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As data management matures, unstructured data evolves from being a storage cost center to sitting at the epicenter of value creation. To make use of unstructured data for competitive gain, it’s important to develop a strategy for managing it to meet the dual needs of cost efficiency and monetization. Here is a 5-stage maturity model to follow for organizations looking to modernize unstructured data management practices.
Enterprise data is growing – no surprise there. It is the current rate of data growth that is truly astounding. In 2010, the amount of data created, consumed and stored was 2 zettabytes, according to Statista. Firms like IDC have been predicting explosive growth overall in data over the next few years: from 64.2 ZB of data in 2020 to 175 ZB in 2025. That’s nearly three times growth in five years. Roughly 80% of all data is unstructured: file and object data including documents, medical images, video and audio files, design data, research data and sensor data.
By some estimates less than 5% of this data is being used for any purpose and enterprise IT teams have minimal visibility into their data and its value. So they store it forever, because that’s the safest thing to do. The end result: outsized storage spending and the inability to leverage data for new use cases and value. A recent Accenture study revealed 68% of companies are not able to realize tangible and valuable benefits from data.
Yet consider the opportunity: from real-time analysis of adverse events to inform patient safety measures and new drug development, early product defect identification in manufacturing, customer sentiment and chat analysis after a new product is released to improve go-to-market strategies or applying machine learning (ML) algorithms to real-time seismic data and satellite imagery to predict natural disasters. According to Forrester, organizations that take a data-driven approach to decision-making grow more than 30% annually.
To make use of unstructured data for competitive gain, it’s important to develop a strategy for managing it to meet the dual needs of cost efficiency and monetization. Here is a 5-stage maturity model to follow for organizations looking to modernize unstructured data management practices.
In this stage, unstructured data volumes are large and distributed across on-premises, edge and cloud silos, resulting in minimal visibility and few if any insights across the entire data storage ecosystem. In many cases, data is treated the same way: most or all data is on expensive primary storage and not being managed appropriately to save money nor meet the needs of distinct groups and workloads. Meanwhile, there is pressure from above to manage costs, moving away from data center sunk costs on hardware/maintenance to more flexible, on-demand cloud storage. Yet without the proper visibility into data assets, requirements and value, it’s difficult for IT and storage professionals to plan and manage effective cloud data migrations. Many will opt for a basic lift and shift approach, which may actually drive up costs further.
Key characteristics:
Dr. Daniel Bryant (Big Picture Tech Ltd)
Lian Li (Tilt Dev)
Emily Jiang (IBM)
This phase is characterized by a move to better control data storage costs by using the storage vendor’s own data management capabilities for unstructured data migration, replication and tiering. Storage-centric data management may be effective in environments with only one storage vendor, but most environments include multiple sites, additional vendors plus cloud deployments. Storage administrators are required to use disparate tools to migrate, replicate and analyze data within these storage silos. This approach achieves some cost savings but may not lower complexity, reduces flexibility and still leaves money on the table. If an organization wants to access the data after it’s been moved to the cloud through the storage vendor’s tools, IT must retain storage capacity and pay egress fees.
Key characteristics:
As an enterprise’s unstructured data reaches into the petabytes and beyond and hybrid cloud IT infrastructure dominates, the need to separate data management from storage management becomes apparent. Storage teams will look to adopt an independent data management approach—sometimes called a data fabric. Teams rely on analytics to look across storage silos and identify opportunities for savings. For instance, moving “cold” data not accessed in a year or longer to cheaper storage (such as in the cloud) frees up space on expensive, high-performing NAS storage.
Key characteristics:
Organizations in this phase move beyond cost savings to better support security, compliance and research requirements. Data policies and open data formats are critical. Organizations are automatically and continuously moving data to the right storage based on business priorities, cost, or monetization opportunities. For instance, an electric car manufacturer wants to understand how its vehicles perform under different climate conditions and so creates a data management policy to continually pull trace files from cars at regular intervals into data lakes and analyze them. Once the study is over, that policy retires and the moved data is deleted or moved to deep archive storage.
Key characteristics:
Some data sets contain value beyond the original application that created it. With advances in scalable, affordable services such as cloud-based data lakes and machine learning, business leaders are eager to see what their troves of stored data might deliver in terms of new insights benefiting R&D, operations and customer relationships. At this ultimate level of unstructured data management maturity, the new prize is managing data for long-term value. Capabilities include the ability to search across storage and cloud silos to find precise data sets and then move the data into cloud analytics environments for access by analysts and data scientists. Mature organizations can tag files with additional metadata throughout the lifecycle, enhancing possibilities for search and query. Storage teams work closely with business/departmental stakeholders to understand data needs for proper planning and long-term objectives.
Key characteristics:
Regardless of where your organization is on the maturity curve, it’s time to stop endlessly buying more storage without insight into the data and to stop treating all data the same. Instead, start analyzing and understanding data to manage it appropriately and by policy so you can fully leverage cloud storage and avoid waste. Start spending time on strategies to deliver greater data value including connecting with the data teams building new analytics infrastructure.
Krishna Subramanian
Krishna Subramanian is President and COO of Komprise.
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