Accenture studied the most analytically-mature companies to better understand this transformation. Coupled with our own experience and the recent Winning in Analytics research, we conducted a deep-dive benchmarking study on analytics operating models in order to better understand how high performing companies are organizing their capabilities.
Just a few years ago, companies were still in exploration mode, trying to balance the benefits of centralized analytics resources against their business units’ demands for ready access to analytics expertise.
The Analytics Operating Model Benchmarking Study suggests that companies are looking to more aggressively transform their analytics operating model and drive change, with the most mature companies well along the path to an agile, integrated analytics operating model (as seen below).
Companies with high maturity levels (Stage 5 companies) were more likely to have established a center of gravity for analytics activities, often in the form of a Center of Excellence (CoE) or similar concentration of talent and resources.
Establishing a center of gravity for key resources builds capabilities, but it also supports consistency and high standards while allowing functional resources to focus on key business problems and applying insights.
Leadership is another key element. Nearly 40 percent of the companies we surveyed said they are elevating analytics by creating a Chief Data and Analytics Officer (CDAO) role responsible for both the vision and the implementation of the enterprise analytics strategies.
The CDAO is often responsible for developing goals, strategies and plans to support the information, reporting and analytical needs of the company, but also acts as an agent to change the analytics culture of the company.
Market Archetypes as an Organizational Construct
We found that companies are investing considerable time and resources in building operating models, and many are thinking along non-traditional lines. Global CPG companies, for example, cluster capabilities within similarly mature markets, rather than by traditional geographic boundaries. This has proven to be a cost-effective approach to allocating scarce analytics resources.
Traditional governance models are often thought of as sluggish. Today's high-performing analytics leaders are building thin, horizontal governance structures focused on outcomes and speed to value. These new structures take a “test and learn” approach, and also employ “fail fast” techniques, rapidly rolling out new ideas and capabilities and testing them repeatedly. If an idea is not working, it is dropped quickly, so that the company does not continue to invest in something that does not add value.
Structured innovationAnother element of agile governance is a structured innovation process. Leaders may set up innovation or “SWAT teams” with a mandate to focus on key business questions for a concentrated period of time.
Executive scorecardsPerhaps the most critical part of establishing an agile governance structure is ensuring that it uses the key metrics that are most important to the business, which might include:
Speed to standing up priority capabilities
Pace of adopting new capabilities
Value creation offices
Leaders are also instituting value creation “offices” to spearhead outcome tracking against key metrics and to ensure that the value from analytics is realized. These “offices” have involvement from senior leaders with accountability for analytics as well as program management office resources who can develop templates and processes as needed.
Companies with advanced analytics capabilities typically field teams with diverse skills, organize talent effectively and continuously engage them for optimal retention, and use multiple talent sourcing options, including attracting talent from other companies.
Analytics pod structures
Many companies are establishing “pod” teams (displayed on screen 2) which have a mix of roles, including data scientist, analytics modeler, visualization expert, data engineer, business analyst and business domain expert. By combining these capabilities, analytics pods can take an integrated view of business problems.
Analytics career paths
Analytics talent tends to follow nontraditional career paths in response to the value that this talent tends to place on becoming subject matter experts in finding insights in large data sets, often instead of following traditional managerial career paths.
Leading analytics companies create mechanisms to source the best talent for their organization, for example, brand marketing, crowdsourcing, even partnering with academia.
With talent in short supply, many companies are developing reward and incentive programs that keep these individuals engaged. The study shows the key to analytics talent retention is keeping these resources challenged.
One of the biggest challenges for analytics organizations is to establish an operating model with a view to scaling priority capabilities, especially in light of the roadblocks posed by existing analytics skills and in-place data architectures. Leading analytics organizations deploy new, agile technologies, as well as hybrid architectures and specifically designed toolsets, to help achieve speed to capability and desired outcomes.
We believe that scaling priority capabilities requires new approaches and mindsets for many organizations. These organizations may need to “unlearn” what has already been learned; for example, they may need to bring the data to the analytics, rather than the other way around.
There may also be differences in the way teams with statistical backgrounds tackle scaling problems, using a hypothesis/test/verify framework. Many organizations have already begun addressing these issues.
By prioritizing the scaling of capabilities, companies stated that they were able to optimize analytics investments, rationalize vendors and suppliers, improve talent acquisition, and rationalize data and tools.
The final distinguishing characteristic of leading analytics organizations is their commitment to raising the “analytics IQ” of all roles within the enterprise.
The intelligent enterprise
Leading analytics organizations—those surveyed by Accenture as well as those we have worked with directly—have a vision of what might be termed the “intelligent enterprise.” They are training resources to use new tools and techniques to improve decision-making throughout the enterprise and they are also implementing innovative technologies such as advanced data visualization to communicate the value of analytics to business units, core functional teams and IT.
Some companies use the Accenture Connected Analytics Experience or build their own immersive environments that leverage visualization techniques to provide greater context for the information presented as well as the trends that are being illustrated. They are moving away from traditional presentation tools and are leveraging more interactive tools to improve collaboration.
A holistic, interactive learning approach where business, analytics, and IT resources are equipped with new immersive tools, techniques, and formal training opportunities allows companies to:
Activate leaders by focusing time, funding and attention on desired behaviors and skills;
Shift behaviors and mindsets to engrain new habits
Embed behaviors into business processes and metrics, all in service of driving a cultural transformation.
As analytics grows in importance and commands a greater share of enterprise resources, companies will need to transform or refine their analytics operating models to reflect changing realities.
Companies have extracted enormous value from analytics and are significantly increasing their investments in hope of accelerating new product development, opening new markets, enhancing their customers’ experience and ultimately becoming an insight-driven enterprise.
We have identified three immediate priorities that companies can take to kick start their journey (see next screen).
Start by aligning on the North Star for analytics by determining where your company is in its journey and what it needs to do to develop sustainable capabilities to reach its goals. This is often driven by an understanding of the organization’s existing talent, tools, and investments so that gaps, redundancies and opportunities can be identified.
Knowing the current landscape will also help to prioritize investments in capabilities and will serve as an input to the analytics operating model, governance structure and ways of working between the functions. It is also important to identify a Chief Data and Analytics Officer or equivalent individual with the authority and accountability to initiate and manage the journey.