How to use Business Intelligence for Data Analytics?

 In Business Intelligence, Data Analytics

Business Intelligence and Data Analytics are mutually interdependent and coexist in the cohesion of each other. To understand how to use BI for DA, we first need to understand what is BI, what is data analytics, and how data analytics is dependent on BI.

What is Business Intelligence?

It is essentially a data-driven decision-making support system. BI is the incorporation of systemic ways and means through which businesses collect, analyze, share, visualize, and report data. Though they do this to aid better-informed decision-making.

Uses of Business Intelligence 

All departments in a firm, including sales, marketing, and customer service, can benefit from business intelligence technologies. Both team members and executives can use the output of BI tools. When conducting investigations, data engineers and data analysts might benefit from the simplicity of a BI tool.

The following are some examples of the usage of business intelligence:

  • Visualize the number of people who visit and use a website over time.
  • Follow the progress of potential consumers through a sales funnel.
  • Compare the results of company measurements to benchmarks and objectives.
  • Evaluate the performance of marketing campaigns and experiments.
  • Segment users by demographic characteristics.
  • Generate reports for the team and executive decision-making.

Features of Business Intelligence Helpful for Data Analysis

Data must first be acquired and stored using data engineering tools to conduct business intelligence tasks. Then you should make it available for analysis and reporting using business intelligence tools.
When looking for solutions to help your business gain insights from data, keep the following criteria in mind to ensure that they meet your requirements.

Data Connections 

To execute business intelligence, you must first be able to obtain access to data. It’s critical to ensure that your analysis-side BI tool can communicate with your other data-storage solutions. For example, data sources like MySQL (database), Google BigQuery (data warehouses), and even ad hoc data files in CSV format. 

Ascertain that your BI tool has access to the most up-to-date data to make quick judgments. Avoid procedures that require the creation of custom data pipelines. Because this might cause problems if the raw data changes or an unexpected event occurs.

Data Querying

It’s also worth considering how easy the BI tool can connect between data sources and connecting to data sources. A decent BI tool would simplify combining queries from various data sources into a new one. Connecting and integrating data from many sources gives extra insights that would otherwise be unavailable.

Data Visualization 

Business intelligence applications include data visualization. An excellent chart can convey information more quickly than a table of statistics. Examine the types of diagrams offered and the customization level of customization.

A BI tool can create dashboards out of sets of charts and tables. On the other hand, the dashboards make it possible to keep track of essential company KPIs in one place. Make sure your selected BI solution can refresh its dashboards automatically so that viewers always have the most up-to-date information.

Data Availability

It’s critical to think about the flexibility that business intelligence tools can give an organization. Modern BI technologies can make it easier for data stakeholders to conduct investigations. They also allow data teams to focus on more in-depth analysis. For example, consider how simple it is to add new users to a BI tool. As well as it is easier to obtain the data they require. 

If there are different account types and user accounts for creators, editors, and viewers, keep track of them. Then examine whether several people may collaborate on the same dashboard. Using a self-service BI platform to help a business become more data-driven. It is a great approach to start the process. 

It is especially true for small organizations, which may lack the people needed to implement a more typical BI strategy centered on a dedicated data team. The sooner a company can access and act on its data, the easier it is for users to get up to speed with a BI tool.

Application Support

Each BI application has its learning curve that can take some time to overcome. It’s an essential factor to consider that you want many users to use the software, even individuals with no technical or analytical experience. Examine the resources available for utilizing each BI tool, like tutorials and FAQs.

Depending on the supplier, active support lines may be available to assist customers with specific questions directly. Make a sincere effort to use a BI tool in a product trial to evaluate if it meets your requirements. 

Plan out and attempt incorporating some of your use cases in the product before and during the trial. Examine whether the product’s features address your issue, where you get stuck, and how the BI tool’s support resources can assist you.

Data Analytics

Data analytics is the supplementing predecessor of BI. Once we have all relevant data at our disposal and facts presented, this is the time to ask specific questions and glean predictable certainties from them. It divides complex structures into easily agreeable forms to better understand the whole.

How to use Business Intelligence for Data Analytics?

In simpler terms, in a sequential follow-up, organizations ought to do these things: Once an organization, through BI knows what is happening to their business, they intuitively explore to learn – “Why it is happening?”. Furthermore would also like to know, “What will likely happen in the future?” And here starts the realm of Data Analytics. 

While from inception, BI stands for – What is happening to your business (for visibility), the preceding questions, i.e., Why is it happening? What will likely occur in the future to set the stage for a business call with the investigation, prediction, and prescription?

This very process of ‘business analytics’ per se encompasses Data Analytics in its entirety. And Data Analytics facilitates the process of Business Analytics; this is where the role of Data analytics lies.

Role of Data Analytics in Business Intelligence

Diagnostic Analytics(Why) – In business analytics, diagnostic analytics comes in handy to initiate the necessary corrective measures. Through this very process, you differentiate the whole complex structure and grasp why something is happening at a certain place.

Now, as you have the requisite information with evidence, you can chart your future course in the best suitable way to avoid failure or enhance performance.

Predictive Analytics (What will) – As from the process described above, an organization is well aware of the money of its products or projects or the organization itself. Now, the organization must want to know where we will be heading in the future by this productivity rate.

Also, if the organization intends to improvise, then the required essentials must be followed or achieved in due course.

Prescriptive Analytics (Next best action) – Now that the whole state of affairs is known, the intended ‘end views’ are targeted with Prescriptive Analytics. This process of analytics deals with – what is the best suitable set of tactics or strategies that ought to be followed. Prescriptive analytics is a set of tools through which corrective measures must be followed or observed in letter and spirit to define the improvisation that is objectified as goals to be achieved.

Conclusion

Thus, Business Intelligence and Data Analytics are interconnected. Thinklayer provides a complete solution for all your Business Intelligence and Data Analytics needs. For more details, connect with our experts. 

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