Advanced analytics is fast becoming a core enterprise competency. Organizations slow to develop its risk falling behind competitors. Companies need quick and reliable insight into the current and future performance of their processes as well as the evolving needs of customers. Advanced analytics is no longer the purview of companies like Google or Amazon. It’s a critical competitive differentiator.
The foundation of advanced analytics is the integrity, diversity and accessibility of big data. The Hackett Group research shows that top performing finance functions are more likely to construct a single source of truth. They have better management and governance policies. Increasingly, they are also leveraging new data management platforms like data lakes and data marts.
Analytics is worthless without data. Data is worthless without analytics. So, they combine elements of performance analysis and forecasting (Finance’s realm), with the implementation of enterprise-wide platforms and applications (IT’s realm). Will IT or Finance become the owners of the analytics capability? Perhaps there’s a need for an entirely different service delivery model.
Traditionally, finance analyzed financial data. The functions/business analyzed operations and customer data. That is no longer the case. Finance is looking outside its four walls and pulling in business/operational information.
By integrating the planning and forecasting processes, finance can provide management with greater insight into enterprise performance -- today and going forward. As it comes to better understand how the business works and thus offer effective decision support, business leaders and management can make smarter decisions.
The ultimate owner of the analytics process depends on the delivery model companies use to develop and distribute analyses. Finance is the traditional, and predominant, analytics engine of the company. And since it has expanded its scope, it develops and delivers analytics/reporting to internal and external stakeholders.
However, in many cases, business units and functions also have their own analysts. The advantage of maintaining a hybrid structure is that analytics happens closer to the user/market. The disadvantages are duplication of efforts and the use of disparate systems that don't talk to each other.
The Benefits of Centralization
There are clear benefits to this centralization approach:
- It leverages the use of a single analytics solution across the enterprise
- It standardizes reporting so everyone is on the same page.
- It eliminates duplication of effort in different parts of the organization
- It reduces the need for hiring multiple analysts, essentially creating an analytics capability economy of scale.
- It helps the COE staff to share knowledge more effectively
- It has a clear, enterprise-wide view
- It can provide oversight for many companies that are still in the exploration and piloting phase and often initiate discrete projects related to a specific business problem, without coordination and cross-fertilization.
In our 2018 Key Issues Study we found the finance executives expect the amount of analytics work performed by the business to drop by half in the next 2-3 years, while more of the workload will be handled by COEs. Seventy-seven percent of finance executives in a COE poll we conducted last year either already have or expect to have analytics COEs in the next 2-3 years.
So, for now at least, there’s a clear trend toward the concentration of analytics in a single entity that reports into finance or IT. Is that the future?
The Pendulum Swings
After taking pockets of analytics activity and pulling them into a centralized entity, ironically the next phase appears to be a reversion to a decentralized model. As data platforms and robust MDM take hold, it’s possible for anyone (with some restrictions of course) to access the company’s vetted data set. At the same time, the volume of data and analytics activity is becoming overwhelming. It’s too much for one entity.
Hence the growing popularity of self-service analytics tools. Top performing finance organizations are 18% more likely to use them. COEs are pushing some aspects of the process to the users, empowering them to answer their own questions without creating a requests backlog. Just what and how much to push out is still unclear. There are different models for what’s retained in the COE and what’s handled by the consumers of the insight.
The democratization of analytics activities raises more questions about process ownership. The Hackett Group research shows that organizations with a high degree of end-to-end process ownership outperform those with low levels, across efficiency and effectiveness metrics.
Who should then be the process owner for what is an increasingly critical driver of growth and profitability?
What's the Future Analytics Service Delivery Model?
There are different ways to imagine the future:
- A COE that sets the guidelines, coordinates and governs analytics activity reporting not to finance but to a senior strategy leader.
- A COE that retains the most complex analysis – or in contrast the most standardized repeatable analysis – while handing everything else to the business, reporting into finance or strategy.
- A standalone analytics function that “explodes” the COE into a full-fledged organization under the lead of a chief analytics officer reporting to the CEO.
There are probably more.
However, no matter your view of the future of how analysis is created and shared:
(1) it’s already expanded into an enterprise-level capability; and
(2) the emergence of new technologies powered by AI and machine learning will surely cause a radical change in the current service model.