How to Avoid the Data Silo Trap

The key goal of a data-centered architecture is data accessibility. Accessibility can impact future business innovation, improve the ability to generate metadata and new data sets, enable search and discovery of the data, and further empower data scientists to deploy said data for machine learning and AI.

If captured data is brought to a specific location to serve a single purpose but then fails to flow to other areas where it might provide value—coming to rest in a single location where it’s rarely accessed for other purposes—a data silo has formed. According to the Rethink Data report, enterprises report that one of the top five barriers to putting data to work is the difficulty of making siloed data available.

Data silos prevent companies from unearthing the full value of data at their disposal. Data goes unused or doesn’t get shared fast enough for distributed teams to analyze and make use of.

If you want the power to use your data your way, your organization needs to examine how disconnected its data is and implement storage and cloud infrastructure that easily transmits mass data where it needs to go at efficient speeds.

Here are five key areas data managers can focus on to prevent data silos from forming.

Deploy a composable cloud strategy

A single public cloud provider can give you integrated, end-to-end solutions for storage, network, computing, and applications. But it comes with natural limitations, including potential overspending on bundled services you don’t need, limits on how much data you can move and where, high costs to pull your data out, and interoperability issues with outside cloud services.

Instead, leverage multiple cloud service providers offering different services—such as storage as a service (STaaS), compute as a service, platform as a service, and software as a service—and design a multicloud that best suit your needs.

A composable cloud helps prevent data silos by enabling you to move and leverage your data to whatever location and service provides the most value. Companies that adopt a composable approach to infrastructure may outpace the competition by as much as 80%, according to Gartner.

Maintain a data lake as a central part of a composable cloud strategy

As data capture grows exponentially, so does data’s value—but only if all potentially valuable data is available for analysis.

By incorporating STaaS as central to your multicloud, you can move or copy most or all your data into a data lake where it can be assessed using data analysis software based on your criteria. Data curators and scientists use these tools to mine information from the data to provide to decision makers.

Ingesting most or all data into a data lake eliminates silos and allows connections to be made from seemingly unrelated data elements.

Build a frictionless data transfer architecture

Your data needs to be free to move as you see fit. As you choose various cloud providers, select partners who empower you to move your data freely—without bureaucratic hurdles and with low or no fees for data ingress and egress.

Then, employ a flexible and frictionless data-transfer strategy. Keep data active and bring it to where it is needed in the most efficient way possible. Different data transfer processes have different strengths that make them appropriate for different purposes.

Continuous or intermittent exchanges of smaller data sets may transmit regularly and almost instantly between colocated cloud services or quickly over distance via high-bandwidth networks. Very large data sets, even of multiple petabytes, can be transferred much more quickly using mobile edge storage devices and storage arrays. The difference can mean data is available in hours or days instead of weeks when using a network.

When considering network transfers, assess bandwidth and ability of the network to handle the anticipated data load at the speeds necessary to ensure data availability and time to analysis won’t be negatively impacted. Consider time to ingest data, time to access data, expected frequency of transfers, security levels required by the data, and costs.

When assessing physical data transfers, aim for mobile units that are designed with robust enterprise security protocols built in and seek out a data-transfer-as-a-service model that doesn’t restrict where you can move your data.

Develop a unified view of all your data

With data moving between multiple cloud services and physical locations, all of them should feel part of a unified data strategy—multiple parts designed to work together. To understand what kinds of data have been collected, where it resides, which is being processed, and why certain data needs to be transferred, you’ll want a unified data management system. Seek a software- or services-based solution that enables a complete view of all your data and data processes through a single pane of glass.

Unify your team’s approach to data

When organizations operate in silos, often different groups work toward their own objectives and in the process aim to keep control of data. This human element can cause data to be stuck in silos.

The solution to this people problem must start with the business owner’s strategy. That strategy needs to institute global standards, global data architecture, global data management, and equal access to the same analytical tools by global teams.

Leaders must make decisions about data via prescribed data governance and processes, including:

• Who has access to what data?
• How do we classify data?
• Which data do we keep and where?
• What do we do with data after it’s analyzed?
• How do we keep data available?
• How do we interconnect data to leverage it in unanticipated ways?

The result should be global tools, capabilities, and solutions that every group can leverage. With management of all data centralized, each team will be freed to make decisions based on insights from reliable, global, accessible pools of data. To learn more, visit us here.