Is Traditional Business Intelligence (BI) Too Slow to Fuel Innovation?

1x1.trans - Is Traditional Business Intelligence (BI) Too Slow to Fuel Innovation?

Is Traditional Business Intelligence (BI) Too Slow to Fuel Innovation?

It is often said that the key to producing fine wine is patience. The more time that goes into it, the better the result. While this may be true for winemakers, it is hardly true for employees attempting to pull reliable insights from their business intelligence (BI) solution. That’s because business leaders need informed answers sooner rather than later to better understand specific opportunities and maintain a competitive edge.

In past years, traditional BI was forced to move slowly because it was hamstrung by technical realities. These include an inability to assess data across multiple sources and platforms, steep costs of storing large amount of data and limitations to computing. This slowness and inability to quickly adapt is likely why BI projects have seen low adoption rates.

Thankfully, various workarounds have improved these processes. For instance, companies can force diverse systems to work together through things like APIs; the cost of data storage has dropped due to Hadoop, data lakes and cloud storage; and limitations on computing have been diminished due to the cloud as well.

Sure, the insights we garner from BI tools take a massive amount of input to generate findings. And companies still must first structure any data they want to use; then they must bring that structured data together in one place.

But that’s not all. Employees will then need to ask inquires of the data through a data analyst or team of data analysts. This can take weeks or even months of back-and-forth deliveries and questions to produce something that looks like insights. But when you take a step back it’s clear these assets have very limited value:

  • Accuracy – The accuracy of your findings might be impacted by old or outdated data. That’s a given. But did you know that the accuracy of BI is also affected by silos within your organization? If data isn’t being pulled across all aspects of your company, that you are probably missing the forest for the trees.
  • Depth – Generating insights through traditional BI makes it difficult to spot anything except the patterns and trends you were already looking for. The kind of unexpected insights that make this process truly valuable are exceedingly easy to overlook. In many cases decision makers only discover what they already know.
  • Speed – This is the biggest objection to traditional BI. Anyone who has slogged through this process knows that it takes days or even weeks to complete. By the time conclusions are ready, the value of those conclusions is limited or eliminated entirely. And since the process is known to be plagued by delays, many decision makers choose to forego BI entirely during “crunch time.”

However, concluding that BI delivers too little ROI to make it worthwhile is not advisable. There are enough examples of companies using analytics to produce actionable insights and gain competitive advantage to dismiss BI outright. The key is to conduct the process more effectively.

How you do that will depend on the scope of your data sets and the kinds of insights you are trying to locate. But if efficiency and productivity are the priority, one BI tool is essential – using machine learning for data analysis.

The reason that digital data has so much value for today’s business is precisely because the volume of it is so vast. Data is coming from countless different sources, accumulating over many months and years and relating to the most minute processes. Analyzing it exceeds a human scale. And only the right features, functions and algorithms are powerful enough to make that data accessible.

Optimal features could include a relational search engine that answers ad hoc questions quickly and accurately, or an automated machine-learning platform that delivers answers to questions you haven’t yet thought to ask.

But instead of focusing on the potential of smarter BI, think about the consequences of continuing with things unchanged. Data volumes are only going to grow larger and incorporate a wider range of facts and figures. Competitors are going to make smarter use of BI to secure their success over the long term. And the companies that continue to sacrifice staff and schedules to perform shallow levels of analysis will waste a lot of valuable resources.

The point is that if the traditional approach to BI does not seem too slow now it will be apparent soon. And by that point significant ground has already been lost to your competitors. The companies that are poised to reap the rewards of BI are honest about what is not working and actively implementing improvements and enhancements.

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