The phrase “Data is the new oil” has become ubiquitous in recent years, with many experts believing that data is the most valuable resource in the world. And just as oil requires careful handling, storage, and refining to unlock its full potential, so too does data require careful management to unleash its value. If left unmanaged, it will remain nothing more than a raw material, without any value or purpose. But if properly managed, it can be transformed into a valuable resource, fueling innovation, growth, and productivity.
In order to guarantee proficiency, it is critical for businesses to strategically acquire, manipulate, and even dispose of data. Many companies have been spurred into action by the phrase “Data is the new oil”, resulting in initiatives to incorporate new data technologies such as big data and analytics. While these projects have generally been successful, they have not always been optimized as they could be.
This is mainly because companies still disregard the practice of data productization and instead concentrate on filling their databases with haphazard, conflicting, duplicate data that serves no purpose or has a low level of reliability. It is important for companies to make strategic decisions when acquiring, manipulating, and disposing of data. This will help them adjust to the new structure in a more organic manner without causing any significant structural changes.
As an example, consider a retailer that places a higher value on maintaining a precise stock level, measured by the ratio of stock to sales volume, rather than simply having a fully stocked inventory. A surplus of inventory can lead to significant financial losses over time as products become outdated or lose their value.
Therefore, it is critical to find a formula that ensures an ideal amount for a healthy sale while avoiding unnecessary losses from unsold products.
Data Productization: Turning Data into Business Value
To extract business value and capabilities from data, it is essential to understand data as an asset. Like any asset, data needs a life cycle with clear objectives. Therefore, a fundamental objective for the good use of these new technologies must be the conception of the “data life cycle” model, which balances democratization, access control, monetization, regulation, and security.
The data lifecycle consists of four stages: acquisition and capture, management and maintenance, backup and recovery, and retention or destruction. Each stage plays a critical role in ensuring data efficiency, security, and reliability. The following sections will explain each stage in detail.
Stage 1: Acquisition and Capture
In this stage, companies should clearly define their data types and sources. This ensures that the data being captured is relevant, reliable, and accurate. Companies should also establish a process for data acquisition and capture that includes data quality checks to ensure data consistency and validity. To achieve this goal, companies must define their data types and sources and establish a process for data acquisition and capture that includes data quality checks.
Acquiring the right data starts with understanding what data is needed and why. Companies must ask themselves questions such as:
- What business problems are we trying to solve?
- What data is required to solve those problems?
- Where can we find that data?
- How can we ensure the data is accurate and reliable?
After enterprises have a distinct perception of the data they need, they must pinpoint the origins of where that data can be retrieved. This may comprise internal data origins such as databases and enterprise applications, along with external origins such as social media, news articles, and public data collections.
It is crucial to note that not all data carries equal value, and some data may hold more importance than others, relying on the business situation. For instance, in the event of a vendor, sales data would hold more value than social media data when it comes to inventory administration.
Stage 2: Management and Maintenance
In this stage, companies should create governance and security policies to ensure data integrity, availability, and confidentiality. This stage also involves data cleansing and normalization to ensure data consistency and standardization. A robust data management and maintenance process ensures that data is secure, accurate, and accessible when needed.
At the heart of data management and maintenance is the need to maintain data quality. This involves ensuring that data is accurate, complete, and consistent throughout its lifecycle. This requires the implementation of processes and systems that support data quality, including data profiling, data cleansing, and data validation.
Overcoming data management challenges necessitates a comprehensive and well-planned set of best practices. While the specific best practices may vary depending on the type of data and industry, the following recommendations address the primary data management challenges that organizations face today:
Establish a Discovery Layer to Identify Your Data:
Setting up a discovery layer on top of your organization’s data tier enables analysts and data scientists to easily search and browse datasets, making your data more accessible.
Create a Data Science Environment to Efficiently Reuse Your Data:
A data science environment streamlines the creation and evaluation of data models by automating much of the data transformation work. A set of tools that eliminate the need for manual data transformation can speed up the process of hypothesizing and testing new models.
Use Autonomous Technology to Maintain Optimal Performance Levels:
Autonomous data capabilities utilize AI and machine learning to continuously monitor database queries and optimize indexes as queries change. This frees up DBAs and data scientists from time-consuming manual tasks and allows databases to maintain rapid response times.
Stay on Top of Compliance Requirements with Discovery
New tools employ data discovery to review data and identify the chains of connection that must be detected, tracked, and monitored for compliance across multiple jurisdictions. As compliance requirements increase globally, this capability becomes increasingly important for risk and security officers.
Ensure You’re Using a Converged Database:
A converged database is a database that natively supports all modern data types and the latest development models built into a single product. The best-converged databases can handle multiple workloads, including graph, IoT, blockchain, and machine learning.
Ensure Your Database Platform Can Support Your Business Needs:
The goal of consolidating data is to analyze it quickly and make informed decisions. A scalable, high-performance database platform enables enterprises to rapidly analyze data from multiple sources using advanced analytics and machine learning, allowing them to make better business decisions.
Use a Common Query Layer to Manage Multiple Forms of Data Storage:
New technologies enable data management repositories to work together, removing the differences between them. A common query layer that spans multiple data storage types enables data scientists, analysts, and applications to access data without knowing where it’s stored and without needing to manually transform it into a usable format.
Stage 3: Backup and Recovery
Backup and recovery are crucial to ensuring that data remains a valuable resource, even in the face of disaster or system failure. Without a robust backup and recovery plan, data can be lost forever, resulting in costly downtime and lost productivity. And with the increasing threat of cyberattacks and natural disasters, a backup and recovery plan is no longer a luxury but a necessity.
To guarantee the efficiency of your backup and recovery plan, it is crucial to adhere to the optimal techniques, which include:
Periodic Backups – Consistently conducting backups ensures that you always possess an up-to-date replica of your data that can be reinstated if an unexpected disaster or system failure takes place. The frequency of backups relies on the data’s volume produced and it’s level of significance.
Multiple Backup Destinations – Storing copies of backups in multiple locations certifies that you have a backup plan in place and lowers the likelihood of data loss caused by a single point of malfunction. Cloud-based backup solutions provide an exceptional option for multiple backup locations.
Evaluate Your Backup and Recovery Plan – Periodically examining your backup and recovery plan ensures that it functions correctly and that your data can be restored if an unexpected disaster or system failure happens.
Data Encryption – The process of encrypting your backup data offers an additional level of security and protects your data from unauthorized access if theft or data breach occurs.
Retention Policy – Establishing a retention policy guarantees that you retain backups for the required duration and dispose of them securely.
Stage 4: Retention or Destruction
The final stage of the data lifecycle is retention or destruction. In this stage, companies should create policies oriented to their business, considering their individual characteristics for each organization and sector. The retention policy should consider data regulatory requirements, data access needs, and data storage costs.
A comprehensive retention policy should consider factors such as regulatory requirements, data access needs, and storage costs. Retaining data can help organizations meet regulatory requirements, preserve historical records, and analyze trends over time. However, it also incurs costs and poses potential risks, such as data breaches and misuse.
Destruction of data is equally important, particularly when data is no longer needed or has become obsolete. The secure disposal of data can prevent unauthorized access, reduce storage costs, and protect privacy. There are many approaches to data destruction, each with its own set of pros and cons. You can physically obliterate storage media or make use of advanced data erasure tools to securely erase data. However, it’s crucial to align your data destruction method with legal requirements and industry best practices.
The Importance of Data Governance
For companies looking to increase agility and efficiency with data, having a solid governance strategy is non-negotiable. The fact is that if these companies do not radically change the way they deal with the governance of their data, the path to monetizing this “oil” will be further away from reality. At some point, the cost of this storage will make the data-driven strategy unfeasible.
To maintain data accuracy, businesses must establish governance and security policies, which entails creating rules and procedures for data management, including access, use, and storage. While some may consider this to be an unnecessarily bureaucratic process, the truth is that without adequate governance and security policies, data can quickly become corrupt, lost, or stolen. By implementing policies that ensure data is only accessed by authorized personnel, and that data is stored securely, businesses can protect their valuable data and ensure its integrity.
Data security may not appear to be a significant concern, but the truth is that anyone, regardless of their organization’s size, can fall victim to data breaches. By creating governance and security policies, businesses can minimize the risks related to data breaches and guarantee the safety of their data.
In conclusion, data is indeed the new oil, but like oil, its full value can only be unleashed through careful management.
