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Datalogz Launches a New Solution to Solve BI Sprawl

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Datalogz launches a BI Ops solution that enables teams to bridge the gaps in their business intelligence environment.

"You could invest in cutting-edge business intelligence tools for your business, but without the proper monitoring, they will never deliver your money's worth.”
— Logan Havern
NEW YORK, UNITED STATES, September 22, 2022 /EINPresswire.com/ -- The leading innovator in the data and business intelligence sectors, Datalogz, has launched a new BI Ops offering to solve the colossal problem of BI sprawl for organizations.

Today, all businesses rely on reports created in their BI environment. As the data function grows within an organization, there are exponentially more reports being created by business users and BI users. Several teams can create reports, from finance to marketing to security. Very quickly, there can be thousands of reports, many of which can fall into the following buckets: compliance risks reports, duplicated data reports, bad data reports, or never used reports.

This mess created by too many reports is commonly known as 'BI sprawl.' It is a problem that emerges in most data mature companies. Too many reports are created by too many people using different tools and in too many different places. The reports often include contradictory or inconsistent data because it was generated from various teams at various times and levels of granularity.

BI sprawl creates some serious issues for an organization. For business users, it creates confusion and frustration. These users cannot completely trust their data and rely on intuition rather than facts to make decisions. Executive leadership can potentially be reporting bad data, which can have grave consequences.

For BI administrators, it is nothing short of a nightmare. Inefficient report creation raises maintenance and commute costs and poses security risks, potentially exposing sensitive data. All of this combined easily results in business intelligence tools being abandoned altogether, which takes away from organizational initiatives to be more data-driven.

Datalogz is the first BI Ops tool to bridge the gaps arising from BI sprawl within a company. Typically, as BI environments grow, teams have a high risk of reporting mistakes, security risks from data propagation in BI, and added BI costs from lack of optimization in the BI environment.

According to McKinsey & Company, data consumption of a large number of reports is one of the leading costs of a company's data expense, with 70% of companies having three or more BI tools! Datalogz bridges the gaps in Business Intelligence and can save companies between 30-50% of their data spend.

The New York-headquartered startup is the brainchild of Founder and CEO Logan Havern, who started the company back in 2020 after witnessing the inefficiencies in the data management processes at his previous workplace while working as a data analyst.

"You could invest in cutting-edge business intelligence tools for your business, but without the proper monitoring, they will never deliver your money's worth. As soon as we had this crucial realization, we pivoted towards the idea of BI Ops, aiming to create a solution that unlocked the true potential lying somewhere hidden within your business intelligence environment," stated Logan.

Datalogz solves for BI sprawl by plugging into the metadata and logs from BI tools to instantly identify duplication, misreporting, inefficient queries, anomalies in usage, and unwanted costs.

To learn more about Datalogz, visit datalogz.io.

About Datalogz
Datalogz is a young venture-backed company that has worked with Fortune 200s, airlines, banks, startups, and more. This positions Datalogz to serve and offer unmatched customization and service. The team comprises elite data entrepreneurs and governance professionals with over 30 years of combined experience. Plus, Datalogz offers strong consulting partnerships with experts in the Datalogz tool suite and governance to aid in any additional governance work and strategy.

Logan Havern
Datalogz
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