Key Learnings for Analytics Managers

One of the major hurdles businesses face today is the lack of leaders who can navigate a data driven path to success. Experts have identified that there is a lack of managers today who truly understand how data can be used to create value (Davenport, 5 Essential Principles for Understanding Analytics, 2015). The analysis above contains information valuable for understanding activities linked to an analytics initiative. Below, we discuss some of the key learnings that can be derived from analysis to benefit analytics managers and assist them in meeting their analytics goals by with understanding and influencing ROI.

Financial ROI of Analytics Projects

Literature review of ROI in the analytics field reveals that the percentage of respondents that take part in surveys and admit to positive ROI for analytics initiatives range from 2% (ZS Associates, 2016) to 27% (Capgemini & Informatica, 2016). In this study, the percentage of respondents who stated that their organizational financial ROI was positive was even higher at 46%. Managers should naturally question this wide range and how can this information be useful. It is difficult to determine causality from such studies as there are many moving parts of an organization which are not necessarily part of the equation. The study commissioned by ZS Associates was targeted towards analytics practitioners and executives in the Sales and Marketing vertical. In comparison, this research did not limit participation to a specific business unit. The Capgemini study focused only on companies with greater than 1000 employees with an average employee base of 23,000. In this research, almost 50% of the participants belonged to organizations with less than 1000 employees. There is no right or wrong answer as dissimilar research studies targeting different types of employers and industries are unlikely to lead to the same conclusion. Managers should

take numbers published in research studies a grain of salt and continue to strive for increasing financial ROI.

What about Non-Financial ROI?

The study found that only 2 out of 13 participants (15%) who selected cost reduction as a benefit ranked it as being most importance. This is supported in the literature review that finds senior management and executives are looking at analytics investment as more than just an attempt to reduce costs or increase revenue. Senior leaders in early adopter analytics firms are more interested in either finding an efficient alternative for current business functions or finding new business capabilities (Davenport & Dyche, 2013).

This raises the question whether managers should also consider non-financial ROI and not just concern themselves with reducing costs or increasing revenue which relate directly to profits? How do we measure intangible benefits such as increasing customer satisfaction or automation of a business process which previously required manual intervention? Can these be easily quantified in dollars amounts so they can be incorporated into financial calculations? Do customized metrics need to be created to measure them? These are questions analytics managers must deliberate to get a deep understanding of the overall ROI. Managers are advised to avoid fixating on financial ROI and encouraged to gauge the ROI of analytics through a holistic lens that looks beyond dollar value.

Boost the Organizational Importance of your Analytics Initiative

Each participant who identified analytics as being extremely important relative to other projects with the organization, also reported that ROI was positive. Managers of analytics initiatives must gather as much support as possible from the senior leadership to increase the importance associated

to analytics activities. Surely, if a project is considered extremely important relative to others, chances are leadership team will strive align the analytics project with business strategy. Managers who cannot impel the importance of their analytics initiative will have low support from senior management and a lower tendency to meet their financial goals.

Setting Goals Almost Always Drives Success

The survey found defining objectives before implementing analytics solutions was effective. The percentage of participants who reported a positive ROI when an objective was identified within top three on the importance scale is consistently over 90% with the exception of those looking to monitor and process streaming data. This finding is a reminder to managers that defining goals before embarking on any project is vital for success.

Focus on the Fundamentals - Better Decision Making

Only 16% of participants identified faster and accurate decision making as the most important benefit. This implies a fundamental gap in understanding the basis for big data analytics. Academic literature and technology research consistently explains that the radical change that big data analytics brings is the ability to make better decisions based on evidence (McAfee et al., 2012). Not primarily using data for accurate decision making is not the right approach; this is supported by the data analysis which found that 80% of participants that chose faster and accurate decision making within the top three on the importance scale saw positive ROI. In contrast, only 33% of participants who placed faster and accurate decision making at the lower end of the importance scale saw positive ROI. This is a critical lesson for managers; the fundamental building block of analytics is to improve decision making by deriving insights from data. Using this as the starting

point, managers can make better decisions that subsequently derive additional benefits such as increase in productivity or innovation.

Getting Support for Business Challenges

Lack of talent and data governance issues pose the biggest hurdle for analytics managers as they strive for financial success. Managers can use this data to build a case when approaching senior leadership for additional resources and argue that the probability of a positive financial ROI is low unless the challenges are addressed

Encourage Simultaneous Analytics Projects

Larger number of simultaneous projects means higher the potential for positive ROI. At least that is what the survey results found. In this case the number of projects threshold was 5. We can speculate about the reasons for why ROI was positive 8 out of 9 times for those organizations that had more than 5 concurrent analytics projects. Firstly, if an organization has multiple analytics projects going on, there is the opportunity for managers to discuss best practices, pitfalls and collaborate to achieve success. Alternatively, the first project may have been so successful that the leadership was quick to approve additional projects. Secondly, and more importantly, to be able to run multiple projects at the same time, there must be strong leadership backing for these initiatives to have approval in the first place. Practice does lead to perfection, or at least very close to it. Managers should promote additional analytics projects within the organization.

Methodology for Measuring ROI

The survey did not find glaring evidence to support or deny that a new methodology is required to influence ROI positively. However, none of the participants were extremely satisfied with their current methodology. This warrants investigation to better understand what is keeping managers

from being extremely satisfied with their current methodology. Managers can take this as a lesson to review the ROI measuring methodology for improvement.

Are Metrics Necessary?

Should managers create new metrics specifically for big data analytics or should they continue to use existing ones? The data analysis does not give us enough information to conclude. Moreover, the answer to that question depends on exactly what the analytics manager is trying to measure. The key point for managers to take away is that metrics are important for measuring progress and aid in calculating financial ROI and attracting executive support. The literature review found that early adopters of analytics solutions are executing projects with an informal approach and overlooking metrics critical for measuring progress (Shim et al., 2015). More than 70% of analytics projects fail because managers fail to provide evidence in the form of metrics to senior management and thus lose their support (Bertolucci, 2013. Managers should follow a methodological approach when executing analytics projects and use relevant metrics to track progress. Experimenting with metrics is expected and necessary on the road to success.