Data Modernization is changing the way we do business! But what is data modernization? Data modernization is moving siloed structured and unstructured data from legacy databases to modern cloud-based databases. It enables informed decision-making by pulling data out of systems more reliably and also helps you to identify high-value data combinations and integrations. However, a few companies are still using traditional data solutions. With the tremendous growth of data in today’s time, they face multiple challenges.
Failing to modernize the backup and strategies has cost many businesses millions and a lot of loss in terms of data too. A study from EMC in 2015 found that the data loss and downtime costs around $1.7 trillion annually”. Since data and users are becoming digital, that number is expected to increase as time goes on.
There are many problems with traditional data. Let’s examine the five key challenges in a traditional data warehouse approach:
Inflexible infrastructure: A lack of flexibility has always been one of the faults in traditional data warehousing. This is a specific issue in today’s unpredictable business climate. The app economy and consumerization of IT have made a culture where information is accessible on demand. IT architecture and data governance must be adaptable, enabling sudden decisions and frequent modifications. With an inflexible data warehouse, the request to modify a data model might take months and involve many people to implement a new policy data source. This costs users a lot of time, money and causes business delays. Thus, those with a traditional data model will struggle to remain competitive. Inflexibility is a common but severe challenge in data warehousing.
Complex architecture: To meet ever-evolving requirements in every data-oriented industry, many users tend to purchase add-on solutions, which creates a complex environment resulting in numerous data. Such architecture requires constant management and updates to ensure accuracy and consistency. There are many technologies that make complex infrastructure but lack integration across standard processes. This also results in increased cost of ownership, governance issues, and a lack of insight. Businesses with complicated or complex data warehouse architecture lack clear and actionable insights. This also affects their ability to make better and informed decisions. Many traditional data tools possess the same or sometimes similar capabilities.
Slow performance: Today’s users are generating access to more information than in previous years – and the volume is growing day by day. An overload of data in any business affects the platform’s performance and also results in delays in reporting. Most of the time, one expects on-demand and quick access to information, meaning it is more crucial to avoid interruptions to service.
Users are investing in app transformation to create ease, simplify and support newer products and services. More data means more information, which can prove complicated for traditional data warehouses. Many conventional data warehouses duplicate and fail to reuse the data, complicating the preparation procedure.
There are greater technical requirements and higher performance expectations, meaning data platforms today have to present more detailed and complex information. This can put older data under strain and negatively affect their performance. They also increase the time it takes for decision-makers to receive data in a consumable format after they have requested it.
Outdated technology: Many existing data warehouses are built on core platforms that are mostly rigid and cannot be updated. The platforms that deploy such technology are missing out on a multitude of data and analytical innovations. This is extensively problematic during high expectations and fierce competition. Outdated technology causes issues with software but also with hardware, including Processors. Outdated CPUs require frequent and significant upgrades to keep up with new and changing requirements. The challenge is that the processor is a component within an integrated server. It is difficult or even impossible to change without upgrading the entire platform and Memory.
Many traditional data warehouses use basic hard drives that struggle to meet the demands of increasing volumes of information and the complexity of user base and analytics. Data platforms need to ingest, modify and prepare information of increasing variety and volume – and they need to do so more quickly today.
Lack of data governance: Data governance is the exercise of decision-making and authority for matters related to information. It helps organizations to make decisions on how to manage and gain value from data while minimizing cost, complexity, and risk. In an age of growing regulatory and ethical requirements, it is more important than ever. Delivering effective data governance means creating a link between people, processes, and technology. If one of these is underperforming, the whole strategy or plan is compromised. Traditional data warehouses may not only struggle to support data governance; they may even undermine it.
To provide effective data governance, it is important to have agreed processes and then obviously visibility into those processes. Data warehousing is no different.
So how can enterprises avoid these challenges? The answer is Data modernization. Data modernization is moving siloed structured and unstructured data from legacy databases to modern cloud-based databases. Data modernization allows organizations to be agile, and reduce inefficiencies, speedy ingestion, data-driven insurance, real-time analytics, and unnecessary complexities surrounding legacy systems.
We all know that the future is increasingly data-driven. Without data modernization, data management, and data governance, businesses will have a difficult time accepting advanced analytics abilities or implementing emerging technologies. In order to enable new, modern data-based insights for the most competitive results, they require data modernization and deploy applications with existing and new data with strong data governance protocols.
The solution to all these data challenges is data modernization, designed with Rawcubes. Installing a modern data platform requires a partner like Rawcubes, who manages everything keeping the client’s goal in mind and is present throughout the process – from planning to implementation. We at Rawcubes will help you design and implement your data modernization strategy. Contact us to unlock the true potential of data modernization in your organization.