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The Analytics Maturity Spotlight: A Model for Organizational Success

06/03/2019 by Tricia Aanderud Chris St. Jeor Modernization - Analytics

Every month, it seems there is a modern technology introduced that can transform your organization in unimaginable ways. Despite the rapid growth of analytic solutions, a recent Gartner survey revealed that almost 87% of organizations believe their analytics maturity had not reached a level that optimized business outcomes.

The hype is not matching reality.

Just like with any endeavor, your organization must have a planned strategy to achieve its analytical goals. Even the best-intended projects die on the vine if not given the proper support.

History of the Analytics Maturity Model

The analytics maturity model (AMM) has its roots in the software capability maturity model (CMM) popularized by Carnegie-Mellon’s Software Engineering Institute (SEI). Like the CMM, the AMM describes the five stages an organization travels through to reach optimization.

In the early 1970s, the US Air Force tasked the SEI with solving a common problem: their software projects were slower than expected and often ran over budget. They needed a way to evaluate vendor business processes and practices consistently. From this exercise, the process maturity framework was created, which would later become the basis for the CMM popularized by Watts Humphrey.

The CMM describes the stages that an organization travels through to reach process formality and optimization. Each level is a measure of process maturity that the organization must solve to become more successful and have more predictable outcomes. The model is like a ladder and each level represents a rung. Organizations must reach each rung before climbing to the next one.

Most companies get to level 2 for software maturity, but few get past it. Usually, it is because leadership gives the subject lip service with little to no investment; and thus, no real strategy for climbing the rest of the ladder.


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Applying the Capability Maturity Model to Data and Analytics

The CMM is the basis for other organizational maturity strategies, such as the analytics maturity model (AMM). In the mid-2000s, The Data Warehouse Institute (TDWI) discussed achieving business intelligence (BI) maturity. They based the interpretation on the level of technical implementation, such as introducing data marts and databases. In subsequent years, others would release versions of the maturity model that viewed it from either an IT department or a customer-centric perspective.

All models shared a similar characteristic of starting at a tactical level, moving to a strategic level, and ending in an optimization state.

Gartner produced the most widely embraced model. In 2008, they released a Web Analytics Maturity Model that detailed the steps going from simple data collection to using the data strategically to drive sales activities. Later, their model applied to more general practices. This model closely followed the CMM, but its focus was on reaching maturity through business intelligence (BI) methods.

Unlike other models, this evolving model incorporated the non-technical aspects of maturity, such as culture, people, and leadership. Organizations find these elements more challenging to solve. While the marketing department can easily own web analytics maturity, this model needed the enterprise to be involved and for centers of excellence, Business Intelligence Competency Centers (BICC), to be formed. This work was so crucial that a C-level executive should be sponsoring it. The model appealed to leadership to use data to transform the organization.

In 2017, Gartner released the maturity model called 2.0. This model showed the BICC maturing into Analytics Centers of Excellence (ACE). It also introduced the idea of a C-level executive responsible for overseeing analytics and data. This model more accurately described how organizations should move their staff toward adopting analytical methods such as machine learning and artificial intelligence (AI).

Understanding the Maturity Levels

After reviewing multiple models and comparing the models to our real-world experience from working with many customers, the Zencos team created our analytics maturity model (shown below). This model describes how organizations evolve from chaos to an optimized data-driven strategy.

Level 1: Chaos rules the operation

In Level 1, the organization is dependent on spreadsheets and ad-hoc analysis. Everything is a one-off; it is anyone’s guess if results achieved by one person could be reproduced accurately by another person. Typically, the only reports available at this level are focused on financial results. Usually, this information is more of a process artifact than something someone planned.

As the organization becomes more successful, the information and process knowledge becomes unwieldy for one person or even a small group to retain, especially without proper tools. This often leads to dissemination of incorrect and misaligned information. The organization cannot function for long in this manner, and they realize they need improved methods to survive. They must begin a transition to the next level.

Level 2: Developing repeatable processes

In Level 2, the organization is rising above the information chaos. The line managers realize they need tighter controls over the processes to ensure they can meet customer demands. To streamline operations, departments adopt applications that record daily work activities and customer interactions, such as order management, trouble tickets, and the like.

Each department selects the tool that best meets their needs without giving any consideration to how the data is stored or accessed. These applications produce reports that are used for daily operations. The managers understand the value of record keeping, but they do not yet view the data as anything more than a byproduct.

The data sets are not used strategically. If analytics are produced, they are confined to one or two individuals and rarely shared across the organization. The management team does not understand the full value that the data can offer.

This stage is where most organizations realize they need a strategy. The effort is generally accomplished and led by the CIO or IT department.

Level 3: Aligning to standards

As they are growing, the organization wants to take on the behaviors of larger, more successful companies but find their existing processes are not standardized across the organizational areas. For example, if IT is fielding requests for reports, they may be overwhelmed by inconsistencies and siloed data and realize that a different approach is needed. In other cases, it may be department managers who recognize their teams are spending too much time moving data between databases rather than producing informative reports. These issues require resolutions.

In Level 2, operational reporting is mastered. In Level 3, the organization realizes the need to take the data to the next level. Companies form business intelligence competency centers (BICC) to create a standardized approach for setting and keeping standards in the adoption of business intelligence (BI) and decision-support tools. The organization starts using dashboards and scorecards to measure performance and set alerts. For these tools to be useful, organizations must link them to business strategies.

The analytics advocate appears within the enterprise during this level of maturation. This individual may be from the IT organization, such as the CIO, or the business side, such as an aspiring data scientist or seasoned analytics pro. The advocate understands the value of data but also recognizes the organization does not realize the full value of the data. With proper influence and support, this person can ignite the organization. In some instances, the advocate may be an outside consultant who was initially hired to assist with other issues.

Level 4: Integrating into the enterprise

In Level 3, the organization aligned to standards so they would be more strategic. In Level 4, the organization wants to use data across the enterprise. The C-suite takes notice and elevates the initiative to the CIO or CFO. They know data is the key to understanding and serving customers better.

This situation, however, may be the most challenging level for organizations to achieve because it is where the decision to compete with analytics begins. For traditional companies, this is a radical change in their business model.

Because of this, it’s possible the organization may not choose to commit entirely at first. If so, an intermediate step is to allow the BICC to explore and experiment with more sophisticated data techniques, such as predictive analytics. With success, this team matures into an analytics center of excellence (ACE), that is ready to take on machine learning and artificial intelligence projects.

Level 5: Establish optimized performance

In Level 5, the organization has fully implemented a data-driven approach to the business decision process. The C-suite has firmly defined and established the roles of the chief analytics officer (CAO) or chief data officer (CDO). The enterprise has embraced a performance culture across the business. They seek out opportunities to incorporate analytics and encourage that mindset throughout each department.

At this point, the advanced analytics team has adopted analytic platforms capable of collaboration and sharing of models across the enterprise. The organization also encourages its vendors to use analytics for improvements, thus shaping new industry standards for analytics.

The analytics team is fully developed and capable of perfecting existing models and creating new methods to push the boundaries of their productivity curve. The team has moved to full machine learning solutions capable of providing prescriptive analytics over that of descriptive or predictive analytics. The models are capable of self-learning over time, thereby perfecting their performance and predictive power.

Getting Your Strategy Correct

Organizations must implement the right tools, engage their team in the proper training, and provide the management support necessary to generate predictable outcomes with their analytics.

Our team has assisted multiple organizations as they transform from simple reporting to data optimization. Using this information, you can lead your organization in creating a successful road map.