Why Good Data Management Is Essential to Data Analytics
Businesses today have access to a greater volume of data than ever before. Organizations that can effectively leverage data as a strategic asset will inevitably build a competitive advantage and outperform their peers over the long term. In order to achieve that, though, business managers must bring order to the chaotic landscape of multiple data sources and data models. That process, broadly speaking, is called data management. As the volume of available information continues to grow, data management will become an increasingly important factor in effective business management.
Lack of proactive data management, on the other hand, can result in incompatible or inconsistent sources of information, as well as data quality problems. Those issues will limit an organization’s ability to benefit from data-driven insights, identify trends, or recognize issues before they become bigger problems. Worse yet, poor data management can lead managers to make decisions based on faulty assumptions.
Data, Data, and More Data
Much of this challenge arises from the proliferation of systems, such as ERP, CRM, e-commerce, or specialized industry-specific software. Add web analytics, digital marketing automation, and social media to the mix, and the volume of data grows even further. Pile on external data from suppliers and external service providers, and it begins to appear unmanageable.
Many companies recognize the value of externally sourced third-party data to enrich and expand the context of the information they already have. It’s hard to imagine taking that step, though, without first getting a handle on the organization’s existing data.
Reining in all of this complexity is a critical first step in the process of creating a strategically relevant data analytics program. From a high-level perspective, that is a twofold process. First, you must make all of those data available in a centralized repository. That includes filtering, transforming, and harmonizing data such that they fit together to make up a meaningful whole.
Second, you must make that information accessible to users throughout the organization so that you can put it to meaningful use creating value for the business. In other words, you must put mechanisms in place that make it possible to access that information easily, quickly, and with sufficient flexibility that users throughout the company can analyze and innovate without extensive IT training or experience.
You must define and deploy these two elements of data management separately to ensure efficiency. Responsiveness and ease of use are a direct result of a well-built data management process and workflow; the sooner you corral and clean up the data, the sooner the data can start working to create value for the business.
Extract, Transform, Load
The challenge of data management begins when organizations are running multiple systems. As noted, that may include ERP, CRM, e-commerce, or any number of other software systems. It is also common for many organizations to rely on multiple systems that serve the same function. Different divisions or corporate entities that operate under the same corporate umbrella, for example, may be running different ERPs. This is especially common in the case of mergers and acquisitions.
Many companies would like to run reports against historical information held in a legacy database that is no longer operational. Because it is not always practical to migrate detailed transactional information when moving to a new ERP system, many businesses make do with a workaround or simply do without, excluding valuable legacy data from their current reporting systems.
When you involve multiple software systems, multiple data models are inevitably present as well. For example, if one ERP system contains separate tables for customers and vendors, but another combines them into a single table (using a single field to designate them as customers or vendors or both), then a simple report listing all of the company’s customers gets to be a somewhat complicated matter.
You must extract and transform the data from those two different ERP systems, then load them into a centralized repository in which there is a common definition of “customer.” That process must include a kind of translation, in which conformity to a common data structure and semantic model occurs.
This process of extracting, transforming, and loading data into a central repository is commonly known as “ETL.” It’s one of the fundamental building blocks of a data warehouse, and for companies that wish to provide robust, flexible, and comprehensive reporting, ETL is invaluable. The end result of a well-designed ETL process is a data warehouse that supports an “apples to apples” view of data from across the enterprise, regardless of which system they came from.
This process also links records that span multiple systems to one another. For example, designating master records with unique identifiers that are not necessarily consistent across two or more systems is common. In the ERP system, for instance, you might designate a customer a unique 10-character alphanumeric code (e.g. “JSMITH01”). In the e-commerce database, the same customer might be uniquely identified by an e-mail address such as “firstname.lastname@example.org.” To build reports that provide a complete picture of that customer, the central repository must connect those two records and identify them both as the same person.
Sources of Data Imperfection
Dynamic data can also create challenges. Dates and calendars, for example, can distort information that appears on financial reports. Easter falls on a different date each year, making year-over-year comparisons difficult for some businesses. Similar examples abound.
Currency conversions and calculations, likewise, are always in flux. Hundreds or even thousands of transactions coming through each day, even with small discrepancies, can add up. Good data management practices provide a framework for factoring that into the process so that data analytics can respond intelligently to those changes.
Human error introduces problems as well. This is especially true when systems are dependent on manually keyed information or data copied and pasted from external systems. ETL is an automated process, so it generally prevents a good deal of human error from ever happening in the first place. Nevertheless, if human error occurred further upstream in a source system, then the ETL process may provide an opportunity to test for data quality issues and alert the appropriate personnel, or potentially even correct such errors on the fly. Although ETL tools are not intended to replace a comprehensive data quality program per se, they can provide a very good starting point for improved data quality along with data harmonization.
Self-Service Reporting and Data Visualization
The second key element of good data management is to make information easily accessible to users throughout the organization with tools that empower them to innovate and create value for the business. Data visualization tools, in particular, have become a powerful force for informing, aligning, and persuading leaders throughout entire organizations. Today, data visualization tools are easier than ever to deploy, manage, and use.
A few years ago, deploying and managing a data warehouse required a substantial commitment of highly specialized technical resources, as well as investment in a robust computing infrastructure that could handle the required workloads. Legacy tools required a deep understanding of the source data and careful advance planning to determine how to use the resulting information.
Today, data visualization tools are extraordinarily powerful and flexible and have grown far less dependent on specialized IT expertise. Frontline users who interact with the data on a day-to-day basis can now perform much of the work involved with creating dashboards, graphs, and other visualizations.
Using Jet Analytics for Data Management
Jet Analytics from insightsoftware provides both components of the data management process as defined here. First, it provides a powerful platform for building a data warehouse and for creating and managing the ETL process such that it brings together data from multiple disparate systems under one roof for clear, meaningful reporting and analysis. Second, Jet Analytics provides a comprehensive suite of reporting tools that make it possible for virtually anyone in the organization to develop powerful visual dashboards, reports, and ad hoc analysis.
Jet Analytics comes with pre-built integration to Microsoft Dynamics and other powerful business management software, making it possible for virtually anyone to create a sophisticated data warehouse in just a few minutes. Users can seamlessly connect and consolidate multiple data sources, bringing information together under one roof, for filtering, transformation, and normalization. Dynamics customers can handle all of this in-house without depending on expensive third-party resources to create and manage their data warehouse. Jet enables you to build and share dashboards quickly and easily, extracting valuable business insights from your business data.
Article written for insight software.com on January 20, 2021.