Five Intelligence Myths Holding Businesses Back

Business intelligence (BI) and analytics tools have long held up the prospect of a time when data will be quickly accessible and turned into knowledge and insights that can be used to make choices both timely and accurate. That future, though, has not yet materialized for the majority. Employees rely largely on technical teams to comprehend data and derive insights from dashboards and reports, from the C-team to the frontline.

Why does traditional BI still not provide value after 30 years? And why do businesses keep making investments in a variety of instruments that demand advanced technical knowledge? According to a recent Forrester survey, 86% of businesses utilize two or more BI systems, and according to Accenture, 67% of the world’s workforce has access to BI tools. So why is data literacy still such a major problem?

The limits of current BI tools, in most use cases, prevent analytical forecasting from becoming accessible. These restrictions have helped to sustain several falsehoods that are commonly believed to be “truths.” Many firms’ efforts to use self-service analytics as well as their capacity and willingness to use data in critical decision intelligence have been hampered by such beliefs.

Myth 1: To assess our data, we must compile it all.  Due to BI’s constrained capabilities, conventional approaches to data and analytics call for compiling all of a company’s data into a single repository, such as a data warehouse. This integrated method calls for pricey hardware, pricey software, and, if using an analytics cloud, pricey computing time. Too many businesses continue to subject themselves to expensive, ineffective, difficult, and incomplete approaches to analytics because they are ignorant there are better ways to combine data and apply business analytics to make informed decisions.

Businesses use an average of 400 different data sources to fuel their BI and analytics, according to a report by IDG. This is a herculean undertaking that calls for specialized technology, software, and training. Any potential time savings these BI tools can provide are inevitably canceled out by the time and money needed to centralize data in an on-premises or cloud data warehouse.

In a direct query, the analytics are applied to the data rather than the other way around. Users can query the data without the requirement for pre-processing or copying. Instead, the user can use the provided database to directly query a few tables. This runs completely counter to the data warehouse strategy. However, the latter is still used by many business intelligence users.

Myth 2: We can’t examine our biggest datasets.  Data shouldn’t need to be fossilized and moved to the analytics engine because it already exists in real time as various, continuous streams of information. However, business intelligence relies heavily on in-memory databases that use this technique. The problem with this is that the largest datasets used by a company quickly become unmanageable or out-of-date.

Over the past five years, data volume, velocity, and variety have all skyrocketed. Organizations must therefore be able to regularly handle massive amounts of data. But it seems like an uphill battle because of the constraints of traditional BI tools, which rely on in-memory engines to analyze data.

Businesses can bypass in-memory engines’ inherent issues and gain access to larger datasets by going straight to the location of the data. Additionally, it makes a business analytics platform future-proof. Without having to completely rewrite code, direct query makes it much simpler to go from on-premises to cloud services like those offered by our partners AWS and Snowflake.

Myth 3: The organization’s data and analytics activities cannot be unified.  Common and recommended practices are frequently confused. Organizations frequently adopt department-by-department strategies, which results in ad hoc picks and combinations of BI tools that provide a cocktail of preference and functionality. Finance might choose one platform, sales might prefer another, and marketing might choose still another.

Before long, each department has its own set of tools, resulting in information silos that prevent the apps from communicating with one another or exchanging analytical data. Approximately 25% of businesses utilize 10 or more BI platforms, according to the Forrester survey previously mentioned.

The issue is that using different tools for data preparation, business analytics, and data science reduces productivity and extends the amount of time needed to transfer between platforms.

When managers give their departments the freedom to select their own strategy, some company areas do better. One of those is not analytics. Data must be trusted by decision-makers and leaders. However, each time trust goes through a new set of tools on the way to developing actionable insights, it gets weakened. Unavoidably, the process yields data conflict and opacity.

Myth 4: Pursuing the AI ideal causes us to become distracted from the practical aspects of running a business.  BI tools are among the many technologies that make the AI-driven promise. The promise is that machine learning will replace human labor with flawless efficiency; yet this is more often the case than not. As a result, many companies have given up on the idea of integrating AI into their routine analytical workflow.

It is understandable for IT workers to be pessimistic about the practical applications of pervasive AI in the workplace. People are still forced to manually organize and analyze their data, draw conclusions, and come to the best decisions. Synthesizing the peculiarities and thought processes of the human mind is difficult, if not impossible.

The secret to turning artificial intelligence (AI) into a useful, efficient analytics tool is to employ it in ways that assist common business concerns without isolating it from them. It’s crucial to understand precisely which AI-driven options you should employ. Although it may be sophisticated, any tool needs guidance and a steady touch to work effectively. Humans can use intuition, judgment, and experience in decision-making when routine tasks are automated. No need to be concerned about a robot insurrection.

Myth 5: We need a vast army of data scientists to fully utilize our data. The ability to compile enormous amounts of diverse data into usable insights is in high demand in the sector. However, firm management continues to hold the view that to analyze the hundreds of billions of rows of data that larger organizations generate, it is necessary to employ professional interpreters.

Data processing, modeling, analysis, and insight extraction are sought-after abilities. As a result, hiring data scientists with in-depth training in these fields is more expensive. But after a while, the value they add starts to wane. Additionally, these workers are no longer the only ones capable of doing data science. A new generation of corporate personnel has entered the workforce, and daily data assessment and manipulation is expected of them.

When non-technical business users have controlled self-service access to augmented analytics and decision intelligence systems, high-caliber data scientists may not always be necessary personnel. These users have crucial industry expertise and an awareness of the business’s decision-making process. A strong foundation of data and analytics capabilities, which traditional BI tools frequently struggle to deliver, is required to make their jobs easier for everyone to access.

In Conclusion

The world of business intelligence has long been plagued by several persistent myths that have hindered organizations from fully realizing the potential of their data. These myths have not only caused frustration but have also led to inefficient practices and missed opportunities.

In the end, it’s crucial for businesses to reevaluate these myths and embrace modern approaches to business intelligence and analytics. By doing so, organizations can unlock the true potential of their data, make more informed decisions, and stay competitive in today’s data-driven landscape. It’s time to leave these myths behind and embark on a data-driven journey toward success.