Key Enterprise Data Management Trends 2022

by | Last updated Jul 1, 2022 | Published on Jul 1, 2022 | Data Processing Services

For any business, proper enterprise data management is important to keep the data safe and secure, so that it can be used for future reference. Providers of data processing services help in managing data by analyzing, organizing and storing it into a database. Having an effective data management system helps to reduce errors in data and set guidelines and procedures for usage of data to help businesses make informed decisions. Companies can respond well to market developments and customer needs more quickly with the help of accurate and current data. Keeping up with the latest data management trends gives business insights into customer behavior or market conditions, which helps predict trends, find opportunities, and stay ahead of your competition. Before going into the latest data management trends, it is important to understand the present scenario of data management in an organization.

What Is the Current State of Enterprise Data Management?

Organizations invest a lot of money in cutting-edge technologies, but data management is rarely a priority. Employees from different departments within the company are forced to juggle data with their other duties. For example, salespeople are cleaning data in the CRM rather than making sales, and finance staff are generating reports in Excel rather than doing data analysis. However, it seems that their principal duty takes a secondary position over their data-related duties.

Another issue is that, due to lack of knowledge, most of the businesses do not actively practice data management. As a result, they don’t fully understand the needs of their stakeholders and the desired business objectives before implementing solutions or developing analytical skills. Thus, technologies are not used, and data initiatives are unsuccessful. However, having an effective data management system will help companies to be more organized and productive, minimize data loss, and ensure security and compliance. Businesses can take the right decisions at the right time when data is properly managed.

Latest Enterprise Data Management Trends

The mounting pressure to implement the latest technologies such as artificial intelligence, machine learning, IoT etc. and improve the business’ ability to use the data to make more educated decisions, increase productivity, and support business expansion are two important considerations for businesses to keep up with the latest data management trends.

  • Cloud data infrastructure: Moving from a conventiona on-premises storage system to the cloud and more specifically to the public cloud allows businesses to free up constrained resources such as time, cost etc. for infrastructure maintenance, and have reliable data available whenever needed. Cloud-native storage platforms, which are based on advanced designs like cloud data warehouses, cloud data lakes, and the new cloud lake houses, offer effective and easily scalable solutions for both data management and storage. On the other hand, the availability of server less cloud services, infinitely scalable cloud computing, and turnkey cloud-native integration tools promotes a strong and vibrant ecosystem to meet the needs of enterprise data management.
  • Proper metadata management: Enterprise Metadata Management (EMM) is a critical element of being able to gain leverage over enormous amounts of organizational data acquisition. It drives timely and effective indexing solutions to help solve needs like what type of data is gathered, how data is structured, the source of data, where data is stored, the relation between data and business process and the connection between data. The operational data catalogue, which is an indexed collection of the enterprise data sources, is the basic implementation of EMM. The idea of “augmented data catalogues,” introduced by Gartner, takes things a step further by adding an automated layer powered by machine learning on top of the conventional data catalogue. Data discovery, connectivity, information enrichment, organization, and governance can all be streamlined due to the automation in augmented data catalogues.
  • Data Lakehouses: While the data lake deals with storage and flexibility pieces of the data management, enterprises now need to resolve to external ETL (extract, transform and load) processing for effective business intelligence insights and reporting. The idea of data lakehouses was developed to simplify this procedure and assist in maintaining the data infrastructure’s integrity and independence. It is a hybrid data management solution, integrating the benefits of both data lakes and data warehouses into a single platform to reduce complexity and upkeep while also taking advantage of economies of scale. Mixed-structured data can be ingested into the lakehouse similar to data lakes, but what sets it apart is the capability to layer warehousing on top of the lake. This makes it possible to keep the underlying lake flexible and dynamic for a wider range of other applications while still leveraging the rigidity and structured structure of a warehouse for conventional reporting purposes.
  • Data Fabric as a Multi-Modal Data Framework: It is obvious that modern businesses cannot continue to use a central, monolithic data management solution. A modern and comprehensive data management architecture that can support the development in complexity and scale of the various data producers, consumers, and applications and services in between is necessary. The multi-modal data management platform architecture that enhances data management design and practices is built on the foundation of data fabric. Three fundamental ideas form the basis of data fabric:
    • Coherence, making sure that the enterprise data management process is curated and arranged in a way that eliminates organizational and technical silos and unifies data management under a single platform.
    • Composability, enabling flexibility, scalability, and extensibility
    • Versatility: in terms of users, interfaces, and applications as a whole.
  • Data quality management with observability: The modern system for producing data is getting more complex with numerous potential points of check as the technical data infrastructure continues to be commoditized (or failure). As a result, it is more challenging to provide a solution to the seemingly straightforward questions of “what went wrong” or “how can we make sure nothing goes wrong”. Fortunately, the quality management process does not need to be reinvented for such complicated circumstances. The DevOps movement, which continues to develop and mature, from the lessons learned from the application of lean and agile approaches to software development is now being applied to enterprise data management. Data observability is another important pillar for establishing total and ongoing data quality management. The commonly accepted meaning of data observability in the context of data management is the capacity to comprehend the health and state of the data in your system, enabling data quality assurance and data lifecycle monitoring and control. While the three pillars of software observability in software engineering are logs, metrics, and traces, the five pillars of data observability are freshness, distribution, volume, schema, and lineage.

The majority of developing trends in enterprise data management relate to its technical and architectural components. Enterprises must evolve their capabilities of putting their data to work for boosting efficiencies, driving informed decisions, and fueling the company’s growth. So, collaborate with a provider of data processing services that can help you ensure timely, relevant and reliable data and also transform your business for the better.

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