Your BI Tools Are Only as Good as Your Business Data. Do You Trust Your Data?
We are all familiar with the old saying “garbage in, garbage out” (or “GIGO” for short). That phrase has been around since the early days of computing, but in this era of high-volume data, advanced analytics, and artificial intelligence, it takes on a much greater significance than ever before.
Modern business intelligence systems can process more data than ever before, and they can do it at blazing speeds relative to the BI software that existed just a few years ago. Not only is the volume of information growing, but its velocity is increasing as well. Mobility and clickstream data, for example, are providing feedback in real time.
For business leaders, these capabilities can be game-changing. Strategic and tactical decisions can be made based on data-driven insights to an extent that was never possible in the past. At the same time, though, the cost of getting the data wrong is also greater than ever. When high-stakes decisions are informed by data analysis, the consequences of low data integrity can be significant.
If your organization wants to maximize the value of its investments in BI tools, it pays to begin by investing in data integrity. At Precisely, data integrity is central to what we do. We help enterprises to ensure maximum accuracy, consistency, and context across all of their data assets. Our approach centers around the four pillars of data integrity: integration, data quality, location intelligence, and data enrichment.
Many organizations still operate with multiple disconnected silos of information. Customer data may be stored in multiple locations, including ERP and CRM systems, specialized billing software, or service management applications. If those various systems are out of sync with one another (as is often the case), it can be virtually impossible to get a 360° view of your customers. The best BI tools in the world cannot do a good job of making sense of disjointed information spread across multiple systems.
The task of connecting and managing data pipelines between multiple applications can be daunting. For most organizations, doing that work manually is simply too expensive and time consuming. Considerable time and expertise are required to develop and test data pipelines, manage complex data formats, and adapt changes to the overall system landscape as it evolves.
A more effective approach is to build a comprehensive integration strategy around a solution that can work with a wide variety of data sources out of the box, including the most complex mainframe and IBM i data, as well as the next-generation cloud data platforms most enterprises are using today. Not only do such solutions make short work of building and managing data pipelines, but they also provide for much greater agility as new systems are brought online, and as new integration points emerge.
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When data is inaccurate, incomplete, or lacks standardization, the value of the data is significantly diminished. In a best-case scenario, BI tools sitting on top of poor-quality data can deliver results that fail to provide enough value. At worst, they can deliver results that are outright wrong. That can lead to bad business decisions, and subsequently bad results.
Data quality must be addressed proactively, by standardizing and validating data and identifying any gaps or discrepancies. Corrections can be automated to a great extent, and workflows help to ensure that data quality issues are captured and addressed by the right people at the right time. A good data quality solution must be capable of uncovering hidden relationships and unexpected patterns in your data. When data is accurate, complete, and consistent, you can rely on the results coming from your BI tools.
Virtually every element of data in the world can be associated with location in some way or another. Location intelligence attaches geospatial context, opening a world of new ways to analyze information. It helps us to better understand assets such as buildings or telecommunications infrastructure, and to relate those to other relevant factors in a geospatial context such as legal boundaries, competitor’s locations, roadways, and natural features such as bodies of water or vegetation.
Location intelligence can also help businesses better understand customer behavior, including where customers live, work, and spend their leisure time. Mobility data provides rich insights into customer movement through time and space. For a retailer planning a new location, for example, location intelligence provides context combining customer mobility and traffic, competitive dynamics, and more. The value of business intelligence increases exponentially as geospatial context is added to your data.
The world’s most innovative companies understand that data is one of the most valuable assets they have. Data enrichment provides additional dimensions, providing the basis for compelling new questions about relationships and variables that affect your business. By enriching your data with information from trusted third parties, you can get even more value from your BI tools.
Ultimately, data integrity is about increasing the trust in organization’s data. Good business intelligence tools can provide valuable insights that help you run your business more profitably, but if the underlying data is weak or inaccurate, the “garbage in, garbage out” rule applies.
The Precisely Data Integrity Suite addresses these four pillars of data integrity, starting with powerful integration and extensive data quality capabilities. We add leading location intelligence to that, providing rich geospatial context, as well as powerful data enrichment and location intelligence capabilities to help you visualize trends in your data.
To learn more about how the Precisely Data Integrity Suite can help you get more from your business intelligence and analytics investments, join our upcoming webinar How to improve trust in advanced analytics, AI, and machine learning.