Data scientists who haven’t used BI tools like Tableau, Power BI, or Qlik Sense often say that these tools simply aren’t needed. They already code their scripts on an open-source framework, and that the scripts do the job just fine.

If you dig a little deeper into the matter, though, you’ll often hear those same data scientists talking about some needs that their scripts don’t quite fulfill. In fact, they usually boil down to the following issues:

Storytelling.

Dashboards and visualizations can be invaluable, but only with the right explanation, narrative, and context. Without that, they’re open to interpretation by every viewer. This is why data scientists (and other analytics users) have to give a voice to the data. They need to explain what they found, tell the narrative, point out whenever outliers skew a trend, and contribute their suggestions. Action requires context, and this approach is what makes informed action possible. After all, that’s what business intelligence tools are all about – enabling data-driven decision-making. If you’re going to make decisions based on data, you want that data to be as complete as possible. And sometimes, in order for the data to be complete, you need more than just visualizations on a page.

Exploration, not preparation.

Without either a hard-working data engineer or a BI tool, you’re likely to spend about 80% of your efforts on prepping the data, and just 20% finding insights once you can finally explore it. Creating business-ready data involves a lot of prep work, including the entire data integration process (transformation, cleansing, and so on). Ideally, you’d find a BI tool that offers data integration capabilities for transforming and combining data. In fact, some of them even include an enterprise-class data integration platform to ensure a smooth data catalog and data analytics pipeline. Without the help of a BI tool (or a dedicated data engineer), you could use up valuable time on the data integration process, when it could have been used more efficiently on other tasks. It’s like taking hours to prepare a meal and then eating it in 5 minutes. If you’re spending more time on preparing data than you are on exploring it, maybe it’s time to consider a change of strategy.

Exploration from every angle.

Scripts in Python, R, etc. can certainly provide effective answers for pre-determined questions; however, their analysis is limited to a linear, SQL-based model. Their query-based approach will only explore data within certain limits, so the discoveries you’re able to make are also limited. A few BI tools take a different approach, with engines that allow free data exploration. You can explore in any direction, and from all angles. This type of data exploration uncovers even obscure connections; you’ll be able to see the trends, outliers, and patterns that most likely wouldn’t have come up with a more conventional approach. You may not have known to query them, or the query-based approach might not have been able to find them at all.

Collaboration.

A common theme of discussion among data scientists is the need for better knowledge-sharing and group problem solving with analytics and data. Until this can happen, their stakeholders will keep ending up with fragmented pieces of tacit knowledge, as well as under-utilized domain expertise. BI tools alleviate the problem by allowing asynchronous collaboration with business users; this saves time that would have otherwise been wasted while waiting for a decision, or sitting through meetings. To be more specific, BI tools allow users to make suggestions on how others might explore or refine the data, or add narrative in order to give business context. Just like multiple people can contribute to a shared document in Google Docs, these BI tools make it possible to turn individual intelligence into collective intelligence.

Flexibility in visualizations.

A lot of data scientists go to open-source libraries for their visualizations. BI tools, on the other hand, build their visuals from the data’s predefined structures. With this approach, you’ll have much more flexibility. The engine aggregates data from a granular level, which more effectively exposes patterns in the data. This makes it easier to create derivative data points on the fly; first the data is grouped together, then visualizations are created from those groups (color coding, benchmarking, etc.). Once that’s established, you can keep track of the codes no matter how many visualizations you’re using. Even better, these tools help you leverage the optimum visualizations in order to analyze data according to certain attributes (like time series or geo analytics), something that’s tricky when you’re using open-source libraries.

Trusted, secure, governed data.

In order to trust your models, you have to be able to trust the data. The best BI tools include add-ons like data lineage visualization, or centralized management that lets you securely administer data using rules-based governance. This last one lets you control sharing, publishing, and which users can access data and apps.

In addition to that, you want cataloged data. Some BI tools offer smart data profiling; this tells users not only whether the data is ready, but also surfaces data quality issues. As an example, it could identify anything that could be PII, then automatically mask it. Lastly, data that’s easily searchable (via metadata) will pretty much make it feel like you’re shopping once you’re able to search by topic, business domain, or data source.

Even if you decide to use a BI tool for your business, you can keep using an external IDE in order to create or refine your scripts. Then you can use them in conjunction with a BI tool, solving the needs mentioned above. Business intelligence tools are a collection of apps and connectors which help you make better decisions by combining data from a variety of sources into a single platform that supports a wide range of use cases like data visualization, dashboards, reports, embedded analytics, and augmented analytics.

Keep in mind that not every BI tool will excel in all capabilities; you’ll have to do your own research to make sure you’re getting the one that suits your needs. The Gartner Magic Quadrant business intelligence report will provide you an unbiased evaluation of BI vendors.

Source: Plato Data Intelligence: PlatoData.io