Data is still essential to companies, but they can only rarely quantify or fully exploit the actual value of this data. The biggest obstacles include ineffective data management, rapid and sprawling data growth, and the lack of data security assurance.
Many companies are increasingly turning to cloud-based data lakes to get the most value from their data. Data lakes can contain hundreds of petabytes (PB) or more of data. A significant risk is that they can turn into a largely unused data swamp of static but potentially valuable data on storage media if left unattended. To prevent “data lakes” from turning into “data swamps” and ensure organizations can get the most value out of their data for business challenges, CIOs, CTOs, and data architects should consider the following four points.
Clearly Define Goals
Organizations can quickly identify the data to collect and determine the best machine learning ( ML ) technologies to generate insights with a clear goal in mind.
For example, with a bike-sharing service, the data from the sensors on the bike can be collected and stored in real-time in a cloud-based data store. All on-bike information (such as local services and bike status information) is visible to the operator of the platform and supports him in making decisions – for example, using the correct number of bikes in different areas, servicing broken bikes, when and where it is necessary – and ultimately to improve the user experience.
New data must be continuously fed into the data lake to achieve the best possible results with suitable software applications. Investments in storage infrastructure can thus positively affect the business results of many companies.
The More Information, The Better
Businesses need to be able to capture the correct data, identify it, store it where it’s needed, and make it available to decision-makers in a usable form. Targeted use of data begins with data collection.
However, with the rapid growth of data due to the proliferation of IoT applications and the introduction of 5G, many companies struggle to keep up and not capture all available data. After all, more and more companies understand that they should collect and, above all, store as much data as possible not to lose the current added value. This includes the value of the information that can be used today and that which will come with future applications.
There is also another positive development: In the early days of data lakes, it was reserved for power users to have a complete overview of the data lake and to find the correct data. With the advent of Structured Query Language (SQL), “normal” users also have more access to the data. For these users, the focus is more on the results. Artificial intelligence ( AI ) and machine learning (ML) help them filter the data and look for patterns. ML today enables near real-time analysis, advanced analysis, and visualization.
It is also important to transfer data to a well-managed cloud storage service in this context. This helps companies to share their daily generated data into a scalable data architecture. Like Seagate’s Live Mobile, high-capacity mobile storage solutions enable organizations to consolidate, store, move, and activate their data between the edge and cloud core. Such solutions also provide a faster way to physically move large amounts of data from one storage location to another.
Regular Evaluation Of The Data
Businesses need to review records they maintain in a cloud-based data lake regularly. Otherwise, there is a risk that it will become increasingly confusing and difficult to use, for example, when it comes to finding the patterns in the data you are looking for.
Deploying cloud storage services with AI and automation software is expected to significantly impact managing large data lakes and gaining insight into information. The best practice is first to select a dataset and analyze it using ML technologies. Once a satisfactory result has been achieved, the company applies this procedure to other data sets. For example, when detecting fraud in a bank, AI-based systems are developed that learn which types of transactions constitute fraud based on parameters such as transaction frequency and size and the type of merchant.
Data that is outdated or no longer relevant can be transferred to another data archive and retained. For this purpose, a company can use a data transmission service. It can move large amounts of data across private, public, or hybrid cloud environments, enabling fast, easy, and secure edge storage and data transfer and accelerating insights.
Mass Data Operations, or DataOps, is defined by IDC as the discipline of connecting those who create and consume the data. DataOps should be part of any successful data management strategy. In addition to DataOps, a solid data management strategy includes data orchestration from endpoints to the core and data architecture and security. Data management aims to provide users with a holistic view and access to and use of the data. This applies to data in motion and data that is “resting.”
Businesses today are generating large amounts of data, which Seagate’s Rethink Data report says will continue to grow at a compound annual growth rate of 42 percent from 2020 to 2022.
A new IDC study commissioned by Seagate found that organizations often move this data between disparate locations, including endpoints, edge, and cloud. More than half move data between storage locations daily, weekly, or monthly. The average size of a physical data transfer is over 140 TB. The faster organizations can move that data from the edge to the cloud, the faster they uncover insights and derive value from their data.
For the long-term success of data management strategies in companies, it is essential to keep the data active and thus avoid the creation of a data swamp. An active data lake provides insightful insights, laying the foundation for the success of digital infrastructure and business initiatives.