What is Business Intelligence?
Business Intelligence is an important area of information system design that aims to extract business knowledge from large volumes of data. Business Intelligence (BI) has to be able to access the data of the organization and reports and data visualizations need to be available for users in their work environment. Users of the corporate data need to be able to analyze and understand the data for a decision-making process.
Biggest Challenge: Adoption
There are numerous challenges faced by an organization when it comes to business intelligence, but the one that remains the biggest obstacle is adoption. Watching an analyst make dashboards with pretty visualizations does look appealing but if a non-technical business user were to make it himself, it will easily take him/her 30 minutes to prepare something. And this is when they know exactly what to do and does not get distracted by the mouse and the keyboard.
This problem might be not clear to everybody as it may look obvious. Why should it be a problem to spend 30 minutes to get something done, if the task itself is hard? The difficulty behind this problem is that the “business person” concludes that he is not capable of managing the dashboard and leaves it to a technical person. The technology itself that he does not understand.
Let’s get a little philosophical…
The adoption challenge is so big that it’s worth getting a bit philosophical for a minute and thinking a little about what business intelligence really is. It can be defined as the production of knowledge to support decision-making at all levels of an organization. Business Intelligence (BI) uses data from many different sources to provide insight into the operation of your organization, by integrating data across all relevant systems and processing them to provide timely information for operational and strategic decision-making. But while it is true that business intelligence is a knowledge multiplier, what is the value of this knowledge?
BI’s goal is to provide new insights. Insights that are then used to identify better decisions for users, so they can stop guessing and start successfully planning and acting. Unfortunately, most of the time, users find it difficult to have quick access to insights and that is why adoption remains a major problem. As a result, it is crucial to successfully create environments that are easy to operate and that deliver business intelligence insights. That is the first step toward a successful adoption.
Organizations have already started achieving the initial steps to adoption. Some are making good strides in creating organizational structure around business intelligence and are applying a strong focus on key business metrics and the reporting of quick, actionable information. They are also applying a single version of the truth policy throughout the organization, something that is key to a successful rollout. But the progress is slow due to organizational reluctance and workload considerations that make the desired changes harder.
Break the wall
With this in mind, what should be done to instantly improve the adoption of business intelligence? One of the first things to do is work to break down the barriers between people and the information they need to make timely, effective decisions.
Typically organizations deploy complex BI tools that require the users to have some level of technical expertise to be used effectively. This causes a delay in decision making, as well as the need to train users and take other, more complex steps to prepare the tools. Not only is this labor-intensive, but organizational leaders also lack an understanding of the key metrics that need to be presented to the end-users and how the information should be formatted to be useful.
The result is the need to spend valuable time in deployment, and the inevitable organization-wide analytic and reporting problems that follow.
Say BYE to the traditional BI
Business Intelligence should be conversational, and users should be able to ask questions in such a way that the information is immediately provided. It should be simple, and based on the fact that business transactions are best understood by those who participate in them, the first step is to integrate the business transactions into the solution. It should be helpful, presenting only the information necessary without a lot of unnecessary “just in case” details. More importantly, it should adapt to the needs of the business and evolve in line with the organization itself.
Imagine a scenario where you are a business user and need a quick report for a board meeting. If you are a director of marketing, for example, you might want to compare the sales of products from last year with this year to see how sales have changed. To do this kind of analysis, you normally need IT professionals to run queries and generate reports in a data warehouse or on your CRM.
The traditional approach is typically reliant on batch processing the data. For instance, you will run the report and then wait for the IT department to provide it to you. If you want to compare with the other sales reports, you will have to manually or programmatically combine them together. In contrast, a modern and business user-friendly BI application like Conbi enables you to simply ask your questions and analyze data without having to be in front of a computer all day or wait for the IT department. Conbi uses conversational business Intelligence, which is a unique mix of Natural Language Processing (NLP) and Artificial Intelligence (AI).
Now let’s imagine you want to quickly create a dashboard and discuss the insights with your colleague to get his feedback. In the traditional way of doing this- you would need the IT department or your analyst to build the dashboard. Then you would have to log in to the data warehouse and load it onto your browser (most likely a PC or laptop). Take screenshots of charts from your dashboard and paste it into slack or a google slide which then requires you and your colleague to go back and forth. Sometimes, you would also have to re-make the report and repeat the same process again. That is why collaboration is key to data-driven decision-making.
Often users show their analysis to other team members or colleagues to get their feedback this helps in collectively deciding on the best course of action for the current business realities. This is one of the primary approaches of data-driven decision making and emphasizes the importance of data sharing, data management, and most importantly, data discovery – enabling data and information sharing across an organization’s departments. Most of the BI tools do have these capabilities, but it is often a multi-step process, one should look to integrate them seamlessly and without interruption.
It is all about reducing the gap between people and information. Although the BI tools were designed for self-service, many deployments of these solutions ended up being “report factories” where the semantic layer was used by IT experts or Analysts to create reports for others. This often reintroduced the bottlenecks and frustrations that self-service BI was supposed to get rid of.business intelligenceNLP
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