What would be the process of becoming a data-driven organization?

During the early stages of the product development life cycle for startups, companies work hard to understand their competitors, some use traditional methods in conducting market surveys in order to get information that would help them understand their market, their competitors and consumers characteristics. Businesses have to do their own analyses and use their intuition for taking decisions.

Data-driven organizations grow and evolve over time, they undergo various stages of growth to achieve maturity. It’s clear that these stages are based upon a hierarchy of needs. Also, we can’t move to the next stage of growth unless we satisfy the initial stage of growth.

Collect

This is the basic stage of becoming a data-driven organization. Data collection varies with the context in which an organization operates. Data can be collected from recorded business transactions, logging errors from computers. Software applications in production also produce data that could be essential in data science. Data can also be collected from experiments and external online sources.

Organize

This involves activities centred around transforming the data from one format or structure to another for proper data management. Cleaning data is also of the essence at this point, it would involve removing corrupt or inaccurate records to make data accurate. After transforming and cleanup, we now Store data using the right tools like data Where-house for easy retrieval.

Analyse Data

This involves spinning up data through different algorithms to derive meaning out of it, it is from analyzing data that we generate reports that will help the company make critical decisions. Analysed data helps us to explain what’s happening to our organizations and why it’s happening. The analysis of data can start with basic analysis tools like reports, dashboards and APIs. APIs help in exposing our data after analysis. As the company matures, more sophisticated forms of data analysis are employed. This includes Data Mining, Descriptive Analysis and Diagnostic Analysis.

Predictions

At this point, the organization wants to understand what will happen in the future. This would now involve different and more powerful forms of data analysis, some of these forms include predictive analytics, descriptive analytics and machine learning.

Automate (Artificial Intelligence)

Here, we close the data science loop and remove human from the process, this involves advanced methods like artificial intelligence, deep learning and reinforcement learning. Data Science is essentially the cornerstone of data-driven artificial intelligence. However, in order to become an AI organization, one must first become a data-driven organization.

Seamline Innovations Ltd provides you with expert services in setting up and implementing data science departments in your organization. our Machine Learning Engineers will finish the organization’s data transformation journey through careful planning and execution. Reach us out at info@seamlineinnovations.com.