Business leaders are driving up to 80% greater returns with analytics

We have all seen IT projects that implement new and shiny tech toys. We’ve also seen well-meaning managers driving their analytics projects without executive buy-in. In these situations, how can businesses achieve maximum ROI from analytics projects?

If you are a board member or executive, do you understand the business benefits of data and analytics? As an Executive, do you actively support your data and analytics projects? If not, there is a risk you may not be maximising shareholder returns.

If you’re a manager currently leading an analytics project without active executive support, there is a real risk of low, or even negative, ROI from the project.

 

Executive support as a driver for financial return

Academic evidence demonstrates that executive support is a key determinant in generating superior financial returns from analytics. A Pennsylvania State University study showed that organisations can increase their Return on Assets (ROA) by 8% by increasing their data and analytics maturity. Executive support was a vital antecedent (1).

An Australian study supports this result and found that ‘Leader’ organisations with advanced analytics maturity can outperform the ‘Laggards’, generating between 60%-80% more profits. The study found that executive-level sponsors who champion analytics are a crucial component of generating the higher profits (2, 3). 

83% of organisations defined as ‘Leaders’ in Analytics have C-suite or Director-level leaders heading their analytics operations compared with 22% of the ‘Laggards’. Dedicated C-level executives, such as a Chief Data Officers or Chief Analytics Officers, act as sponsors to drive analytics efforts across the organisation. These sponsors are involved in all communications and governing bodies across the organisation to champion analytics-related causes and drive an analytics-driven culture. The C-level executives also drive a centrally-led analytics organisational structure where 84% of ‘Leaders’ have a centralised  structure compared with only 28% of Laggards (2,3).

Another key differentiator of Analytics ‘Leaders’ over ‘Laggards’ is a clearly-defined analytics strategy and well-developed roadmap that aligns with their overall business strategy. The C-suite has a clear commitment to analytics and fosters a culture of data and insights-driven decision-making. 85% of ‘Leaders’ have clearly defined key performance indicators, which are mapped to analytics deliverables. These metrics will be understood and monitored across the organisation whereas only 20% of the remaining organisations claim the same (2,3).

 

Analytics-driven culture is critical for superior financial returns

Along with sponsoring and supporting analytics, executives also need to nurture a culture that supports the use of analytics. This kind of culture ‘carries the logic of how and why things happen and positively influences decision makers to incorporate analytics-driven insights into their decision making’ (1). While the roles of analysts and data scientists support analytics and have advanced capabilities to drive an analytics-driven culture, the culture needs to be nurtured from the top down. 75% of ‘Analytics Leaders’ have a strong analytics-driven culture compared with 19% of other organisations. ‘Leaders’ will differentiate themselves with a culture of experimentation as they facilitate analytics projects and a safe-to-fail attitude that is embedded at every level of the organisation (2, 3).

Analytics capabilities allow leaders to increase technical skills & profits

Executive leaders must ensure the firm has top talent to boost profits from analytics. The better the analytics skills, the better the insights and the more likely the organisation’s leaders will incorporate the analytics-driven insights into their decisions. ‘Leading’ organisations outperform ‘Laggards’ regarding hiring, managing and developing analytics talent. Analytics employees in leading organisations have well-defined roles and responsibilities with clear expectations of their output. Employees are assigned to projects that match their skills and external contractors are accounted for to fill any gaps (2,3).

Analytics ‘Leaders’ not only offer deep technical training to their analytics talent, which is tailored to their specific business needs, but also company-wide analytics training to their employees to increase the understanding and appreciation of the value of analytics across the organisation and build an analytics-driven culture (2,3).

 

Technology, data & tools generate value

Analytics that generate real business value need large volumes of data, so organisations need the appropriate IT infrastructure to enable successful analytics. 81% of ‘Analytics Leaders’ have data infrastructure, including well-maintained enterprise data warehouses and use advanced technologies (like AI, NLP and ML etc), as opposed to 27% of other organisations (2,3).

Surprisingly, ‘Leaders’ and ‘Laggards’ make similar investments in Artificial Intelligence, however, ‘Leaders’ reap greater rewards and create 4-5 times more value from AI pilot deployments, which are deployed in half the time compared to ‘Laggards’. Therefore, you can’t expect superior financial results just by investing in your AI program, you need to be aware of all of the other influencing factors such as active executive support and an analytics-driven culture of experimentation  as described above. (2,3). 

‘Leaders’ regularly evaluate their technology platforms to ensure they are meeting the evolving needs of the business and they don’t just invest in the latest tech and tools for the sake of it. Research shows that organisations that invest in the latest technology and tools without strategic leadership actually have lower profits than organisations with executives that support and guide their technology investments (2,3).

In my experience, this is due to a combination of reasons:

• The lack of executive understanding and support for data and analytics fails to create and nurture a data and insights-driven culture.
• Without an insights-driven culture and the business benefits that flow from this, investment in top data and analytics talent does not happen. While when analytics experts are hired, their full potential is not realised, it is because leaders do not know how to harness their talent.
• In the absence of top talent, or if underutilised, the potential of an organisation’s analytics tools and technology can’t be fully realised.
• Without top analytics talent, data-driven insights may not be generated and hence optimal business decisions may not be made consistently and hence profits may be affected.

It is no surprise that the top 5 most valuable companies in the world by market cap are all data- driven organisations (Apple, Microsoft, Amazon, Google and Facebook). They all have the following attributes:

• Founders, board members and executives make decisions based on insights derived from analytics, and they nurture insights-driven cultures.
• Each of these companies has vast amounts of proprietary data and they vigorously protect it.
• They invest in world-class data and analytics talent to generate deep insights and create great products.
• Experimentation is in the organisation’s DNA. They rapidly test ideas and make decisions using insights derived from analytics on their vast data assets.
• They invest billions of dollars in the latest technologies (including AI) to enable successful core business operations and the development of new business models.

I am frequently asked, “What should we do?” and “Where should we start?” and “Should we invest in this new technology” and “What sort of person should I hire to lead our data effort?” These are all questions answered in a Data Strategy. I formerly worked at The Boston Consulting Group, so I admit to having a strategy bias. But here at Aginic, we do ‘Data Strategy’ differently. The best way to develop a Data Strategy is to learn (and advise) by doing! 

 

What business problems are you trying to solve?

We first ask the question, ‘What are your highest priority business problems?’All people, process and technology advice and decisions flow from this prioritisation. Which business problems, if solved, will generate the most returns? But how much investment is required to generate those returns? An easy way to facilitate that process is by plotting initiatives on a matrix with potential value to the organisation and feasibility to execute successfully on the x and y axes.

Once this ‘Business Strategy’ prioritisation has taken place, we select a valuable and feasible problem on the matrix, and create an experiment run over ~2 weeks in an agile way of working with a cross-functional team solving this problem essentially on the ‘back of an envelope’. We use the organisation’s data, talent and technology and identify the enablers and barriers. What is already fit for purpose but what needs to be changed or built?

Do the executives and leaders of the business understand the value of analytics in making insights-driven decisions? Does your organisation’s culture enable rapid experimentation and support the potential for failing? Is your data quality an issue? Does your technology enable easy pipelining and analytics? Do you have the right capabilities to frame the problem and experiment, to ingest, wrangle and analyse the data? 

The results of this experiment will become the primary source of the ‘Data Strategy’ and guide our strategic advice. Without running an experiment, it is very difficult, nearly impossible, to effectively advise organisations on ‘What should we do next?’. How do you truly know how hard it is to get the data, and determine what quality is the data in? How do you know how capable your people are in both data engineering and analytics? How else can you actually assess your tech stack and analytics tools?

In parallel to the experiment, we also target a wide stakeholder group within the client organisation to complete our proprietary ‘Data and Analytics Maturity Survey’ to provide insights into the organisation’s current levels of maturity. We then complete 1:1 interviews with key executive stakeholders and the relevant subject matter experts. Finally, we run workshops with key business groups and the IT, data and analytics teams. From the aforementioned inputs, we assess the current state, we develop an organisational view of the future target state, and also create a roadmap to achieve the target state.

Based on our previous strategic engagements, some of the most common recommendations and actions are:

• Spend time with senior leadership discussing the value of maturing their data & analytics capabilities
• Structure and run Data Science experiments to prove value from analytics to solve business problems with data
• Boost capability in an organisation’s agile way of working to ensure they can deliver value rapidly to the business
• Educate the analytics talent (technical analytics) and also the business personnel (data literacy) to analyse their data assets to derive accurate insights to enable better decision making
• Highlight capability gaps that need to be filled in their analytics talent
• Advise against large “big bang” investments in technology and talent
• If required, build simple cloud-based data and analytics platforms enabling the ‘business’ to solve their priority business problems with democratised access to the data, and analytics tools.

 

Conclusion

Executive support, strategy, culture, capabilities, and the right data infrastructure and tools all contribute to the success of analytics within organisations. Having these elements in place, as research and experience shows, can increase your profits by as much as 80%.

Aginic has former consultants from Tier 1 strategy consulting and Big Four firms, and also deep data analytics and engineering expertise, so we can develop a Data Strategy to enable firms to maximise the return on their analytics investment. Not only can we deliver to your analytics objectives, we can help you figure out how to become a ‘Leader’, not a ‘Laggard’. 

 

References

• Germann F & Lillien G. Performance implications of deploying marketing analytics (2013). Intern. J. of Research in Marketing. 30:114-128. https://pennstate.pure.elsevier.com/en/publications/performance-implications-of-deploying-marketing-analytics
• The untapped value of analytics. Melbourne Business School & A.T.Kearney (2018). www.kearney.com/documents/20152/1452161/The+Untapped+Value+of+Analytics.pdf/f08ab1a3-644d-127f-7329-062e22c957bc?t=1553233480334.
• The Analytics Impact Index (2020): www.kearney.com/analytics/article/?/a/the-impact-of-analytics-in-2020