Data-driven marketing vs. Conventional Marketing

Chapter 6

Organizational Maturity Assessment

Prior to any planning or action, aiming transformation, it is crucial to understand the current standpoint of the firm, in multiple aspects to evaluate the maturity level, in terms of data-orientation. These assessments may include available data, existing monitored KPIs and performance metrics, infrastructure and most importantly, organizational culture and perception toward this topic.

 After all, the pre-requisite of successful implementation of the required projects, facilitating transformation with costly resources in terms of infrastructure and time, is the amenability of all engaged functions and thus, people.

"Therefore, it is advised to make the cultural shift as a priority. It is vital that all the functions become aware and receive essential skills, knowledge or training enabling them to envision the full picture in long run. By doing this, not only the organization will avoid creating an ambiance that some may feel left-out or even being threaten, but will facilitate the path to transformation by pro-active contribution of pivotal departments such as operation, sales, marketing, finance, purchasing and human resource". 

Data Maturity

Data maturity, simply measures the status of a company in terms of leveraging data and using the insights for any key-decision.

A mature firm in terms of data maturity, has a fully developed framework which is embedding data and reasonings concluded from the analysis, across all units or divisions and makes no final decision, until the data supports it.

In this context, it is worth to mention that data has a broad definition, from information collected from customers to operational or financial relevant generated data.

Since being data mature, can be referred as a progressive journey, it is best to first breakdown the stages, from just an idea to turning into a component that is vital part of the business strategy:

1. No analytics: The very base or initial stage, an may be associated to start-ups that are planning to be a data-oriented firm, or organizations that might have underrated the value of data and analytics.

2. Descriptive analytics: This stage provides insights to comprehend the reality, alongside to current events, by depicting of the data. This stage usually has the capacity to answer “What did occur”? It may also help to measure the efforts of a company, to some extent.

3. Diagnostic analytics: As the name of it indicates, this stage can perceive relations between several variables by analyzing historical validated data. It may be helpful to respond the question “Why did it occur?”.

4. Predictive analytics: By analyzing previously made decisions, action and consequences, this stage has an advanced capability to correlate between actions and outcomes to predict the result of a certain decision, with a high precision.

5. Prescriptive analytics: The emerging stage that each company propels to reach. This stage leverages machine learning algorithms to produce sensible recommendations and advices on future actions. This stage answers to the question, “What is best to do”?

Back to the data maturity assessment topic, there are hundred models introduced to evaluate the current level of maturity of an organization. It is not quite important which to choose, the idea is to perform such assessment, prior to planning.

One of the best practices is to measure the readiness of maturity in multiple areas, from lagging or explorer to leading or innovator framework. 

Each element in above model may be dissected or adjusted based on the company, in a rational way, but with renewing the definitions, it would be not complicated to understand the maturity level of the firm you aim to assess in terms of data and analytics.

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