5 Business Intelligence Myths That Separate You From a Data-Driven Business

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For decades, business intelligence (BI) and analytics tools have promised a future in which data is easily accessed and transformed into insights and insights to make fast and reliable decisions. However, for the most part, that future has yet to arrive. From the C team to the front line, employees rely heavily on technical teams to understand data and gain insights from dashboards and reports. As the CEO of a data and business intelligence company, I've heard countless examples of the frustration this can cause.

Why, after 30 years, is traditional BI no longer delivering value? And why do companies continue to invest in multiple, fragmented tools that require specialized technical skills? A recent Forrester report shows that 86% of companies use at least two BI platforms, with Accenture finding that 67% of the global workforce has access to business intelligence tools. Why, then, is data literacy still such a prevalent issue?

In most use cases, the inaccessibility of analytics forecasting stems from the limitations of today's BI tools. These limits have perpetuated several myths, widely accepted as "truths". Such misconceptions have undermined many companies' attempts to deploy self-service analytics, and their ability and willingness to use data in critical business intelligence.

Myth 1: To analyze our data, we need to bring it all together

Traditional approaches to data and analytics, shaped by the limited capabilities of BI, require bringing a company's data together in a single repository, such as a data warehouse. This consolidated approach requires expensive hardware and software, expensive compute time if you're using an analytics cloud, and specialized training.

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Too many companies, unaware that there are better ways to combine data and apply business analytics to it to make smart decisions, continue to resign themselves to analytics approaches that are expensive, inefficient, complex and incomplete.

According to an IDG survey, companies use an average of 400 different data sources to power their BI and analytics. This is a herculean task that requires specialized software, training, and often hardware. The time and expense of centralizing data in an on-premises or cloud data warehouse inevitably negates any potential time savings these BI tools should offer.

Direct querying involves bringing the analytics to the data, rather than the other way around. Data does not need to be pre-processed or copied before users can query it. Instead, the user can directly query the selected tables in the given database. This is in direct opposition to the data warehouse approach. However, many Business Intelligence users still rely on the latter. Its time-consuming effects are well known, but people mistakenly accept them as the cost of performing advanced analytics.

Myth 2: Our largest datasets cannot be analyzed

Data exists in real time as multiple, fluid streams of information; it shouldn't be fossilized and moved to the scan engine. ...