3 Common Limitations with Business Intelligence Tools and How to Fix Them with Data Science
If you’re like most modern data-driven organizations, you’re probably already using business intelligence tools such as Power BI, Tableau, or Looker to visualize various KPIs, trends, and other detailed information related to daily functions. When implemented correctly, these tools help you to quickly answer questions around what is CURRENTLY happening and make your day to day tasks and vital business decisions much more informed and effective. One of the most common limitations with business intelligence tools, however, is that they don’t often enable you to predict what’s likely to happen in the FUTURE.
While understanding your past and current state is a great start, I’ll let you in on a little known secret… adding predictive analytics and automated insights into your existing dashboards can give you better insights, predict future outcomes, and is not as difficult as you may think. Data Science tactics such as statistical modeling and machine learning make identifying what may happen equally as accessible as understanding what has or is currently happening. This allows you to respond quickly to changing conditions or get in front of potential business challenges.
3 Common Challenges with Business Intelligence (BI) Implementations
Typical BI implementations allow business users to easily consume data specific to their goals and daily tasks. The ability to analyze both past and present events unlocks information about the current state and is essential for remaining competitive in today’s data forward market. With that in mind, there are some common limitations that many organizations encounter when relying on these tools alone.
Limitation 1: Useful Insights, Trends, and Patterns Arise only When Looking at the Right Data, within the Proper Context.
The good news is that a modern data warehouse eliminates the risk of reporting on inaccurate or untimely data by organizing information in a manner that enables fast and reliable reporting. That being said, you must also rely on your business users to ask the right questions to develop helpful reports. This often results in a delayed discovery of vital insights and overlooking key data. Additionally, you have higher chances of missing key insights due to human error and the inability for efficient reporting to fully cover all segments of detailed data. Even the best dashboard can exclude important information since they focus solely on specific business questions.
For example, at a logistics company, dashboarding shows every detail around the supply chain and warehouse inventory. With so many variables that could affect the timeliness of your orders (number of employees, truck availability windows, congestion in areas of warehouses, etc.) it’s nearly impossible to combine all of the information and see the bigger picture in a timely manner. Especially when changes are happening in real time. It is also hard for a single person to separate the individual events from the overall effects. With machine learning you can ingest large amounts of data to identify orders at risk of being late based on key variables. Using statistical techniques, you can differentiate the sources of inefficiencies by cutting through all the noise in your data to find systemic issues.
Limitation 2: There is a Reliance on Static, and sometimes Arbitrary Business Rules.
Many effective dashboards use benchmark metrics to show if a department is doing well or not. For example, a sales organization has BI Tools that use data to track engagement with their leads. Under their current business rules, a lead is considered “cold” if there hasn’t been communication in 5 days. When a lead goes cold, the sales and management teams are alerted so that action can be taken to re-engage the lead. A good dashboard would present in some way the number of cold leads and the number of leads at risk of becoming cold. But how do you really know that 5 days is the appropriate amount of time? What if millions of data points show that leads are likely to go cold if you haven’t contacted them in 2 days? That could be a lot of missed opportunities. In such a fast changing environment, sometimes even based on the context of your business questions, business rules set by people may be misunderstood, inaccurate, or outdated.
Limitation 3: Since Most BI tools Utilize Historical Data, They Lend Themselves to Highlighting Past Events Rather Than Future Ones.
Visualizations based on this information are framed around questions of what has or is happening. While there is no doubt that understanding the past is essential to improving future decision making, adding on a layer of predictive analytics would enable a culture of reactive data driven decisions to shift towards more forward thinking and innovative choices.
Using advanced analytics to look toward the future is a practice all businesses should employ. To exemplify the significant impacts this practice can have, we will look to the healthcare industry. Many medical providers track their readmission rates, how often a patient returns with related health problems after they have been discharged. This metric helps evaluate the quality of care among other factors. Using data science, they can zone in on certain subsets of patients who pose a high risk of readmission. This provides healthcare providers with real-time knowledge of their most at risk patients, allowing them to make proactive actions so that their patients leave healthy and with less chances of readmission. This proactive approach is much more effective than looking back at historical data to later figure out which subsets of patients had higher readmission.
How to Solve these Challenges with Data Science
Upgrading Your BI Tool with Data Science Enhances Your Current Information by Answering “What Will Happen”
Data science helps businesses extract insights from large amounts of data and create outputs to automatically detect significant changes that may arise from patterns spotted in data. In many cases, it is because of the benefits of data science initiatives that companies begin to see significant ROI on their data investments. This is because data science better equips you to:
- Make predictions for future events based on trends in historical data
- Detect significant changes in business events and determining their outcome
- Assess potential results of business decisions
- Analyze broad sets of data with many inputs to find key insights
- Understand data points that impact the whole company rather than a specific siloed department
While the benefits of data science are undisputed, for many organizations, data science initiatives seem unapproachable. Whether it’s because your data science team finds it difficult to consistently communicate insights, there is a lack of understanding as to how a prediction is being reached, or simply not knowing where to start because the process seems so large scale, your company is not alone. One of the easiest ways to tackle these barriers is to combine your current BI tool and analytics practices with data science.
4 Benefits of Adding Data Science to Your Business Intelligence
Combining data science and business intelligence helps overcome many of the previously discussed challenges of both data science and BI tools by:
Incorporating Data Science Findings with Current BI Tools
This enables consistent communication of data science trends in a clear cut and approachable manner. Rather than requiring a data scientist to explain each output via a presentation, it can be communicated clearly to business users through a tool they are already familiar with through clear visualizations. This drives higher levels of adoption by business users and can help data science teams find additional use cases to bolster trust and collaboration.
Answering Questions of Trends and Patterns with Quantifiable Values
As mentioned before, the use of arbitrary and static business rules can have a major, negative impact on your decisions. Data science allows business users to identify dynamic and definite metrics against which they can measure success. Incorporating these insights with existing dashboards and benchmarks furthers the value of this form of decision making.
Enabling Dashboards to Pull Key Information from a Broader Data Set
Ingesting through big data, and picking up patterns from the deepest level of business data through data science practices increases operational efficiency and insight generation. Data science algorithms learn from the deepest level of historical data, then forecast on new data to detect the most relevant points to communicate to business users in real time.
Shifting the Focus of Certain Dashboards to be More Forward Looking
By integrating the ability or results of using custom built forecasting models with existing dashboards, business users can compare the current trajectory and history with the predicted future values. This enables forward thinking decision making and a natural platform to apply advanced insights to existing analysis.
Getting Started with Data Science Enhanced Business Intelligence
It’s hard to believe how accessible it is to gain predictive insights by updating your BI Tool with data science capabilities. If you have a strong strategy already, it just takes implementing a data science and a bit of dashboard reworking. Aptitive has helped numerous organizations across multiple industries gain the benefits of data science through similar solutions using BI Tools like Tableau, PowerBI, Looker, and more. Whether you have ideas on where to start and want to incorporate your results with your BI Tool or you need help understanding how to best incorporate data science, reach out to our consultants at Aptitive to unlock these insights as quickly as possible.
Originally published at https://aptitive.com on October 20, 2020.
