The volume of corporate data is rapidly increasing every day. All this information needs to be collected, structured, and stored in a specific way. This article will discuss these aspects further. You will learn about the properties of corporate data and what to consider when digitizing and storing it. We'll also touch upon the possibilities offered by artificial intelligence.
Modern companies generate more data than they can efficiently process
About 20% of the data typically covers 80% of essential business processes. Companies must prioritize data processing to avoid spending excessive effort covering all available processes.
The first step is to select the right data and focus specifically on it.
There are several approaches to doing this:
Engage Adjacent Business Processes. This involves tasks performed by multiple departments or branches of a company. Common issues include data duplication or errors in data entry. Minimizing manual data processing can optimize team performance.
Focus on Collaborative Departments. When one team's process follows another, a long data transfer chain can form, increasing the likelihood of data exchange and processing errors.
Collaborate with Partners. Each company (counterparty) has its data formats and rules. During the integration of products from different businesses, ensure there are no duplicates and that the formats are consistent.
Digital Transformation in Business: Challenges and Approaches
Only 25% of companies are satisfied with their data management processes and infrastructure. This is understandable as businesses realize modern technologies' potential for improvement. However, only 44% of organizations have an IT team capable of implementing these optimizations. Nevertheless, two effective solutions for digitizing corporate data have emerged globally:
Traditional Approach
It is usually initiated by top management, who declare the need for optimization and turn it into a task for middle management. This layer identifies who will implement the idea. However, the lower levels often do not understand the importance of this task, slowing down changes and sometimes causing conflicts.
Initiative-Driven Approach
This is the opposite story. It all starts with the user's feedback on the product. Then, the employee who contacts the customer initiates changes in the processes in accordance with the feedback received. Communication with the audience helps to understand the user experience, how the product is perceived, and how to improve it. Therefore, it makes sense to encourage employees to take an initiative that goes up to top management. This level will make the final decision and allocate the budget for optimization.
Effective Data Management Requires:
Visibility/Accessibility
The speed and ease of data retrieval. Labeling can improve visibility, facilitating search and data processing, resulting in better reporting and visualization.
Reliability
Ensuring the data source's reliability is crucial for data management, processing, and integration. You need to clearly understand where the data comes from and ensure it is consistent and in the right format.
Security
Data security is vital for all businesses, especially (!) those handling personal, financial, or health information. Moreover, compliance with data storage regulations is becoming stricter.
Scalability
Automating repetitive processes reduces data duplication across departments, enhancing data consistency and reducing errors.
Corporate Data Quality
Data quality is often discussed in terms of organization, but for corporate data, it means value. To fully meet this criterion, such data should have the following properties:
Relevance: Regularly discuss with clients whether certain data is necessary for business development.
Completeness: Collect enough data to achieve business goals. Missing critical data can lead to analysis and further action errors.
Structure: Data must be structured for processing. For example, documents should be stored not just as PDFs but in forms accessible for analysis. Otherwise, it will be difficult to work with them.
Accuracy: This indicates how reliable and complete the data is for achieving business goals.
Creating or Buying a Data Solution
Businesses are at different stages of development and pursue various goals. Factors like budget, expertise, and security influence decision-making. For example, for established businesses, the time to develop a custom solution is not critical as improvements enhance user experience.
Startups, however, need to enter the market quickly. A delay of several weeks can lead to losses. Therefore, such businesses may benefit from integrating ready-made tools initially.
In any case, developing your own solution begins with choosing the type of storage:
On-Premise: This is the basic type of storage wholly owned by the business. All data is stored and processed at local facilities on the company's premises and usually does not go outside the company. Such storage may not be connected to the public Internet for enhanced security. Data is exchanged exclusively within the local network.
Cloud: Cloud storage provides a choice between Private and Public Clouds. Private Clouds are similar to On-premise, but the remote hardware is located in a third-party data center. You don't share data storage or server capacity with anyone. In the public cloud, the server is publicly available. Physical devices and resources are allocated using software tools (complete restriction of access or permissions to individual users).
When choosing a data storage, pay attention to its key characteristics:
Deployment
On-premise solutions require internal deployment and continuous integration managed by the company’s IT team, while cloud solutions often offer simplified deployment via service providers. For example, AWS, Azure, and Google Cloud Platform have many out-of-the-box solutions that make it easy to deploy to the cloud according to CI/CD processes.
Cost
On-premise servers have higher initial costs, while cloud storage offers lower start-up costs. In the long run, having your facilities may be more profitable. However, it all depends on the specifics of a particular project.
Control
On-premise solutions provide full control over data, while cloud storage places data physically elsewhere.
Security
The level of protection of cloud storage is lower than that of local storage (especially those not connected to the Internet). However, the main risks have been addressed today, and the clouds are up to standard. The storages of leading providers are used in sensitive domains such as healthcare, insurance, and finance. For example, Amazon's storage has a security level of 3+ with a maximum of 4. Therefore, this solution can be called protected as well as the local one.
Along with local and cloud storage, there is a compromise — a hybrid data storage format. Confidential data is stored locally, while all other data is sent to the cloud. This way, you get maximum security and control where it is critical, and the flexibility to manage your data in a way that meets modern requirements. The cost of the solution is also optimized.
Example: a BI Platform on AWS
A supermarket chain wanted to enhance marketing ROI and data visibility. This analysis required a BI platform, but resources were limited.
The action plan included a compromise — creating its own tool via Amazon's cloud services: S3 storage, data processing engines, lambda triggers, AWS Glue for data processing, and Redshift for data storage. This solution provided flexibility and cost-efficiency, supporting various business needs.
Data Security
If you look deeper at cloud providers, you'll see they have already built many basic security standards into their infrastructure. These include firewalls, data encryption systems, HTTPS protocols, antivirus software, user access control to their data, etc.
It is also important to mention data protection in the work environment:
Physical protection. First, this means restricting access to data warehouses and processing machines for company employees and any other persons.
Special software. It is important to protect software from malware, which will act even before the firewall and other systems.
Employee awareness. No matter how advanced a company's security system is, a person can harm its protection. Due to a lack of knowledge or improper habits, employees can always have a data leak.
Another issue is communication hygiene. Everyone knows about phishing attacks through emails with links to fraudulent websites. After clicking on the link, the attacker gains access to the user's data on their computer. Today, such attacks also occur in messengers. In corporate communication, chats have replaced other communication and data exchange formats. Therefore, information security standards should be extended to all platforms where confidential data may be stored.
Benefits of AI in Data Analysis
Today, AI is essential for gaining a competitive edge. A three-tiered approach is usually used here:
Data Level: The basic level where data is collected and prepared for further analysis.
Analytics and Development Level: At this stage, the employee analyzes the data and creates visualizations. For example, the person who initiates changes according to the initiative model.
Decision-Making Level: This is where the product is modified and optimized. The more data there is, the deeper the analytics and the more serious the changes can be.
In addition to descriptive analytics, when you can clearly see all the data, there is also forecasting. You can identify expected results thanks to advanced data processing and machine learning. For example, what maintenance may be needed or how sales will change in six months. This way, you get new data that is important for making the right business decisions.
Example: Enhancing RPA with AI Tools
A leader in the Robotic Process Automation (RPA) market faced data infrastructure issues and sought a way to optimize its system to enhance the competitiveness of its product.
The outsource team assisted in planning and implementing a transition to the cloud, introducing CI/CD pipeline automation. They also added innovative features to the RPA tools, including an automated smart analytics platform with dashboards that allowed users to filter data according to their needs. For instance, users could drag tables with charts and create specific environments for data analysis.
Additionally, an intelligent document processing system with optical character recognition was developed. This enabled the processing and structuring of previously unstructured corporate documents. The developers implemented a computer vision mechanism to capture User Flow. As a result, the system learned to replace these flows with automated scenarios. This relieved some company employees from the burden of manual work, allowing them to focus on more creative tasks.
In conclusion, effective data management significantly enhances business productivity and employee efficiency. Prioritizing data management and leveraging skilled professionals can result in a robust data management system that benefits the company.