Opinion #other
3 April 2024
Ihor Sheyko

How data management helps to structure your business – best approaches

The data control strategy should have a robust data quality management framework. Ihor Sheyko, Managing Partner of Digicode, insists that organised data is a critical component of this framework, representing a basis for in-depth analysis of data patterns and anomalies to identify potential problems. The advanced data management processes are a prerequisite for structuring all business assets.

Strategic decision-making through Organised Data

Data management allows business owners to receive reliable information to make the best decisions and increase business efficiency. It improves the operation of all system components by contributing to achieving the set goals. Data management is the basis for developing business strategies, upgrading results, increasing quality, and achieving set goals.

The advantages of effective data management for structuring business and revealing strategic solutions demonstrate the following aspects:

  • High customer-orientation. Data analysis allows business people to learn who the representatives of the target audience are, what they want, and how they interact with certain companies.
  • Optimisation of processes. Analysis helps identify bottlenecks in business processes and optimise them to increase efficiency.
  • Data analysis makes it possible to predict future trends and changes in the market, which helps the company to be ready for changes.
  • Decision-making. Information obtained from data analysis serves as a basis for making tactical decisions.

Over 56% of decision-makers state that most companies’ corporate data is stored in an organised data warehouse.

Most effective approaches to structuring business processes with Data Management

Custom technical solutions based on individual entrepreneurial experience in a specific field of activity represent a foundation for generating the most effective business solutions. In this case, the companies can use data management to reveal the smart methods to structure business processes.

The most effective approaches to structuring a company’s activities include the following data management strategies:

  • Centralised data repository. It represents a type of data management system that provides business intelligence support. Data warehouses are intended for query and analysis purposes only. They typically contain large volumes of data. It comes into storage from various sources, such as application logs and transactional applications.
  • Data integration and interoperability. It is a set of methods, tools and architectural procedures that allow companies to combine and use all data types. The integration process ensures consolidation, data purity, and freedom from errors, allowing the business to use the data as efficiently as possible.
  • Business intelligence and analytics. It helps the business implement reliable solutions, optimise processes, achieve strategic goals, and adapt to change. Business analytics is collecting, studying, and interpreting data about a company’s performance to identify current business status. It is a basis for evaluating results and analysing business processes to determine the current status of the business.
  • Customer relationship management (CRM). Implementing a CRM system allows specialists working with clients to minimise the time spent entering information into the client database. More than 74% of companies claim that CRM software has improved their access to customer data.

Effective data management requires the development of rules and procedures that will determine what information resources should be collected, transformed, and sent to storage. Thus, it is necessary to ensure all participants’ maximum use of resources to achieve the most effective data management processes.

Future-Proofing through Scalable Data Architecture

Big data architecture is a comprehensive framework for efficiently managing, storing, processing, and analysing large and complex data assets.

The innovative technologies in the area of scalable data architecture involve the following approaches:

  • Process automation and continuous improvement. Software robots perform repetitive tasks, freeing human resources for more strategic work. This method is known as robotic process automation or RPA. Commonly used tools include Automation Anywhere, Blue Prism, and UiPath.
  • Machine learning (ML) and artificial intelligence (AI). ML and AI can be used to predict results, automate processes, and customise the user experience. Many AI and ML capabilities are available through tools like Microsoft Azure AI, Amazon SageMaker, and Google Cloud AI Platform.
  • Culture of continuous improvement. Promoting a culture of continuous improvement allows processes to be effectively assessed and optimised in response to feedback and data.

In recent years, the rapid growth of digital data has created new challenges and opportunities for businesses, researchers, and governments. As a result, industries realised the enormous potential of organised big data in driving insight, decision-making, and innovation.

Ihor Sheyko is the Managing Partner of Digicode.