Business intelligence (BI) is the process of transforming data into insights that can help organizations make better decisions and achieve their goals. But how do BI professionals use databases to drive business value? In this article, we will explore some of the ways that databases can support BI initiatives, from data integration and quality to analytics and visualization.
One of the key challenges of BI is to integrate data from various sources, such as internal systems, external applications, web services, or cloud platforms. Data integration involves extracting, transforming, and loading (ETL) data into a central repository, such as a data warehouse or a data lake, where it can be accessed and analyzed by BI tools. Data integration enables BI professionals to create a unified and consistent view of the data, regardless of its origin, format, or structure.
BI professionals leverage Dbs by embracing trends such as data integration, advanced analytics, real-time processing, self-service BI, cloud solutions, mobile accessibility and robust data governance. Integrating SAP solutions & AI technologies further enhances the capabilities of BI in driving business value through actionable insights and informed decision-making. SAP HANA, an in-memory db is commonly used for real-time analytics, providing rapid access to large volumes of data. SAP is also adapting to the cloud trend with solutions like S/4HANA Cloud, SAC providing businesses with the advantages of cloud infra BI professionals often integrate BI solutions with SAP's mobile apps, enabling users to access SAP-based analytics on the go.
Another important aspect of BI is to ensure that the data is accurate, complete, reliable, and relevant. Data quality involves validating, cleaning, and enriching the data, as well as applying business rules and standards. Data quality helps BI professionals to avoid errors, inconsistencies, and anomalies that can compromise the validity and reliability of the insights. Data quality also improves the usability and trustworthiness of the data, as well as the compliance and governance of the data management processes.
The core function of BI is to perform data analytics, which is the process of applying statistical, mathematical, or logical techniques to discover patterns, trends, correlations, or anomalies in the data. Data analytics can be descriptive, diagnostic, predictive, or prescriptive, depending on the level of complexity and sophistication of the analysis. Data analytics enables BI professionals to generate insights that can answer business questions, solve problems, identify opportunities, or recommend actions.
BI will always be about finding exceptions within the data. Oftentimes business leaders think this is like going to the vending machine and picking the snickers bar. Analytics without discipline is an exercise in confirmation bias. The data says what it is expected to say. Analytics is also more complex than academic. In school the outliers and coefficients are obvious among the 10 variables. In the real world there are 1,000 variables. Analytics done well is hard and over long time frames.
BI professionals use specialized tools like SQL to analyze and extract meaningful insights from data stored in databases. They then create reports, graphs, and other visual presentations to share these findings with others in an easy-to-understand way. BI professionals can also tap on the databases to predict future trends or outcomes by using advanced algorithms. This helps them understand patterns in the data and make smarter decisions based on what might happen in the future.
The final step of BI is to present the data and the insights in a clear, concise, and compelling way. Data visualization involves creating charts, graphs, dashboards, or reports that can communicate the key findings and messages to the intended audience. Data visualization helps BI professionals to convey the meaning and value of the data, as well as to engage and persuade the stakeholders. Data visualization also facilitates the exploration and interaction with the data, as well as the feedback and collaboration among the users.
As the volume, variety, and velocity of data increase, so do the demands and expectations of BI. Therefore, BI professionals need to keep up with the latest trends and innovations in the database field, such as cloud-based, distributed, or hybrid databases, that can offer scalability, flexibility, and performance. Moreover, BI professionals need to leverage the capabilities and features of modern databases, such as in-memory, columnar, or graph databases, that can support advanced analytics, such as machine learning, natural language processing, or graph analysis.
To effectively use databases to drive business value, BI professionals need to have a range of skills and tools, such as SQL, Python, R, or Java, that can enable them to manipulate, query, and analyze the data. Furthermore, BI professionals need to be familiar with various database platforms and systems, such as Oracle, SQL Server, MySQL, MongoDB, or Neo4j, that can store, manage, and process the data. Additionally, BI professionals need to use various BI tools and applications, such as Power BI, Tableau, Qlik, or SAS, that can integrate, visualize, and share the data and the insights.
Achieving genuine business "value" is a nuanced task. Emphasizing the understanding of stakeholders' vision and organization's overarching goals in non-technical terms is key. The Five Whys or Ishikawa methods are fantastic for drilling into why specific BI initiatives are pursued, ensuring a more focused and impactful approach. Incremental value and an agile strategy are also beneficial. The iterative approach, grounded in stakeholder needs, feasibility, and quality, ensures BI professionals meet immediate requirements while evolving with the organization's changing landscape. This adaptability is crucial in a field where technologies, business goals, and data landscapes are constantly evolving.