Solutions Review’s Expert Insights Series is a collection of articles written by industry experts in enterprise software categories. In this role, Jens Graupmann, Exasol’s SVP of Product and Innovation, provides key ways to optimize data management strategy in the current circumstances.
A robust data management strategy is needed to support business goals and initiatives and empower data teams and leaders. However, IT teams are increasingly realizing that many data management approaches that worked in the past no longer suffice in today’s data-driven environment, leading to missed business and digital transformation opportunities. In a business landscape where data is power, organizations must adopt new data management strategies, architectures, and tools to better leverage the influx of data.
In this article, we look to the future of emerging data analytics trends, solutions, and approaches, and how companies can optimize their data management strategy today to be future-proof for 2023 and beyond.
Mastering data mesh amidst the hype
The modern enterprise is evolving its approach to data management, with companies thinking about how they manage, distribute and use data with their teams. As such, companies are shifting from traditional, monolithic processes to embracing new technologies and strategies to become more data-driven. For example, we are seeing the adoption of a data mesh architecture, which many organizations see as an answer to their ownership, quality, governance and access challenges. Datamesh advocates the shift to a more decentralized and distributed architecture that fuels a self-service data infrastructure and treats data as a self-contained product.
In 2023, we expect even greater pressure on organizations to move faster and build resilient, flexible data architectures that will push data teams to data mesh deployments. However, despite the growing enthusiasm around data mesh, we expect many implementations to hit roadblocks due to misinformation and overhype surrounding the concept. Companies need to approach data mesh thoughtfully and be willing to learn and better understand and execute the concept.
To ensure companies can adopt data mesh at scale, the first step is to eliminate misinformation. There has been a lot of debate and confusion about how to avoid datamesh from exacerbating data silos, and whether datamesh and datafabric are actually the same thing. To overcome these challenges and move past any debates or uncertainties, companies must take responsibility to educate themselves to strengthen their understanding of what a data mesh is and how it can optimize their data management strategy.
Moving to a metadata mindset
Beyond data mesh, metadata is emerging as another critical piece of the data management puzzle as organizations look to accelerate the time to value of their data, optimize costs, and comply with the ever-evolving landscape of industry and government regulations. As organizations continue their cloud migration efforts, the role of metadata in the data ecosystem will continue to grow. At the same time, increased interest in data discovery, governance, virtualization, and catalogs, and the need to accelerate data delivery through data pipelines and warehouse automation, are driving greater demand for metadata.
Despite its usefulness, metadata is still often overlooked and underestimated in data analysis and data management. Businesses should heed this as it alone could sabotage your data management strategy. Metadata form the basis of high-quality data. It provides information about other data so that it can be more easily understood and used by the organization. It answers the who, what, when, where, why, and how questions for data consumers and is critical in building a robust data management strategy.
Metadata management also plays a major role in supporting data management programs. Looking ahead, modern business teams should embrace a metadata mindset – and expect metadata-based data management to emerge from the shadows as a focal point in the marketplace.
Avoid a deluge of data with edge analytics
Our digital-first world is changing the way business is done. As data volumes increase, it is impossible to analyze the critical components of an optimal data management strategy without assessing the capabilities of a centralized data storage infrastructure. Today, the amount of data created pales in comparison to the bandwidth available to process and evaluate this plethora of data.
The future of data analytics lies at the edge – a type of decentralized data analytics – where data can be analyzed at its source (i.e. the “edge” of the information network). By allowing raw data to be analyzed at source, edge analytics avoids the need to transfer data back to a central system, while still bringing all insights together for centralized decision making. This is the solution for businesses that want to increase their speed to data and support higher quality analytics with lower latency and scalability. Additional benefits of decentralizing and applying edge analytics include improving decision making and business outcomes. And with the push for businesses to become more data-driven, it can be costly not to explore all options.
Drive ROI through effective data management
Data management tools are proven to deliver positive results: 58% of companies using these tools are more likely to meet their revenue goals than non-data-driven companies. And data-driven organizations have an even higher chance of exceeding their revenue targets than non-data-driven organizations. Today’s modern businesses understand that data is their greatest asset. Looking ahead, they will continue to rely more heavily on real-time access to their data and analytics, especially as companies restructure to ensure universal access to data across the organization. As we move through 2023, the organizations that now embrace a data-driven mindset and invest now in effective data management strategies will be best prepared for success.
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