Self-Service Business Intelligence: How It Will Evolve in 2020

December 10, 2019 • Shannon Flynn


The trends currently driving self-service business intelligence provide insight into how the field will look in 2020. Data collection and analysis has ramped up in almost every industry. As a result, BI has transformed from a task for IT staff to a skill employees need in day-to-day work.

New self-service business intelligence platforms can be used by anyone, whether they have a technical or analytic background. These tools promise the democratization of this technology and may change how companies approach it.

The Rise of Self-Service Business Intelligence Tools 

Two shifts in the industry are driving the adoption of self-service business intelligence tools. First, the amount of data that businesses are collecting and analyzing is increasing at a rapid pace. Traditional business intelligence solutions and techniques aren’t built to handle this much. Instead, companies turn to platforms specifically designed to handle big data.

The second shift is the rise of commercial artificial intelligence (AI) applications. AI enables business intelligence tools to analyze big data. At the same time, it makes tools user-friendly without sacrificing analytic power.

Where Commercial Artificial Intelligence Enters

Many business intelligence tools integrate AI-powered natural language processing (NLP) features. Users can ask a question in natural language and receive a data-based answer. For example, with Microsoft’s Power BI, users can ask the program to show them something, such as top-performing products by sales revenue. Then, the platform will instantly generate a visualization of the data.

AI-backed features will transform business intelligence. Innovative tools can be used by just about anyone — even employees who don’t have data or technical backgrounds.

Most users of business intelligence tools — around 70 percent — are casual users with a limited skill set. They need tools that cover the basics and don’t require a technical background.

Businesses without self-service AI tools will continue to rely on analysts who have the technical know-how. As a result, the people asking questions won’t be the ones answering them. This process may prevent trial-and-error or investigative approaches to research. Skilled analysts without data or technical backgrounds won’t have the ability to toy with and investigate correlations and connections in the data. Therefore their skills might end up wasted.

Challenges of Self-Service Business Intelligence 

The pivot to self-service business intelligence tools could create new problems for businesses. Improper use of this technology could result in data becoming siloed off and inaccessible to other departments. According to one survey, only 22% of users have access to self-service business intelligence tools when needed.

Data sets can be prepared by individual analysts who aren’t in communication with the rest of a business’s analysts. As a result, you can’t build upon research and insights pulled from that data.

Business intelligence tools might make it more challenging to use data in a way that’s both ethical and legal. Harmful procedures or workflows could expose confidential or identifying data. As data breaches become more common, you’ll need to carefully guard company data. Not all employees are guaranteed to understand good data stewardship. Businesses will need to strike a balance between data availability and privacy regulations.

Predictions for Self-Service Business Intelligence 

New advancements in business intelligence — like artificial intelligence and big data analytics — make the tools easier to use for average employees. As a result, more people, even those lacking technical backgrounds, can use BI tools to find insights from large data sets.

Remember, however, that the technology may present new challenges. Businesses must consider data privacy concerns and the possibility of walled-off information. You can expect the use of business intelligence tools in business for years to come. How will the market shift as the technology becomes consumer-friendly?