The journey to making data-driven business decisions starts with a business use case and ends with reporting and visualization. Data is the new petroleum or the new gold currency. Organizations that take advantage of data stand out in the competition.
Imagine a world where a decision maker opens a web browser login to a reporting dashboard that shows Key Performance Indicators (KPIs), status of business processes, the burn rate of a budget, or current budget performance compared to prior year – all in one place. A more specific example of data reporting benefits is the ability to predict and monitor the status of parts lifecycles on all machines in a factory. Imagine the gains in production efficiencies by doing maintenance on many machines on a production line to get it up and running again in a few minutes instead of repairing one machine at a time while waiting for ordered parts.
Today’s Power BI reporting technology improves data analysis and reduces time lags in leaders’ abilities to make business decisions. Here, our Inviso Power BI experts map out a journey in six steps toward achieving descriptive analytics to make data-driven decisions in your organization.
Step 1: Business understanding
Business understanding is the most crucial step in the entire process. Our business intelligence analyst starts by learning the business process and how the data is generated. The analyst documents the workflow if it is not documented. It is important to know how the company is currently managing the business and how long it takes to accomplish tasks. The analyst interviews stakeholders to understand and document pain points. The analyst concludes by documenting questions that the business would like to answer from data.
Step 2: Data acquisition
Once data questions are clarified, it’s time to identify where the data is stored. The analyst does extensive research on data silos, databases, and other places where relevant data is stored and located. At times, data might be sourced through surveys or through API (Application Programming Interface) calls to third-party services such as weather, industry averages, or other publicly available data that can be used to enrich data analysis. Collaborating with a solutions architect, the data analyst designs an ETL process to load the data to a centralized place, which serves as the single source of truth. This process is documented and a solution is designed to acquire the data.
Step 3: Data cleaning
In the real world, data is usually incomplete due to user entry errors, foreign characters, or other unforeseen circumstances. Other issues might result from how the ERP system is storing the data in its online processing database. Often, there are issues in data integrity and this step addresses cleaning of data and preparing it for exploratory analysis.
Step 4: Data exploration
In other words, data exploration is data exploratory analysis. Our analyst takes a deep dive into the data to find relationships between data and its relevance to the questions the business is aiming to answer. This step complements data cleaning as analysts find issues with data and go back to fix them. The analyst finds the relationships between the data and identifies facts from the dimensions. An entity diagram that represents the relationship between facts and dimensions is created in preparation for data modeling.
Step 5: Data modeling
After an entity diagram is created, the business intelligence analyst uses it to create a data model. Keeping in mind that all transformation to the data is done upstream as much as possible to source data and downstream as needed. If the source is a SQL database, then the appropriate solution would be to use a tool such as SSIS or ADF (Azure Data Factory) to model the data into Star Schema prior to doing a direct query to it or import it to Power BI. On the other hand, if the source of data is a flat file, like an Excel file, then doing the transformation in Power Query is an excellent choice before analyzing the clean data using a powerful tool like Power BI. With that said, at times, data might be stored in a Data Lake, so using Power Query is useful in creating a star schema model.
Step 6: Reporting and visualization
The most exciting part of the project is the last step of the process – reporting and dashboard creation. Our data analyst and architect design a suitable, cost-effective solution to host and distribute the dashboards and report to clients. Providing valuable insights and answering key business questions via dashboards and reports is how we aim to create value for stakeholders. This analysis identifies relevant data metrics to display in appropriate, effective reporting visuals. It is imperative to present the right data point with the right visual. This allows stakeholders to understand current business performance at first glance, for example.
During this step, extensive analysis is done on the data and wireframe, and a proof of concept is created to represent the data in a dashboard with KPI cards usually placed at the sides or top of page and relevant data visualized in the middle, starting with important data at top left and less important to the right. Creating dashboards is truly an art form of design and function.
Dashboards and reports are usually created in an Agile/Lean environment where the analyst interacts with stakeholders to get feedback for each release iteration until the business is satisfied with the final product. Once the dashboard is finalized, it’s time for automation, and the Power BI dashboard is deployed to app.powerbi.com or to the Power BI Report server. The dashboard is now accessible through a URL and is refreshed nightly or every 15 minutes, oftentimes reading live from source data. Finally, important questions from stakeholders are answered, and business status can be monitored via dashboards.
Driving business decisions forward
To help your organization make data-driven decisions in answering today’s business problems, contact Inviso’s expert team to help identify fully automated data reporting and dashboard solutions at email@example.com.