Every month, it seems there is a modern technology introduced that can transform your business in unforeseen ways. There has been rapid growth in the analytic solutions available. However, a 2018 Gartner survey revealed that almost 87% of companies believe their analytics strategy did not provide optimized business outcomes.
The hype is not matching reality.
Just like with any endeavor, your company 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.
What is the Analytics Maturity Model?
The analytics maturity model (AMM) has its roots in the software capability maturity model (CMM). The model describes the five stages a company travels through to reach maturity.
In the early 1970s, the US Air Force tasked the SEI with answering a simple question. Why were their software projects slower than expected and often over budget? They needed a way to evaluate vendor business processes and practices consistently.
From this exercise, the process maturity framework was created. It would later become the basis for the CMM popularized by Watts Humphrey.
The CMM describes the stages that a company travels through to reach process formality and maturity. Each level is a measure of process maturity that the business must solve to have more predictable outcomes. The model is like a ladder; Each level represents a rung. Companies 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. Thus, there is no defined strategy for climbing the rest of the ladder.
Applying the CMM to Data Strategies
The CMM is the basis for other maturity strategies. In the mid-2000s, The Data Warehouse Institute (TDWI) discussed achieving business intelligence (BI) maturity.
They based the explanation on the technical level, such as introducing data marts and databases. Other vendors released data maturity models, viewing it more as maturing the IT department. Others used a customer perspective. All customer service needs should drive analytic maturity.
All models shared a similar trait. They start at a tactical level, move to a strategic level, and ended with optimized processes.
Gartner produced the most widely embraced model. In 2008, they released a Web Analytics Maturity Model. This model detailed the steps going from data collection to using data 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 BI methods.
Seeing Analytics Maturity in a New Way
Unlike other models, this evolving model used the non-technical aspects of maturity, such as culture, people, and leadership. Companies 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.
The company required focused areas called Business Intelligence Competency Centers (BICC). This work was so crucial that a C-level executive should be sponsoring it. The model appealed to leadership to use big data to transform their companies.
In 2017, Gartner released the maturity model called 2.0. This model showed the BICC maturing into Analytics Centers of Excellence (ACoE). It also introduced the idea of a C-level executive responsible for overseeing analytics and data.
This strategy includes machine learning and artificial intelligence (AI). It described how companies should move toward adopting advanced analytics. All of these levels are significant achievements. Companies need to move through these levels of analytical maturity to realize the full potential of their big data strategies.
Understanding the Data Maturity Levels
After reviewing multiple strategies and our field experience, the Zencos team created our maturity model. The following model describes how businesses evolve from chaos to an optimized data-driven approach.
Level 1: Chaos rules the business
In Level 1, the company 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. There is undoubtedly no analytical strategy available at this point.
As the business reaches new success, the process knowledge becomes unwieldy for even a single person to retain. With the proper tools, even a small group struggles to be productive. Managers fail to see this situation as a real or solvable issue. They are focused on survival.
This situation often leads to spreading incorrect and skewed information. The company cannot function for long in this manner, and they realize they need improved methods to survive. They must begin a transition to the next analytical level.
Level 2: Developing repeatable business processes
In Level 2, the company is rising above the information chaos. The line managers realize they need tighter controls over the means to ensure they can meet customer demands. To streamline work, departments adopt applications to record daily work activities and customer interactions.
Each department selects the tool that best meets their needs. Few managers are giving any thought to how the underlying data is stored or accessed. In most cases, these applications produce reports for daily activities. So the decision-makers understand the value of record-keeping, but that is all. However, they do not yet view the data as anything except a process by-product. They fail to see the competitive advantage of real analytics program.
The company is not using the data strategically. If analytics are present, the results are confined to one or two workers and rarely shared across the company. Most likely, these folks are using low-level analytics tools such as Microsoft Excel. The management team does not understand the full value that the data can offer. They forget to ask the critical business questions that data and analytics can so quickly answer.
This stage is where most leaders realize they need a strategy but may not understand what to do. The effort is generally accomplished and led by the CIO or IT department.
Level 3: Aligning to company-wide standards
As they are growing, the business wants to take on the behaviors of larger, more successful companies. Often the managers are disheartened; they realize their existing processes do not apply across the business. For example, if IT is fielding requests for reports, they may be overwhelmed by data quality issues. There may be multiple data sources available, and none match one another.
They realize that a different approach to analytics maturity is needed. In other cases, it may be department managers who recognize their teams are spending too much time moving data between databases. All of the data sources make it confusing. They need those analysts producing informative reports. These issues require resolutions.
In Level 2, the company masters enterprise reporting. In Level 3, the leaders realize the need to take the data to the next level. This level is where most get serious about data quality. The management understands the need for proper data governance that allows the company to reach full analytics powers. Rightfully so they focus on data.
IT may be moving applications and thus, data into the cloud. They must ensure they do their cloud homework. With more convenient access to the data, workers may become more curious and grow interested in playing with some of the self-service tools. The visualization and analytics tools must be easy to learn and use.
Companies form business intelligence competency centers (BICC) to create a standardized approach toward adoption of business intelligence (BI) tools. The management team starts using dashboards and scorecards to measure performance and set alerts.
Data visualization becomes as a valid way to present data. For these tools to be useful, analysts must link them to business challenges and answer business questions.
The analytics advocate appears within the enterprise during this level of maturation. This person may be from the IT department, such as the CIO. It is also possible that an aspiring data scientist or seasoned analytics pro will lead the charge.
The advocate understands the value of data. She also recognizes that the management team may just be starting to realize the full value of the data. With proper influence, statistics training, and support, this person can move past obstacles and ignite the business. In certain cases, the advocate may be an outside consultant who was initially hired to assist with other issues. However, the skill set arrives – the business must welcome it.
Level 4: Integrating analytics into the enterprise
In Level 3, the company aligned to standards so they would be more strategic. In Level 4, the leadership team 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. They want all workers to use data – they add self-service BI as a required skill.
The staff must be able to communicate with data. The team needs to ensure their reports have takeaways. They have data storytelling skills. When passion ignites, they may seek alternative ways to communicate their data findings.
This situation, however, might be the most challenging level for companies to achieve.
This moment is when the decision to compete with analytics begins. For traditional companies, this is a radical change in their business model. They want to be data-driven. They want their analytic investment to pay off. Initially, this means more investment in sophisticated data platforms that can handle large volumes of data with ease and speed.
Because of this, it’s possible, and the business may not choose to commit entirely at first. If so, as a smaller step, the BICC experiments with advanced analytics. They may explore how predictive analytics can assist with business goals.
With success, this analytics team matures into an ACOE who is ready to take on machine learning and AI projects like text mining or image analytics. One example is how banks running AML compliance programs use AI to fight financial crime. One thing is sure – they must have robust, flexible, and expandable platforms for this work.
Level 5: Optimized data-driven performance
In Level 5, the company 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 occasions to incorporate analytics and encourage that mindset throughout each department.
At this point, the advanced analytics team has adopted mature analytics platforms. These platforms allow them to share models across the enterprise. The company also encourages its vendors to use analytics for improvements, thus shaping new industry standards for analytics.
The analytics team is fully developed. The team is capable of perfecting existing models. 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
Companies must implement the right tools. They must adequately train workers and managers. And the managers must provide the needed support to generate analytics.
Our team has assisted multiple companies as they transform from simple reporting to analytic maturity. Using this information, you can lead your business in creating a successful road map.