Today, all companies are looking to do more with their data. Organisations are looking at moving beyond reporting, to provide users with greater insights from their data. Using Azure Synapse Analytics, organisations can utilize the cloud as the central repository for all of their data. Information is not limited to just columns of data; with Azure Synapse, companies can move and analyze other information, including images, sound, or caches of PDF documents. This information can be organised to maximize the company’s ability to access the data and provide insightful information with Power BI. Data used in Power BI can come from the entire data store created by Azure Synapse. Azure Synapse is used as a central data repository, as it contains the tools needed to load data into a data lake, which can be used for machine learning or a virtual database for Power BI. For very large amounts of data, Azure Synapse can create data warehouses and data marts with optimized resources used for Power BI reporting.
Once Azure Synapse has processed the data needed in an organisation, the next step is to implement a data visualization environment, such as Power BI. Power BI empowers users to gain more insight into their data by creating an environment where people can interact with their data to find answers to business questions. Developing the data models used in Power BI is the first step to providing access to data from all parts of the organisation. Data can come from IoT devices and social media, as well as a number of applications the organisation uses in its day-to-day environment. The data that needs to be reported upon may be streamed, perhaps with Azure Event Hubs, a data warehouse, database, or data lake. The data needs to be in an accessible location so that it can be used not only for reporting but for advanced analytics AI/ML processes. If the data is stored in one location, it will be accessible not only for Power BI reporting but also for data science modelling.
As lots of data is needed for this task, the data needs to be stored in a secure, scalable location, and to meet these requirements, the data should be stored in the cloud. Centralizing your data storage in Microsoft Azure provides a secure, scalable, fault-tolerant location. Today, many companies are looking to store and analyze data that may not be text. Video, pictures, and sound files are often analyzed using dedicated AI libraries. For this wide variety of data, a data lake is often the solution that best fits these needs.
A data lake is the logical place to store data from daily transactions, streamed GPS location data, and image files, along with any other data the organisation wishes to keep. How the data is stored determines the ability for analysis. Azure provides a hierarchical file structure that can be used for analysis as well as Power BI. Data scientists can use this data to create models to provide additional insight, which you can use within Power BI. Data scientists can also use Power BI to help them quickly analyze data to determine which elements are needed in a model. Power BI offers many different business intelligence features, many of which have been added recently, such as goals, key influencers, decomposition trees, and smart narrative, which can provide additional insight into the factors contributing to organisational success.
The task of managing data used for analysis can be challenging as the data can be quite large and can include structured data stored in a data warehouse or unstructured data such as images, and anything in-between. Management of the data will need to include a method for adding more data and organising it in a format needed for business intelligence tasks. Extract, load, and transfer methods can be used to gather data from different parts of the organisation or third-party applications. Based on the skills in your organisation, the data transfer may happen with low-code solutions, data flows, or Python code. It does not matter where the source of the data is as you need to gather it to be able to provide operational insight. To provide answers to business questions, your organisation needs to develop solutions to provide all of the information needed for accurate decision making. Machine learning elements can further enhance the decision-making process by providing predictive analysis to determine the future state of the company or to point out anomalies with performance that may have gone undiscovered.
The task of providing this data so that Power BI can access it and the insights others may provide with the data is all available in one tool, Azure Synapse Analytics—a unified analytics platform. Power BI can be used to report on data from a data lake and Azure Synapse provides the capability to ingest data from multiple solutions, create large data warehouses, manage data lakes, build targeted data marts for BI reporting audiences, develop machine learning solutions, and provide Power BI the information needed to move from providing reporting to providing business analytics solutions.
Move from information to insights and leverage your own data to generate business insights.