Business intelligence and analytics dashboards are valuable tools that have long helped businesses monitor their performance.
In most cases, these tools do exactly what they are supposed to do: pull data into an easy-to-use interface, so that decision makers can view business metrics across different departments at a given point in time.
But that’s where dashboard capabilities usually end.
To stay competitive, businesses need to move beyond snapshots of current or historical performance and instead look to the future. That’s where data science comes in.
BI Dashboards Don’t Predict the Future
Business intelligence (BI) is, by definition, backwards facing, while data science looks to predict the future of your business and customer interactions through complex data modeling techniques. When a BI analyst attempts to predict future events, he or she is usually making a best guess based on past occurrences.
Similarly, a typical BI dashboard is intended to consolidate and arrange data into visualizations — not as a tool for conducting analyses or predicting business outcomes. But as data science grows in popularity and big data becomes increasingly prolific, most companies are moving away from guessing at future revenue and profit using dashboard reports.
Instead, BI and data science teams are working closely together to make businesses more data driven.
BI analysts lay the foundation for data science by aggregating, filtering and transforming data into a more digestible format that feeds into BI dashboards. In turn, a data scientist leverages that data to quickly and easily build models — and ultimately predict future profit or revenue.
For instance, a BI analyst might tell you how much revenue a certain customer segment brought in last quarter. A data scientist, on the other hand, could build a predictive model to tell you, based on certain features, how much revenue certain customers are forecasted to generate over the next year.
BI Dashboards Don’t Show Why Your KPIs Change
In the same vein, the typical BI dashboard will show you key performance indicators — like profit or sales — but won’t tell you why those KPIs are changing. And without insight into those trends — and some experimentation — it’s nearly impossible to make informed decisions about how to change them.
Let’s say your dashboard shows a dip in revenue in a given part of your customer base. While it’s nice to have that information, you have no way of knowing why that dip is occurring, or what you can do to mitigate it. For that, you need data science.
A data scientist can identify relationships in your data that give insight into what drives those trends.
By building a data model that is attuned to the features of your business, he or she will be able to tell you what experiences might cause certain groups of customers to buy more — for example, applying targeted marketing efforts based on how frequently a customer actually shops will have a more positive effect than inundating them with unwanted mailers at inopportune times.
BI Dashboards Emphasize Only Your Biggest Results
A BI dashboard aims to show the highlights of your data, without an opinion attached. Data science is more concerned with identifying hidden opportunities in data and showing you how to act on them.
Let’s say you’re viewing your company’s online advertising spend in a BI dashboard. You will probably only see results from the services you spend the most money on — like Google Adwords or Facebook Ads — and not the smaller services.
And while those less expensive ads might represent a smaller volume, they could have a higher return on investment — but you would never know by looking at your dashboard.
Data science looks at huge volumes of data, and takes that data into account, whether it’s small or large in scale. Therefore, if a data scientist was looking at your ad spend, he or she might identify missed opportunities that wouldn’t have been presented to you otherwise.
BI Dashboard Reports aren’t Actionable
Ultimately, the human element of data science is what makes it powerful. Machine-only software solutions like analytics and BI dashboards won’t help you find the meaning in your data, or uncover the actions you can take to make a real business impact.
Here’s the problem: No dashboard will be able to take a deep enough look at your data to provide you with actionable information.
On the other hand, a data scientist can use his or her analytical expertise — and business sense — to tell you what the relationships in your data mean, and how you can improve certain metrics through targeted action.
So while BI dashboards can help you quickly and easily take stock of certain metrics, remember that leveraging your data is so much more than that. If you want to be truly data driven, you have to move beyond the dashboard — and into data science.