As its name suggests, business intelligence (BI) is about using data to make intelligent business decisions. BI is the “it” factor that can turn an organization’s data into a competitive edge, driving digital transformation and strategy formation for informed decision making in today’s complex, data-rich business environments.
This requires the monitoring, collecting, and reporting of copious amounts of data for business intelligence analysis. The sheer amount of data and complexity of the environments through which it flows is greater than ever before, making the roles under data management, data science, and data and BI analysis even more challenging.
Data analysts and business intelligence analysts are on the frontlines, navigating database management systems, mining and analyzing the data, and preparing data visualization and modelling.
When it comes to analyzing data information to detect trends and patterns that can impact various facets of business, a host of different areas and specialists can be involved—data scientists, financial analysts, marketing analysts, logisticians/logistics specialists, and computer systems analysts, to name a few.
They need full visibility into the organization’s data. What challenges do they face in accessing and utilizing this data for business intelligence?
- Disparate databases, data systems, and reporting and analysis tools that lack integration. These systems and tools can’t communicate with each other, so data movement and dependencies are unseen and lost in translation.
- Disparate business units and teams with different approaches, tools, and goals for data analytics. These various teams may need the same data and rely on the same systems, but the humans aren’t communicating with each other or collaborating in how they access and analyze the data.
- Questionable data quality. Data from disparate sources and systems can lead to oversights and data blind spots, corrupted data, and all around “messy data.” If you can’t trust your data, you can’t use it for informed decision making.
- Spreadsheets. Manually entering and tracking data information and flows within a spreadsheet is a drain on man hours—hours the team could be using to analyze and optimize data for business intelligence efforts, rather than chasing down data and organizing spreadsheets.
Gain Full Visibility of Data Systems with Data Lineage
Data lineage shows all the paths data takes through an organization’s environment and what happens to it at every stage, from creation to consumption, mapping dependencies between data entities. This map allows organizations to tame data complexity, remain efficient, and optimize their data for business intelligence efforts.
How can data lineage help with these BI efforts?
- The various datasets, streams, and flows are mapped and combined for a more manageable view of data sources.
- Connections between workspaces, systems, and data objects are revealed so there’s no surprises or blind spots.
- Previous revisions and changes in the data environment can be detected (called historical lineage), allowing analysts to see what it looked like in the past and how that differs from and impacts the current state of data.
- The effects of change and what datasets have been and will be impacted become apparent, highlighting which data is essential.
- Time and resources are saved by automatically scanning and mapping the data environment, freeing BI analysts to utilize data in generating the insights needed for informed decision making.
In short, by having visibility of the entire journey of the organization’s data, BI teams have the full data picture they need to interpret the data for informed decision making.
MANTA Automated Data Lineage for BI
Historical Lineage and Time Slicing
Establishing a plan for delivering historical lineage can ensure BI teams have a complete picture of the data lifecycle to successfully analyze the data journey.
MANTA’s Revisions feature allows you to see how lineage looked in the past and compare it to its current state by taking a slice in time to show how the system looked at a selected point. MANTA even compares different “time slices” to see how the lineage has developed.
Data Transformation and Active Tags
Data in today’s enterprises undergoes transformations across multiple touchpoints—through data warehousing, migration, integration, etc. Data transformation is crucial for the data visualization, reporting, and analysis needed in business intelligence. It can also be resource intensive.
MANTA’s automated data lineage can help ensure data quality and provide insights and support for data transformation. MANTA identifies transformations and automatically highlights them with MANTA Active Tags for visibility as to where and how data is transformed.
MANTA Supported Scanners
MANTA can help you better utilize your data for business intelligence with scanners for database reporting and analysis tools, including:
- Snowflake: Maps lineage for a comprehensive overview of the data journey and lifecycle to understand data in context, increase business agility, and deliver data intelligence that fuels growth.
- Google BigQuery: Connects to this serverless, highly scalable data warehouse to scan metadata and read SQL programming code and logic to create a detailed visualization of data lineage.
- Microsoft Excel: Processes files and looks up objects (e.g., graphs and pivot tables), with all mapped objects connected with their source objects by analyzing queries with MANTA’s database connectors.
- Qlik Sense: Extracts metadata for data flow analysis, processes essential Load Script statements to define how data is loaded and transformed, and analyzes the usage of the data loaded from physical data sources in visualizations used in the applications’ sheets.
- Tableau: Connects to Tableau Online and Tableau Server to automatically extract all workbooks and data sources and pushes data lineage visualization to various platforms, optimizing Tableau as an end-to-end data-visualization reporting tool that simplifies data for actionable BI insights.
- SAS: Connect to SAS models and analyses SAS scripts, database connections and user defined functions and procedures
- Power BI: Scans reports, datasets, and database connections for data lineage visualization, amplifying Power BI’s capabilities in providing interactive data visualizations for data insights.
- Microsoft SQL Server Analytics Platform System: Connects to the on-premise data warehouse platform to scan metadata, reads all SQL programming code and logic stored in it, and creates a detailed visualization of the data lineage.
- Microsoft SQL Server Analysis Services: Extracts lineage from the SSAS analytical engine to create the most precise end-to-end data lineage for this analytics platform for greater capabilities and control in creating reports.
- Microsoft SQL Server Reporting Services: Processes reports to create the most precise end-to-end data lineage for this highest layer of the Microsoft business intelligence environment.
These are just a few of MANTA’s natively supported scanners. See the complete list encompassing databases, ETL tools, reporting and analysis software, modelling tools, and programming languages.
This post was first published by MANTA, on their blog, and is reposted with permission