How Can Your Company Keep (and Use) All the Data It Collects?

Data capture capabilities keep growing, and companies want to turn everything they collect into business value. But is it realistic to expect to keep all the data your business collects? What are the challenges and solutions?

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We spotted it on the horizon and watched it get closer and closer. Now Big Data is with us, and we live in a world of data-fueled (and accelerating) innovation. Companies collect information in unprecedented amounts from sources that were once well outside the range of data capture strategies. Edge computing, artificial intelligence, and machine learning have ramped up the promise of data-based productivity and competitive advantage. To manage the amount of data being captured, business leaders must operate an almost dizzying array of tools, services, and cost models.

Data retention has taken center stage in many enterprises. Converting large amounts of data to insights can fuel innovation. Innovation drives results that can increase revenue and profits and put the enterprise in a highly favorable competitive position. The retention of high data volumes, however, comes with challenges. Cloud computing and its current face, multicloud, is not delivering on the promise of migrating data workloads on and off clouds and among different clouds in a frictionless manner and as needed with no lock-in, no concern of throttling, and no penalties.

Business leaders’ top concerns relating to collected data include:

  • Ensuring that needed data is collected
  • De-siloing data
  • Making data usable
  • Managing storage
  • Ensuring security

Fundamental questions underlie all these concerns: Can companies retain all the data they collect in a multicloud environment? If so, can they leverage that data to add business value? The answer to both questions is “yes.” It is possible to effectively retain all the data collected in a way that effectively drives innovation and success.

Considering Challenges

Issues around data retention fall into three camps: the tools used to transfer large amounts of data, the multicloud environment itself, and the costs connected to big data management.

Companies that capture a great deal of data from various sources at the edge, for example, sensors or cameras, can find it difficult to keep the data at the point of collection and then move it to the cloud or to data centers for storage, processing, analysis, and easy access. Some of the reasons for this difficulty include:

  • Insufficient capacity for the data sets.
  • Inability to move the datasets to the device in the allotted time.
  • Lack of quick access to data once it is moved.
  • Inability to fit the required frequency of data movement.
  • Not rugged enough.
  • Technologies that don’t work with operating system and cloud provider.
  • Inflexible cost structures.

Read Seagate’s Enterprise Data Transfer Playbook for more details about these challenges and options for solutions.

Keeping Control of Your Data in the Multicloud

The second challenge resides in today’s multicloud environment, which might more aptly be described as a “multiple cloud” environment with various, separate cloud-based data repositories.  Echoing the pre-cloud data environment, the clouds do not freely talk to each other. Data ends up siloed, and splitting data between environments can impede workload portability and increase compliance risks. 

Cost is the third major hurdle to tapping the business value of data. Many leaders find that data-related costs are volatile, making budgeting and forecasting difficult. Unexpected cloud costs after a migration include those related to data egress and ingress, as well as costs related to the number of API calls made by applications. High storage costs are also a challenge, forcing companies to make tough decisions about retaining or abandoning data.

Further challenges related to multicloud data management increase the challenges many companies face. The difficulty in scaling in the multicloud can add to data storage issues. Fragmented systems require minimizing weak links by ramping up security and resiliency.

Cost volatility and issues related to multicloud environments make it harder to get data where it is needed or where it can contribute to analysis and decision making. Add in the range of unsustainable costs associated with data movement and storage, and it is not surprising that companies use less than a third of the data collected.

Clearing the Way for Success

When considering a long-term data storage strategy from a strategic viewpoint, start with the data itself and how you need to engage with it. Consider these key points:

  • Maintain data ownership and control. Make sure you can process, use, and place your data where needed.
  • Access with no delays. Make sure your strategy allows fast, anytime data access.
  • Frictionless movement. Find solutions with no egress fees or API fees and no lock-ins. Make sure your strategy allows easy data ingress and egress. This applies to data within a network as well as the movement of large data volumes.
  • Cross-cloud flexibility. Work with cloud providers that allow you to choose the solutions you want.
  • Ease of orchestration. Orchestration brings different data, resources, and applications together to offer a clear, simplified system to meet a business need. Seek storage capabilities that support this action.
  • Simple, predictable pricing. Make sure that there is no guesswork or ambiguity in solution costs. Look for capacity-based pricing.
  • Top-of-the-line security. Look for stringent, globally adopted security standards to ensure maximum protection of your assets.

Seagate’s Lyve™ Cloud offers a simple, trusted, and efficient object storage solution for mass data that meets these challenges. Its predictable capacity-based pricing with no hidden fees for egress or API calls reduces total cost of ownership (TCO). Click the link to learn more about how Lyve Cloud can help you Unlock Data’s Potential with a New Cloud Storage Strategy.

These five actions have proven useful for executing effective data storage strategies:

  1. Think tools and training. Invest in the right technologies such as automation tools, visibility tools, and cross-environment storage platforms. Make sure that you are providing the training needed to manage the tools most effectively.
  2. Use a cloud cost estimation application. Third-party estimation tools help measure cloud resource costs before making a data placement decision.
  3. Be generous in developing criteria. Create a detailed set of criteria to apply prior to each migration to ensure that your tools will be able to meet time and access requirements.
  4. Monitor the environment. Continue to monitor requirements and capabilities to identify and address any changes over time. Include application and user environments.
  5. Automate security. Automation of security processes helps reduce the burden on personnel and may offer more protection.

Data Retention Can Mean Big Business Benefits

The goal of data retention is the conversion of data to insight which can then fuel innovation—ultimately resulting in increased revenue and profits as well as increased competitive advantage. This chain of effects has been borne out among highly innovative companies that have mastered data retention, at least to some extent. For example, the most innovation-mature organizations are better able to get data to developers quickly. In the area of data-driven innovation, companies that have implemented effective multicloud strategies are growing data-related revenue almost 1.6 times faster than companies still facing multicloud issues. In addition, such companies report:

  • Much faster time to market compared to competitors.
  • Launching significantly more products and services.
  • Driving higher customer satisfaction through product innovation.
  • Strengthening competitive position.
  • Increased customer wallet share.

 Solving the “all data retention” puzzle can contribute to results like these. Read Seagate’s Multicloud Maturity Report to learn more about effective cloud and data retention strategies.