Your data should ascend through Maslow’s hierarchy of needs
Big data refers to the high volume, speed and variety of information resources. The industry emerged from the explosive data growth as a result of the digitization of our society and is a decisive driver of technologies, infrastructure and related services. Big data innovation is driving growth in all industries and sectors, and the momentum continues to increase. Some companies are growing faster than others, and a lot of it depends on how effectively they integrate and use data in their operations. This is usually referred to as data maturity and can be seen in the context of the big data maturity model.
The Big Data Maturity Model provides the evolutionary framework
The Big Data Maturity Model is a framework that describes the use of data to develop technological platforms and processes that are aligned with the goals of an organization. Data maturity models are much more than simple frameworks. They have been compared to a prominent psychological model of human behavior known as Maslow. Hierarchy of Needs, begun by Abraham Maslow, a major 20th century psychologist. Data maturity goals have a similar track record. Each phase depends critically on the previous tier and encompasses the critical infrastructure, technology, and human resource decisions that embed the data in each department.
Goals of The Data Maturity Model
At the top of the data maturity model is an organization that has efficiently merged data in all of its operational processes with the following goals:
- Assessing the capabilities and ultimate potential for the use of data in critical areas of the organization
- Identifying, developing, and guiding milestones, objectives, and goals
- Building data capabilities within the organizational infrastructure
- Improving productivity, efficiency, and overall success of all organizational activities
- Risk assessment and prevention of data-related operational issues
Stages of Data Maturity Models
There are five main stages of data maturity models that are widely cited in the industry, and while they have different names, they all typically describe the same stages of development, including:
Stage 1: Inception/Discovery
Companies in the discovery phase understand big data and its potential to add value to the business. In this phase, decisions are made to update processes, employees and infrastructure. In order to move forward successfully, it takes thorough research and strategic decisions making.
Stage 2: Startup/Pre-Adoption
This is the phase when organizations continue to research while developing strategic goals and objectives. In addition to their research, at this stage companies plan to design and implement an initial infrastructure aligned with their goals while reviewing use cases that extend beyond this stage and into the future.
Stage 3: Practical Adoption
This is the early introductory phase when one or two projects or prototypes (also known as proof of concept or POCs) have run through the production line and are preparing to launch. This is a critical phase that ties the adoption of big data analytics from one area to another across the organization.
Stage 4: Strategic Integration
This phase is characterized by corporate acceptance. It is a critical transition phase in which important additional insights are gained, employees are involved in the process, and workflows throughout the company are profoundly changed. The use of analytics at this stage typically enhances decision-making by managers throughout the organization in terms of performance gaps, infrastructure and governance.
Stage 5: Optimized/Visionary
This is the “top” of the pyramid of the maturity model. Here, the organization operates big data operations with high efficiency using an optimized infrastructure in accordance with the strategic goals coordinated with its goals. Big data is an important part of the company’s activities in this phase and is initiated, budgeted and planned across the company.
Where is your organization on the data maturity model?
Regardless of whether your company is in the start-up phase or rising to the top of the model, significant progress can be made at all levels. The first step is to examine how data can make a positive impact at every level of your business. The next step is to learn how to collect data through purchased datasets or web scraping and use that information to improve decision making, pricing strategies, marketing plans, etc. By implementing maturity models, companies can accurately measure the current data environment Company. After an assessment, concrete growth opportunities can be acquired using a prescriptive maturity model. Finally, comparing one organization to another in the same industry can enable even greater growth.
All of these steps implemented by data maturity models have numerous advantages. Over time, key strategic initiatives can be more effectively defined, activated and implemented. In addition, every department and project is linked to data, which improves overall performance in different areas of the organizational structure. Finally, the integration into the data of the organization enables greater agility if necessary.
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