Data's role as the driving force behind strategic business decisions in enterprises is becoming increasingly prominent. Platforms like Tableau and Looker are enabling self-service business intelligence (BI) over cloud data warehouses such as Snowflake and Databricks. Now, business users are empowered to harness and understand data without being data experts themselves. However, several key challenges persist within the workflows necessary to leverage them. Generative AI, which can support natural language interfaces to business and data users, has the potential to address these challenges.
The core of the problem is inefficient communication cycles between the data and BI teams creating reports and the business teams that provide key context and goals. Information discovery, flexibility, and consistency issues are all challenges to optimal utilization and exploration of data.
Challenge #1: Information Discovery
In many enterprises, the process of finding the right information resembles an overwhelming journey through a maze filled with countless graphs and dashboards. The sheer volume and complexity of these options make it an uphill task to pinpoint the correct one, containing accurate, relevant, and comprehensive information. This is further exacerbated when different departments, or even different individuals, employ their own set of metrics and parameters, leading to a convoluted data landscape.
Teams can end up spending a disproportionate amount of time merely trying to navigate this labyrinth or develop a new, fitting graph or dashboard. Business users often need to carry context and information from one report to another to understand their needs. This often leads to an unnecessary duplication of work, the creation of redundant tickets, and an overall inefficient use of resources, which could have been better utilized in driving core business activities. The aim shouldn't be to replace dashboards but to replace the often time-consuming round trips with a data analyst who wastes their time servicing monotonous one-off requests rather than working on advanced analytics tasks.
Challenge #2: Flexibility
While dashboards serve as crucial tools for data representation and analysis, they're often limited by a notable degree of inflexibility. Once a dashboard is established, adapting the presentation format or even minor modifications to the displayed information typically involves another trip to the BI/data team. This not only disrupts the smooth workflow but also impedes the decision-making process, which relies on real-time data insights.
This rigidity can hinder quick responses to evolving business questions, thereby slowing down the overall pace of operations. To add to this, given the time and resource this process takes, many questions around data go unexplored leaving insights on the table. Enterprises often find themselves making a hard choice: either make do with what's already available, or spend precious time and resources in creating tailor-made in-house solutions.
Challenge #3: Consistency
Consistency remains a significant issue in the world of self-service BI tools, though it is critical to making business decisions. One primary concern is the data refresh timelines and information around how numbers are generated. If users aren't clear about how a metric was defined and when the data was last updated, they may question its accuracy and reliability leading to diminished trust in the dashboard. The underlying calculations for metrics, which are central to any dashboard, can also be opaque or subject to varying interpretations.
For example, in the context of a sales team aiming to extract insights, metrics can be significantly shaped by a range of factors such as geographic location, timing, customer categorization, and distinct promotional efforts. The definition of metrics and the validity of their parameters further complicate matters. There could be debates around specific measurements like whether returned orders should be included in the total order count, depending on the specific business context and objectives. These subtleties have the potential to give rise to ambiguity and varied viewpoints among stakeholders, leading to confusion or disagreements regarding the information being communicated.
Incorporating Generative AI
Acknowledging these pain points shouldn't be equated with accepting them as inherent to the data analysis process. A shift towards generative AI solutions are underway, and by leveraging a conversational interface users can intuitively interact with data, saving time and fostering a culture of data-driven curiosity. A conversational interface which will accurately answer business questions will address the above challenges. While generative AI models are critical, using a generic one-size-fits-all model will not suffice. The real strength is in using tailored solutions that align with an enterprise's specific needs.
Every enterprise operates within its unique ecosystem, characterized by specific business goals, industry norms, and operational dynamics. The adoption of generative AI and the use of a chat interface must be customized to individual business logic and metrics to ensure reliable and flexible data exploration. Only then can we fully leverage the power of AI to bridge the gap between data and insightful decision-making, ensuring that teams are aligned, informed, and ahead of the curve in an ever-evolving digital landscape. If you are interested in applying a customized generative AI approach in your enterprise to reduce wasted resources and missed insights, talk to Numbers Station today.