent, micromanaging them rarely brings good results. Instead, managers should use their position to remove obstacles and make prioritization and strategy as simple as possible for subordinates.
Implication 6: Technically competent leaders allow collegial relationships to flourish
Executives noted that analytics units should be led by managers who are technically proficient in using data. This allows leadership to become effortless, as subordinates and managers have an easier time forming trust between one another.
Implication 7: Competitive advantage can be obtained by allocating time for innovation
Many executives argued that to achieve truly disruptive results, analytics teams need to set aside time to think and produce innovative solutions that might profoundly affect company performance. One team’s method for executing this in practice was setting aside one day each week for team members, which would be exempt from normal work tasks and instead was spent actively innovating.
Implication 8: Projects shouldn’t be selected based on data availability or method accessibility
Multiple interviewed organizations had run into the issue of being “data-driven” instead of “business-driven”, meaning that often proposed projects that might have had a significant business impact were discarded simply because executing them would require extra effort either through obtaining new data or learning unfamiliar methods. Organizations had achieved improved results by encouraging open-minded culture.
Implication 9: Analytics units should not be located under IT
Analytics units that were working under IT in the organizational structures voiced deep dissatisfaction due to ideological mismatch. Executives felt that IT departments generally fostered a culture of cutting costs, which caused analytics units to have reduced innovation output and fewer ambitious projects that could potentially have a profound effect on the company.
Finally, it is worth noting that while these nine implications may seem enticing and intuitive at first, executives still need to keep in mind that “silver bullets” rarely exist for organizational design. Thus, it is vital that decisions made by executives should reflect the organization where they are being made.
If you are interested in the topic and would like to read deeper into the rationale behind these implications on my thesis, please reach out to me via email at jaakko.hypponen@gmail.com.