Many data scientists prefer to build scripts in open-source frameworks because they know they will function. Business intelligence technologies such as Qlik Sense, Power BI, and Tableau should be revised. However, these same data scientists frequently identify flaws in their own methodologies, which the greatest BI tools can remedy. This is why it is important to know Do Data Scientists Use BI Tools and use them for your business.
Do Data Scientists Use BI Tools: What are BI Tools?
Business intelligence (BI) tools are any data analytics or machine learning technologies that are used to extract insights from a company's internal or external data holdings. Depending on how long a business or institution has been in operation, these entities frequently collect vast amounts of information and data about themselves. This could include data on personnel management, data on client engagement with the business, and much more. Many business intelligence technologies convert the huge amounts of data collected by a firm, team, or individual into information. They can use this to make future decisions or assess the success of prior decisions.
How Do Data Scientists Use BI Tools?
Data scientists who work with business and financial data generally employ business intelligence technologies for prescriptive and predictive analytics, as well as to create presentations for key stakeholders in a company or product. Business analysts, for example, use business intelligence tools to examine previous data. They can use this to provide insights to a customer or firm on the type of business decisions to move forward. Financial analysts employ business intelligence tools to forecast and anticipate economic trends, as well as to assess the risk of individual investments.
In contrast, data scientists in advertising and marketing can employ business intelligence technologies to track sales, commercials, and consumer behaviour. Specifically, numerous business intelligence platforms can gather vital business indicators such as web traffic, sentiment analysis, and other forms of engagement. You can use these measurements to enhance areas like SEO and keyword search. Furthermore, any business owner can invest in business intelligence solutions to monitor the progress of their brand and product development, as well as probe deeper into audience comments and user profiles. So, data scientists use business intelligence technologies in a variety of applications other than data science.
Do Data Scientists Use BI Tools: What are the Benefits?
1 - The Significance of "Telling the Story"
Your visualisations and dashboards may be less effective without narrative, explanation, and context. If you only have the visualization, each observer may interpret the meaning differently. Data scientists (or other analytics users) must speak up about the data. You must convey the tale first, followed by an explanation of what you uncovered, such as an outlier skewing a pattern. Then your audience can take educated action because action requires context. In a broad sense, this is the goal of employing a business intelligence tool: to use data to drive decision-making.
2 - Flexibility While Creating Visualizations
Data scientists frequently use open-source libraries to create visualizations. However, this means that the visuals are generated with predefined data structures. Instead of forcing the data to match the visualizations, you should have visualizations that fit the data; flexibility is essential for showing patterns. Some BI applications employ engines that aggregate data at the granular level, allowing you to select the optimal visualization options for data analysis based on specific criteria (geo-analytics, time series, and so on), which is frequently difficult to achieve using open-source libraries.
By creating derivative data points on the fly, it is feasible to group data, generate visualizations from the groupings (such as benchmarking or colour coding), and then apply those codes across many displays. If your visualizations make assumptions about data structure rather than being adaptable enough to fit the data that is available, you may wind up with skewed or missing data.
3 - The Need to Explore Associations Openly
The finest business intelligence solutions do not use the traditional linear, SQL-based analytical methodology; instead, they use an engine that allows you to explore your data from any viewpoint. Scripts in Python, R, and other languages are highly competent at finding answers to pre-determined queries. But this method limits the data that can be explored, which limits what you can learn from it.
However, with the correct BI tool, you can reveal outliers, patterns, and trends, as well as relationships that you couldn't have discovered using a query-based method or would not have queried otherwise. Certain BI tools are a better alternative for maximising the effect of data on your organisation since they can reveal obscure linkages within the data.
4 - The Requirement for Controlled, Trustworthy, and Secure Data
Models are useless unless you can trust the data; the best BI solutions use rules-based governance to ensure data integrity. Add-ons include safe data administration via centralized management (due to rule-based governance), giving you control over who publishes, shares, and accesses apps or data. Another add-on enables data lineage visualisation, which allows you to see where the data originated from and where it is headed.
You also need to catalogue your data. Some BI tools incorporate smart data profiling, which analyses data readiness and immediately flags data quality issues. Smart data profiling, for example, might identify potentially sensitive information and automatically disguise it. Finally, the ability to readily search your data using metadata simplifies the process significantly; users can search by business domain, topic, or data source.
5 - The Need to Investigate Rather Than Prepare Data
To have usable data, you must prepare it carefully. However, if you do all of the preparation yourself, you may spend the majority of your time on that rather than finding ideas as you investigate. Data engineers can manage the complete data integration process (such as purification, transformation, and so on) to ensure that the data is business-ready, but you'd need a full-time data engineer if you wanted to spend all of your time exploring rather than planning. Top-tier BI solutions have data integration and transformation capabilities, eliminating the need for you to do it yourself. Some even integrate an enterprise-class DI platform to provide a unified data catalogue and analytics data pipeline.
Conclusion
Business intelligence is a set of apps, processes, and infrastructure that make it easier to access and analyze data. This enhances and optimizes your decisions, whether you are a data scientist or a citizen data scientist. If you decide to use a BI tool to make more data-driven decisions, make sure you choose the proper one. Do Data Scientists Use BI Tools is an important question to answer. Contact Tech Bridge Consultancy today to learn more about BI tools and data science.
