By Rex Ahlstrom, BackOffice Associates
The enterprise data journey is multi-faceted, requiring the marriage of systems integration, data migration, data quality, data governance and data archiving throughout the lifetime of your corporate data. Underpinning this process is the new concept of data "modernization," which involves analyzing and preparing data to become holistically integrated into these processes.
Data modernization facilitates the overarching business processes that drive organizations’ success by coordinating and optimizing these events across the data lifecycle. To effectively unlock the power of data, businesses can now incorporate next-generation applications to support all requirements for data consistency, reliability, reusability and execution across information governance projects. When we talk about maximizing the effectiveness of integration, migration, data quality, governance and archiving, what we are really talking about is establishing trusted business processes.
By comparison, IT tools were not specifically designed to execute business processes; they generally handle activities at a lower level. IT tools do not provide a business process that oversees more than their specific function, and they are not typically built to add the all-important people and process parts to the equation of "people plus process plus technology" to their solutions.
Here are three main strategies to keep in mind throughout the data modernization process, to ensure that your business is working as efficiently as it should be:
Focus on Data Strategy and Data Quality
Do not underestimate the importance of a well-managed data governance team to document the processes and define the data standards and strategy to support those processes. These teams can also be used to address "data chaos" in the company, as well as manage the migration of data from any source to new systems, such as SAP S/4 HANA in the cloud.
Equally as important is the data quality aspect of data modernization. It is undeniable that poor data quality costs companies huge hits to the bottom line. The average organization loses $8.2 million annually due to poor data quality, a survey by Gartner revealed.
Is your data "business ready"? Do you have established rules that passively or actively monitor your data? In many cases, once the data resides in the system, it is almost forgotten and essentially becomes a part of the machine until needed. If those data have been interacted with without quality control, it is likely they are wrought with errors.
As Cindy Hoots, vice president of the beer manufacturing and distributing firm SABMiller said, "For the business to operate optimally, we are dependent on trusted and accurate master data; for example, we need reliable customer data to ensure we can take orders and distribute based on those orders." She added, "We also rely on accurate material master data to ensure we have the information we need to manufacture the right product to the right specifications."
Understand Data Relationships across the Business
In order for businesses to use data most effectively, we must understand the relationship of the data across the business. A combination of technology and services is needed for continuous data modernization. In the data modernization process we often talk with one area of an organization and find that their data is applicable to many other departments.
To connect data on a companywide level, a core group of people from each department should direct the enterprise’s path to data modernization. Until this type of broad approach is used, there will inevitably be roadblocks and difficulty reaching key business objectives. Ultimately data can, and should, be integrated across all departments within a company.
A key piece of data modernization involves analyzing the data to uncover the business objectives for both the group and the overall company. Often the company will have broad business objectives that are supported by data, but people within the business typically look at data with blinders on, focusing only on specific customer systems, procurement or manufacturing elements, and forgetting to look across the many different types of data one layer up.
In the marketplace, we need to get beyond a mere tacit understanding of this problem by very few. Though this is integral to the data modernization process, many enterprises have yet to successfully address this issue.
What is the rule of commerce today? Speed. Commerce happens as quickly as you can think. Data is shared among suppliers, partners, customers and service providers in the blink of eye. We are now able to establish complex, integrated webs of applications but there are still systems with data that have never been modernized. The combination of the original data and the potential data quality issues that have worked their way into systems can wreak havoc on the entire system as whole and render it unreliable.
Data modernization is essential for successfully interfacing data across many different types of systems while ensuring highest quality standards in real time.
Keep a Flexible Data Platform
The final key to successful data modernization is using a platform that is flexible enough to be globally useful. This can happen through a combination of software and prebuilt content that provides a "template" approach to information governance, where processes and work product can be saved and reused across any migration, data quality or data management project.
A flexible platform also includes the ability to manage the "people" component, from teams to tasks to the importing and manipulating of existing project plans. Providing configurable, on-the-fly reporting and dashboards, as well as remote access through mobile devices for executives, helps unify, visualize and track the effectiveness of the information governance program.
Rex Ahlstrom is chief strategy officer at BackOffice Associates, a worldwide leader in information governance and data migration solutions, focusing on helping customers manage one of their most critical assets – data.