Business Intelligence: Challenges and Opportunities

The most successful organizations support their decision making with business intelligence systems at every level, from strategies to daily operations

Digital transformation is the trending topic that everyone, from companies to academia, wants to address. Now, nobody doubts that disruptive technologies transform business models. This conviction is based on our everyday experience: you just need to subscribe to a digital service or buy something online or carry out a digital transaction to realize that the way business is done is no longer the same, and that companies have to evolve in order to survive in the Digital Era.

Business Intelligence (BI) is one of the pillars of this digital transformation. Even though this concept emerged in the 1980s and was popularized in the 1990s, it remains current and its value has grown exponentially hand in hand with disruptive technologies. Rather than being a specific tool, BI is a broad concept that encompasses good practices, methodologies, processes, human capital and platforms than enable relevant data collection, storage and analysis to support decision-making processes in organizations. 

Although the Digital Era has generated an enormous, highly varied, at times unstructured and extremely dynamic information flow, data generation, storage, visualization and analysis, opening up significant areas of opportunity. The principal challenge is to design BI systems that effectively and efficiently assist in decision-making processes and generate specific value within the organization.

A common error when designing BI systems is to focus on the available information rather than the necessary information. Another mistake is to place more importance on the BI tool (visualization and exploitation) than on analyzing the data and model needs related to decision-making processes, particularly those that really add value for the organization.

As an EGADE Business School professor and business intelligence consultant, I have identified three major challenges faced by organizations today when implementing BI systems:

  • Lack of clarity of the true purpose of intelligence systems. It’s not about generating a large data warehouse or a colorful, spectacular-looking control panel, but rather trying to obtain relevant data and perform a reliable analysis to support decision making.
  • Consider all data sources, especially external ones. Most projects become a data push offering, generating a large number of boards with information available in the central systems, instead of the information the decision maker actually needs. A rigorous analysis is not performed to determine whether external data (customers, competition, suppliers, market, etc.) are more important than internal data (sales, inventories, collection, etc.).
  • Independent management of BI projects. Very often, the areas responsible for transactional systems handle a very extensive project and improvement portfolio, are swamped with a lot of work and have to deal with late deliveries. Intelligence systems should be executed by specialists, respond rapidly and undergo constant improvements.

The most successful organizations support their decision making with BI systems at every level, from strategies to daily operations, generating competitive advantages based on the use of valuable data. The three most interesting areas of opportunity to capitalize on business intelligence are:

  1. Know your customer: Digital transformation offers a solid platform of applications that make it possible to collect information on our customers. A well-designed digital platform unlocks a continuous channel that generates valuable information on our customers’ likes and preferences, their level of satisfaction and their purchase intent. As customer profile data are obtained, control boards can be designed to help marketing teams apply solutions and services one by one.
  2. Production and route scheduling optimization: Technology connects not only customers, but also all the machines that produce goods within a plant. Large amounts of production process data are collected through sensors, in such a way that intelligence systems can detect areas of improvement in real time and generate optimized production schedules. Predictive maintenance is a reality: equipment failures can be forecast and their root causes easily detected with the appropriate models. The real-time connection of distribution equipment, for raw materials or finished product, can also optimize the fleet, in equipment and locality, delivery routes, fuel consumption, etc.
  3. Human resource analytics: The Digital Era demands highly trained and motivated human resources. Each generation of collaborators has its strengths and weaknesses. BI systems make it possible to collect data on the activities completed by collaborators and manage their performance in real time. Tools are now available to talent management areas for identifying the most productive profiles and for correlating work schedules and working conditions, leading to the design of better incentive systems.

This list of areas of opportunity is by no means exhaustive, but offers a small example of what can be accomplished with the appropriate design, development and implementation of a Business Intelligence System. Despite the challenges implicit in this type of initiatives, the outcomes can far surpass any efforts and investments. As a result, the companies at the forefront of the Digital Era have focused on developing data collection and analytics capacities. In short, such capabilities are the new way of competing in the marketplace.

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