Organizations aiming to adopt data analytics should be mindful of myths that could harm their data-driven business
Business intelligence (BI) and analytics tools have long held up the prospect of a time when data can be quickly accessible and turned into knowledge and insights that can be used to make choices that are both accurate and on time. However, that time has not yet come for the majority of people. Technical teams are widely relied upon by employees, from the C-team to the frontlines, to comprehend data and derive insights from dashboards and reports.
The limits of current BI tools, in the majority of use cases, are what prevent analytical forecasting from becoming accessible. Several myths that are commonly believed to be “truths” have been sustained by these limits. Many firms’ efforts to use self-service analytics as well as their capacity and willingness to use data in critical decision making have been hampered by such beliefs.
- We must gather all of our data together to assess it: Due to business intelligence’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 that there are better ways to combine data and apply business analytics to them to make informed decisions.
- Analysis of larger datasets is impossible: 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 The problem with this is that the largest datasets used by a company quickly become unmanageable or out-of-date. 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, the direct query makes it much simpler to go from on-premises to cloud services like those offered by our partners AWS and Snowflake.
- Within the company, data and analytics activities can’t be coordinated: 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. When managers give their departments the freedom to select their 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. The method invariably leads to data opacity and disagreement. Understanding and consistency are essential.
- Pursuing the AI dream causes us to become distracted from the practicalities 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. 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 talents you should employ. Humans can use intuition, judgment, and experience in decision-making when routine tasks are automated. No need to be concerned about a robot insurrection.
Due to the unreliability of legacy BI tools, businesses lack confidence in AI-driven operations. Because their departments don’t share the same analytics vocabulary, they lack confidence in democratized access to data. Furthermore, they have little faith in their data because in-memory engines aren’t growing to the required level, leaving them with insufficient and thus unreliable data. In the age of digital transformation, corporations offer value through data and business analytics innovation. To innovate, however, you must be aware that your walls are permeable