In this digital era, enterprises generate large amounts of data, which is initially unstructured. Every enterprise knows that data must be utilized to its fullest potential. It can be done by analyzing the data and extracting rich insights. However, data analytics will not provide the required results when the data is not optimized. The first responsibility of an organization is to manage and optimize data storage for big data operations. When data storage is not optimized, analysts do not have access to data at the right time. Once data storage is optimized, analysts can use AIOps solutions and big data techniques to make informed business decisions. Read on to understand how to manage and optimize data storage for big data.

Understand the data produced by your enterprise

The first step for any enterprise is to understand the data it is producing. Your enterprise might be producing a bulk of transactional data or customer data. Some enterprises produce large amounts of reference data or machine data. It is essential that you understand the type of data and choose storage solutions accordingly. Further, data analytics techniques are also decided based on the data type. For example, telemetry data require effective predictive analytics models to extract performance metrics. Once you understand the type of data produced, you will have an idea of what data storage solutions to use.

Analyse your requirements and choose the right storage solution

There are multiple options for storing large volumes of data in 2023. While traditional databases and warehouses are present, organizations also have access to cloud-based storage. Most companies have moved beyond traditional storage arrays in 2023. A few organizations rely on object-based storage to handle large amounts of unstructured data. Most organizations have opted for cloud-based data storage, which is a more scalable way. With cloud computing, you can use data storage services as you need. You don’t have to pay for the data storage section, which is not under use. If you are looking for a cost-effective and scalable solution for data storage, choose cloud-based storage.

Categorise data according to storage tiers

Some enterprises use different storage tiers and categorize their data. For improved performance and data analytics, you can also invest in data tiering. Frequently used data can be stored in the high-performance data storage tier. Similarly, data used once in a blue moon can be stored on a low-performance storage tier. It is crucial to note that high-performance storage tiers will cost more to the organization. With the help of data tiering, an enterprise can understand data that is used more frequently. The enterprise will use only those high-performance storage tiers that are required and nothing extra.

Remove data redundancy

Data redundancy can impact the performance of your predictive analytics models. Duplicate data might confuse the data models and analysts. As a result, accurate insights cannot be generated by analysts or predictive analytics models. There is a need to optimize data storage and remove redundancy before the data moves to analytical systems. After removing redundancy, you will see a significant improvement in data processing performance.

Use compressions techniques

Not every data will be used daily by predictive analytics models or analysis. Let us say a lender has loan data of customers that have already paid the loan. Also, the customers have surpassed the maximum age limit for applying for a new loan. Such type of data will not be used regularly, but the lending company cannot discard it. In such a case, the lending company can compress the data and store it in low-performance storage tiers. Data compression techniques help enterprises reduce data storage costs significantly.

Data storage compliance

By optimizing data storage, we also mean following the regulatory norms. Enterprises have access to client data, customer data, market data, and other sensitive information. Since the information is sensitive, a company cannot leave it unguarded. For the same rationale, every jurisdiction has some compliance norms for data storage. Ensure that your data storage systems are compliant with local laws. Also, implement security measures so sensitive information cannot go into the wrong hands.

Invest in data lifecycle management

You will have to keep an eye on the data from its creation to deletion. If your data storage systems offer high observability, you can gain control over data lifecycle management. Data lifecycle management also includes complying with regulatory norms. You must have a proper strategy to delete data when it’s no longer needed. It will help the organization save on storage costs and remove clutter.

Data lifecycle management also includes data archiving. Data archiving means moving data to a separate location, thus clearing primary data storage locations. It also helps enterprises to save on storage costs by moving data to a separate location. Data that is no longer required is only archived. It is crucial to note that data archiving is not like data deletion. You can restore archived data during emergencies or other requirements.

Monitor data storage systems and needs

Data optimization is not complete without extensive monitoring. An enterprise must keep an eye on storage uses to identify the need for scalability beforehand. Storage systems must also be updated and configured frequently to maintain the required level of performance. With the help of data storage monitoring, enterprises can ensure that data is accessible to different analytical systems. If the company needs more data storage, it can plan in advance.

Enterprises also must monitor the software systems used to optimize data storage. If any of the software systems fail, data optimization might go downhill. In such a case, enterprises can trust AIOps platforms to monitor data storage systems. AIOps platforms are capable of monitoring performance trends and determining anomalies. If something goes wrong within the cloud or database, you will be notified in real-time.

In a nutshell

When data storage is not optimized, data analytics might fail badly. Without AIOps solutions and improved analytics, an enterprise cannot generate rich insights. The enterprise will have nothing to make informed business decisions. For the same rationale, it is essential to use data archiving, data tiering, lifecycle management, and other data optimization techniques. Optimize your data for improved analytics right away!