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3 Reasons Traditional BI and Data Discovery Tools Have Failed

By Charles Caldwell, VP of Product Management at Logi Analytics

The business intelligence (BI) industry has been challenged with poor user adoption for years. Yet, many CIOs continue to push BI as a core initiative.

According to Gartner, analytics and traditional BI is forecasted to shrink by 13% in 2021. Why? Successfully levering the full capabilities of business intelligence is still difficult to achieve, and product managers are searching for more. These individuals are looking for ways to expand the impact and value of their BI tools, but are lost about where to start.

The overall goal of BI and data discovery tools is to provide business teams with the proper data and information at the right time to support insightful, data-driven decision making. However, these solutions fall short and continually fail the industry through inefficiency, hefty costs, and an overall lack of value and insightful data production.

Here's a closer look at why traditional BI and data discovery solutions should face upheaval from organizations:

Inefficiencies through difficult processes

Currently, traditional BI and data discovery solutions force users to exit their current workflow to even attempt to secure any valuable data. When your team is operating in the middle of their workflow and needs data to inform a decision, they shouldn't have to exit the application to enter yet another application, gather data and then jump back in. The likelihood of delays in report deliverability also factor into this headache. This process dramatically slows down any workflow and causes frustration for employees, especially when the data secured isn't always useful.

Additionally, many BI and data discovery tools are not designed for business users, but instead more technical individuals within the organization. Traditional vendors often try to cover the complexity of their solution with self-service options and features, but users continue to feel like they need an advanced engineering or computer science degree to navigate them. This sucks up valuable time for non-technical users as they work to navigate a difficult platform to get the information they need.

Lack of insightful data production and overall value

The point of adopting BI and data discovery tools is to mine insightful data and information to make more informed business decisions. But what if the data you receive isn't always useful? This is an unfortunate reality when using traditional BI and data discovery tools.

Often when businesses adopt a BI or data discovery tool, the excitement fades after teams are handed reports that don't meet their needs and aren't useful to the decisions they're making. For some, the issue could be stemming from purchasing the wrong solution for their business - but, for others, this is just a common failing of these tools. Even if you have a firm understanding of your business's data needs and which information will help drive smart business decisions - and you purchase a solution that you think meets these needs - you could still come up short.

Another common reason for this issue is rooted in the fact that traditional BI and data discovery tools don't always get into the nitty-gritty details of data. If these tools can't drill down into deep data - and do so quickly - your organization won't be able to make true data-driven decisions when needed.

A case study from the Rubicon Project is a great example of this challenge. Rubicon was using a traditional BI tool that failed to capture details about any data produced during an auction in the efficient manner the business needed. Rubicon relies on data to analyze information from real-time bidding auctions that occur within milliseconds of each other, so delays just won't fly.

While the tool flagged major events like downtime, it failed to alert any outliers in customer data or other metrics with significant detail. This forced the analytics team to conduct manual analysis of the data, slowing down the entire workflow and missing important information and insights.

Failed cost optimization

A study from New Vantage Partners found that 55% of organizations have spent over $50 million on BI, with some reaching nearly $500 million. Unfortunately, implementing a BI tool requires businesses to adopt yet another application that, with inefficient workflows and lack of insightful data, don't bring the ROI businesses need.

When business leaders run into issues with their BI solutions, it's easy to chalk it up to operational difficulties at first and adopt add-on data discovery solutions or purchase more advanced capabilities to try to mitigate challenges. Unfortunately, throwing more money at the problem won't lead to a solution. There are fundamental issues with the tools that cause them to continually fail.

Instead of continuing to try to fit a square peg into a round hole and make traditional BI and data discovery solutions work, businesses should look to more modern technology like embedded analytics. Embedded analytics eliminates the need to add an additional application to your data tech stack and instead integrates analytic content and capabilities directly into business process applications. Embedded analytics goes a step further than traditional BI, producing details and predictive reports in real-time that don't require users to exit their workflow. Rather than continuing to rely on traditional BI and data discovery tools and spending more to try and solve the issues with it, business leaders should look to the future and move to more effective solutions like predictive and embedded analytics.



Charles Caldwell 

Charles Caldwell is the VP of Product Management at Logi Analytics, which empowers the world's software teams with the most intuitive, developer-grade embedded analytics solutions.   He has over 20 years of experience in the analytics market, including over 10 years of direct customer implementation experience. Charles writes and speaks extensively on analytics with an emphasis on in-app embedding, optimizing user experience, and using modern data sources.

Published Thursday, March 18, 2021 7:37 AM by David Marshall
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