Development of a Data Management Strategy for Azerbaijan
Development of a Data Management Strategy for Azerbaijan
87% of companies have a low level of data management maturity, which means their analytics strategy isn’t delivering optimal business results. Learn how to take your data utilization to the next level.
Most companies struggle with data
Quick access to and use of data is the main challenge on the path to digitalization
0%
This data is not used within the organization
0%
Users believe that the data is out of date
0%
Users claim that they cannot make good use of the data
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companies do not use real-time data
Fundamental Principles of Data Management Strategy
Help increase data utilization. Quick access to data.
The data is treated as an asset
The owners of the data elements have been identified
The single version of the truth
The definitions are universal and clear
A constant focus on data quality
Data elements are protected
Three Key Elements of a Data Management Strategy
1. A Five-Level Maturity Assessment Model
Companies are focusing more on adopting new technologies than on staff development and implementing data management processes. But it is impossible to achieve high levels of maturity and carry out a digital transformation without these elements
CHAOS
Process repeatability
Compliance with standards
Analytics Integration
Optimal performance
CHAOS
Process repeatability
Compliance with standards
Analytics Integration
Optimal performance
People:
No one is responsible for data and analytics
Roles in data and analytics processes have not been defined
Processes:
Requests for one-time reports are the most common
No control or intermittent control
Technologies:
Spreadsheets are the primary tool
Information anarchy leads to the spread of false and distorted information
People:
Management may realize that they need a data strategy, but they don't know what to do about it
Most often, these processes are carried out by the IT department and led by the IT director
Processes:
The emergence of elements of a data management system, most often processes related to data quality (master data, formats)
Technologies:
Each department uses the tool that best suits its needs
Reports on daily activities are generated
People:
The data is considered a factor supporting management
Managers Become Leaders in Business Analytics
A business analytics center is being established
Processes:
Data management processes are beginning to be automated, standardized, and scaled to the enterprise level (becoming cross-functional)
Technologies:
Standards for the technologies used are emerging
Descriptive and diagnostic analytics are used
A corporate data warehouse is being created
People:
Data is an asset and the key to understanding and better serving customers
Self-service business analytics—an essential skill for employees
Training in data visualization and storytelling
Processes:
Centralized data planning and management
Centralized metrics for data management processes are being implemented
Technologies:
"Data lakes" are being created
Experiments with predictive and prescriptive analytics and "Big Data"
Data cataloging and democratization technologies
People:
The roles of Chief Analyst and Chief Data Officer have been defined and the corresponding organizational structures established
A culture of productivity improvement based on analytics has been implemented throughout the company
Processes:
Data management processes are highly predictable
Data and process quality metrics are clearly understood by employees, and their calculation is automated and accessible in BI
Technologies:
An advanced analytics platform has been implemented
The analytics team has switched to semi-functional solutions for machine learning
2. Pitfalls when working with data
We propose conducting a free employee survey to gather key insights into employees’ attitudes toward data usage, identify the most “sensitive topics” in the field of data and analytics, and examine data quality issues
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employees feel overwhelmed or dissatisfied when working with data (*)
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Nearly 75% of employees waste their time due to poor data quality (*)
3. Reference Data Management Model
We use a reference data management model to plan project tasks and develop a target architecture.
The data management architecture defined in the Data Management Association (DAMA) DMBOK® Guide identifies a set of subject areas
3 Steps to Developing an Effective Strategy
Analysis of the Current Data Management System
Development of a target architecture
Development of a set of tools
Analysis of the Current Data Management System
Development of a target architecture
Development of a set of tools
Tasks:
Analysis of policies, current architecture, and primary data sources
Development of a list of recommendations on key areas for improving data management
Results:
Organization of information, report on the results of the analysis
An Overview of Current Technologies and Trends in Data Management
Approved Key Directions and Initiatives for Improving Data Management
Tasks:
Development of a role-based model and target architecture for technical solutions related to the management, processing, storage, and use of data
Development and alignment of the company’s data management vision
Results:
Report describing the target data architecture and technical solutions for data processing
Data management policies and processes, including the allocation of key roles and responsibilities among departments
Задачи:
Coordination of the selection of IT tools and platforms for data management
Development of a roadmap for implementing a data management system within the company
Development of project specifications for the implementation of the roadmap
Results:
A targeted set of technical tools for data management
The AGROTREYD Group is a vertically integrated agro-industrial holding company with a complete production cycle (production, processing, storage, and trade of agricultural products)
Asino in Ukraine is a modern pharmaceutical company specializing in the development and manufacture of high-tech medicines. Today, the company ranks 8th among Ukrainian pharmaceutical manufacturers.
Asino in Ukraine is a modern pharmaceutical company specializing in the development and manufacture of high-tech medicines. Today, the company ranks 8th among Ukrainian pharmaceutical manufacturers.
PJSC “Romensky Plant ‘Traktorozapchast’” is a recognized leader in the production of spare parts for automotive and tractor equipment. The plant operates its own mechanical assembly, foundry, forging, and tooling facilities and manufactures over 100 types of products for wheeled tractors, combine harvesters, trucks, and passenger cars, as well as consumer goods.
