How to Assess the Analytics Maturity of Your Organization? 

Data analytics transforms raw data into actionable insights that drive informed decision-making, enhance performance, and create value. Yet, organizations vary in their data analytics maturity and capabilities. Wondering how you can monitor your current position and elevate your data analytics journey? This article outlines steps to evaluate and enhance your data analytics strategy and roadmap.

There’s no one-size-fits-all formula for defining a ‘world-class’ product or service in a landscape marked by rapid innovations and evolving industry standards. The use of data analysis for decision-making has gained paramount importance. Analytics teams are proactively reevaluating their technology architectures to meet the demands of the ever-expanding realm of big data while maintaining efficiency.

A robust analytics architecture significantly impacts data analysis effectiveness and the success of business decisions. To tailor their approach and execution, organizations must assess their analytics resources and initiatives. This is where the analytics maturity model becomes invaluable.

What is an Analytics Maturity Model?

Models are essentially schematic illustrations for extensive growth phases that offer improvement suggestions to move from the present situation to the intended state. Models work with particular and quantifiable indications and features for this purpose.

The analytics maturity model describes the path an organization has chosen in terms of its capacity to effectively incorporate data from both internal and external resources, handle the data, and perform analysis that modifies the data into insightful findings that might influence organizational choices.

Source: Altexsoft

It is an extensive set of technological advances, individuals, operations, and approaches necessary for extracting economic benefit from raw data and operating on the findings to provide business benefits. 

In simple terms, the analytics maturity model is a benchmark for evaluating the analytical skills of an organization and what stage they are currently at in relation to the targeted level.

Also Read: Difference Between Data-Driven and Normal Organization

Types of Analytics Maturity Models Used in Organizations

  • Blast Analytics Maturity Assessment Framework
  • DELTA Plus Model
  • Web Analytics Maturity Model (WAMM)
  • SAS Analytics Maturity Scorecard
  • Gartner’s Maturity Model for Data and Analytics
  • Online Analytics Maturity Model (OAMM)
  • Data Analytics Maturity Model for Associations (DAMM)
  • Analytics Processes Maturity Model (APMM)
  • Analytics Maturity Quotient Framework
  • TDWI Analytics Maturity Model

Phases of Analytics Maturity

The analytics maturity is not just a single event. It is a detailed process that includes different phases to get the desired results. It has the following phases: 

Descriptive Phase

The beginning of this process uses previous records to answer the question, “What happened?” in previous years. By using historical data, it answers questions about how much, what, and where. It can often be used to gain an understanding of the organization’s past performance. These analytics feature relatively easy instances with limited organizational significance. 

Diagnostic Analytics

This phase advances further into organizational data and data mining technologies to answer the question, “What happened and why?” Although considering that this is primarily a basic step that uses the same technologies as descriptive analytics, it uncovers the correlations behind the data, providing greater understanding. 

It includes statistical methods that include statistical summarization, correlations among numerical factors, bi-variate and multivariate analytics for finding variations and patterns in data. The Diagnostic Analysis phase centers around collecting overlooked insights from data that were missing when conducting the Descriptive Analytics phase.

Phases of an analytics maturity model
Source: pHdata

Predictive Analytics

The goal of this stage is to construct models that derive predictions from past data. It centers around data mining and incorporates many types of advanced analytics methods, including machine learning and artificial intelligence. 

This phase answers the question, “What is going to happen in the future?” Predictive analytics analyzes the data’s underlying patterns. The correlation between the predictor factors and the outcome variable is established to construct a mathematical or statistical model. These mathematical models shall be used to forecast the result variable with fresh data. 

This phase includes several steps of data preparation, data cleansing, feature engineering, model development, and model evaluation. It facilitates the automation of the decision-making process by applying models that predict future events and provide you with the necessary data to make the right decisions.

Perspective Analytics

Prescriptive analytics deals with the question of “How can I accomplish it? What must be done?” It helps you understand several mathematical models, analyze their effectiveness on the data readily accessible in a particular sector, and determine the best approach to address the organization’s issues. 

The objective is to determine the most effective course of action in any given circumstance. ML and deep learning tools look over huge quantities of data using algorithms and statistical modeling frameworks and then recommend solutions to decision-makers.

How to Improve the Analytics Maturity of Your Organization?

There are certain steps that can help you to improve the analytics maturity of your organization.

Step 1: Improve Data Quality

The initial step approaching data maturity is to organize your organization’s data in one central system, such as a data warehouse or data lake. Initially, the organization will primarily utilize its own data. However, you should look for viable systems that can integrate external or distinctive data sources.

Step 2: Improve Organizational Dynamics

Concentrate first on smaller-scale, high-quality analytic efforts that can attract managers and draw support from every department to help make progress. The management team has to set up data as an essential component of the organization right away as leadership recognizes the significance of analytics. 

The appropriate dashboards should be available to employees so they are helpful in making data-driven choices. IT needs to be involved during every step to ensure efficient data management and integrity.

Step 3: Improve Analytic Team Dynamics

Begin by creating groups to help with your initial analytic operations and improve the group interactions. These groups should consist of members from different divisions, analytical managers, and stakeholders. Keep in mind that your stakeholders are your clients, as they are going to be engaging with the product on a daily basis. 

When exploring low-budget, high-impact projects, look for certain analytical skill sets inside your organization. Extensible solutions that offer cutting-edge analytics, AI, machine learning, and other features will make things simpler to progress to a more advanced degree of analytics maturity.

Step 4: Improve Usage and Technology

Your analytics maturity will benefit significantly by using the appropriate IT framework and analytic technologies. To enhance this aspect of your business, start making efforts for expansion along with constructing your analytics structure. Additionally, you should look into cloud-based analytics and evaluate the way each system will integrate with your current IT architecture.

Explore cost-effective alternatives that analysts and skilled employees across the organization could benefit from rather than investing a lot on a component like a data lake that just a handful of people will use.

Step 5: Democratize Analytics

The democratization of analytics or upskilling of data professionals within the organization is one of the most essential phases in increasing the analytics maturity of your organization. Data democratization functions as a shift in management procedure. It is the most efficient approach for organizations to succeed in the majority of decisions based on data.

When your data scientists concentrate on more extensive, challenging initiatives, a number of skilled employees might be engaged in minor analytical initiatives that speed up ROI.

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Challenges in Deploying Analytics Maturity in Your Organization

Challenges in deploying Analytics Maturity in your organization can include:

  • Limited Data Infrastructure: Setting up and maintaining robust data infrastructure, including data storage, integration, and governance, can be challenging. It requires investment in technology, skilled resources, and overcoming legacy systems.
  • Insufficient Data Quality: Poor data quality, such as incomplete or inaccurate data, can hinder analytics initiatives. Ensuring data accuracy, consistency, and relevance is crucial for deriving meaningful insights.
  • Siloed Data and Organizational Structure: Data silos within departments or business units can impede effective data sharing and collaboration. Lack of a centralized data strategy and coordination can limit the organization’s overall analytics maturity.
  • Cultural Resistance and Lack of Skills: Organizations may face resistance to change and adoption of analytics-driven decision-making. Building a data-driven culture requires executive buy-in, training employees with the necessary skills, and fostering a mindset of experimentation and innovation.
  • Aligning Analytics Goals with Business Objectives: Identifying the right analytics use cases aligned with business goals is crucial. Lack of clarity and alignment between analytics initiatives and organizational objectives can result in poor adoption and underutilization.
  • Scaling Analytics Capabilities: As organizations grow, scaling analytics capabilities across departments and functions becomes complex. Ensuring scalability of infrastructure, talent, and processes is essential to maintain analytics maturity.

Conclusion

The Analytics Maturity Model is designed to outline an approach for enhancing organizational competence broadly. You can dig into client behavior more extensively, find new possibilities, and respond to market changes more rapidly. Analytics Vidhya offers a comprehensive approach to tracking and determining the growth of analytics across your organization. By proactively implementing initiatives to elevate your analytical maturity, you can position your organization for long-term success.

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Frequently Asked Questions

Q1. How do you assess data maturity of a company?

A. Assessing the data maturity of a company involves evaluating various aspects, such as data governance, strategy, infrastructure, and analytics capabilities. This assessment helps determine their level of readiness and effectiveness in leveraging data for decision-making.

Q2. What is an analytics Maturity assessment?

A. An Analytics Maturity Assessment is a structured evaluation that measures an organization’s level of analytics implementation, adoption, and effectiveness. It assesses various dimensions, including people, processes, technology, and data, to gauge the organization’s analytics maturity.

Q3. What is analytics maturity of an organization?

A. The analytics maturity of an organization signifies its capability to effectively and efficiently utilize analytics for data-driven decision-making, innovation, and value creation. From basic reporting to advanced predictive and prescriptive analytics, the organization’s analytics maturity determines the extent of their analytical capabilities.

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