[{"innerBlocks":[],"name":"core\/freeform","postId":3491,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Data lakes are becoming the preferred platform for modernizing data environments, but many organizations continue to struggle with managing them.

Rising challenges in managing a swelling tide of information for many organizations is resulting in something more closely resembling a data swamp than a data lake, many data experts believe.

A growing number of organizations are using data lakes to either augment data warehouses or to serve as the enterprise data hub, says Ben Sharma, chief executive officer at Zaloni, a data management company based in Durham, NC.

Also See:<\/b> Phoenix Children\u2019s data lake helps improve patient care<\/a>

\u201cUnmanaged, poorly thought through data lakes are simply data swamps, and their usefulness decays over time,\u201d Sharma contends.

\u201cCompanies are realizing that they need more agile data platforms and deeper analytical capabilities to compete effectively in their market,\u201d Sharma says. \u201cThe major trend we see is organizations moving from sandbox or single-purpose big data applications to enterprise wide governed data lake implementations.\u201d

A number of other trends are emerging in several data-intensive industries, Sharma adds.

\u201cThe Internet of Things is a big topic. Machine learning is also on everyone\u2019s list. It is early stage, but as an industry, we are all looking for ways to leverage automated algorithms to improve our understanding of our data and to get faster insight,\u201d Sharma says.

\u201cWe are also seeing a real emergence of IT in the big data landscape,\u201d he adds. \u201cAs data lakes become more mission critical, organizations are looking to IT to provide the governance, security and automation required for these applications.\u201d

Perhaps the biggest challenge organizations are facing is \u201cfinding, rationalizing and curating the data from across an enterprise for analytics solutions,\u201d Sharma explains. \u201cThe ability to easily access data, refine data and collaborate on data needs continues to be a large roadblock for many analytic applications.\u201d

The need for improved analytics is growing within several industries, including healthcare, and that will force improvements in data management capabilities.

\u201cWhile there are increasingly powerful and effective analytics applications, the data management, integration and governance activities continue to be a major hurdle in rapidly making effective use of scale out architectures. For this reason, many organizations are still slow to adopt big data technologies in a production capacity,\u201d Sharma says.
","saveContent":"Data lakes are becoming the preferred platform for modernizing data environments, but many organizations continue to struggle with managing them.

Rising challenges in managing a swelling tide of information for many organizations is resulting in something more closely resembling a data swamp than a data lake, many data experts believe.

A growing number of organizations are using data lakes to either augment data warehouses or to serve as the enterprise data hub, says Ben Sharma, chief executive officer at Zaloni, a data management company based in Durham, NC.

Also See:<\/b> Phoenix Children\u2019s data lake helps improve patient care<\/a>

\u201cUnmanaged, poorly thought through data lakes are simply data swamps, and their usefulness decays over time,\u201d Sharma contends.

\u201cCompanies are realizing that they need more agile data platforms and deeper analytical capabilities to compete effectively in their market,\u201d Sharma says. \u201cThe major trend we see is organizations moving from sandbox or single-purpose big data applications to enterprise wide governed data lake implementations.\u201d

A number of other trends are emerging in several data-intensive industries, Sharma adds.

\u201cThe Internet of Things is a big topic. Machine learning is also on everyone\u2019s list. It is early stage, but as an industry, we are all looking for ways to leverage automated algorithms to improve our understanding of our data and to get faster insight,\u201d Sharma says.

\u201cWe are also seeing a real emergence of IT in the big data landscape,\u201d he adds. \u201cAs data lakes become more mission critical, organizations are looking to IT to provide the governance, security and automation required for these applications.\u201d

Perhaps the biggest challenge organizations are facing is \u201cfinding, rationalizing and curating the data from across an enterprise for analytics solutions,\u201d Sharma explains. \u201cThe ability to easily access data, refine data and collaborate on data needs continues to be a large roadblock for many analytic applications.\u201d

The need for improved analytics is growing within several industries, including healthcare, and that will force improvements in data management capabilities.

\u201cWhile there are increasingly powerful and effective analytics applications, the data management, integration and governance activities continue to be a major hurdle in rapidly making effective use of scale out architectures. For this reason, many organizations are still slow to adopt big data technologies in a production capacity,\u201d Sharma says.
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Why pressure will grow for organizations to manage data lakes

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