Near as I can tell, there is one lever businesses have yet to pull in order to connect with customers, optimize processes, maximize sales opportunities and deliver game-changing results. Data.
While many enterprises talk about Digital Transformation, and even have Digital Transformation initiatives in flight, how many are investing in the building blocks required to make it happen? How many leaders are speaking a language that instills passion about data? Incentivizing their teams to make it happen?
It’s more than dumping data in a Lake. More than employing robotic process automation. More than hiring a staff of data scientists. It’s actually less about homogenizing the data itself and more about harmonizing the way we interact with the data. Fred Smith of FedEx was a visionary when it came to Digital Transformation. When he founded his company and he said, “Information about the package is as important as the package itself”. Think about that.
Digital Transformation is the integration of digital technology into all aspects of an enterprise to alter how they operate internally, externally and how they deliver state-of-the-art products and services to customers. Data is the glue for systems, interfaces, solutions, etc. Essentially, making data agnostic to the various technologies and easy for all participants (customers, vendors, employees, etc.) to collaborate with trusted information.
I spent years in supply chain where leaders would often “find fault, assess blame, and if time permits, solve the problem.” Followed by years in “big data,” working with data scientists and analysts that would be forced to spend 80% of their time finding, integrating and cleaning up the data before they could do their big brain work. This ultimately led me to my current passion, which believe it or not, is data quality. I know, data quality and governance are by far the least sexy terms on the planet. Data? Quality? Governance? Who wants to be governed?
At its core, Digital Transformation is only possible with a solid data strategy and high-quality data as a first step.
The four key components are a Data Quality Framework and Technology, Operating Model, Decision Tree and Measurement Model.
Data Quality Framework enabled with a complementary technology platform ensures the availability, usability, integrity and sustainability of our most critical data.
Operating Model provides a structured and repeatable process, aligned to an organizational construct, for sustained data integrity and value creation.
Decision Tree ensures that data quality measures (and related data governance actions) are always based on organizational value drivers and follow a structured, repeatable and scalable governance model.
Measurement Model organizes critical data quality metrics to inform business performance and dimensionalizes the information for proper analysis and action. Additionally, business KPI’s and metrics should be in scope to continue to show progress toward business objectives.
With these elements in place, data can seamlessly flow around the enterprise, automating and enabling manual and labor-intensive processes today (ETL), and delivering confidence that is assured (and testable).
With solid data and the right technologies in place, companies can analyze vast amounts of real-time information from mobile applications, social media, IoT devices and more. Digital innovation solutions provide a streamlined architecture and the self-service data analysis tools allow business users across the organization to create customer-focused digital products and services.
One example is an enterprise data intelligence platform that combines data governance with data quality and analytics. An integrated platform provides critical visibility into the quality of information across the data landscape, enabling self-service data extraction, transformation and analysis.
Ultimately, with the right technologies, aligned to the business process, and an enabled community approach to data management, businesses can optimize and automate a variety of operational activities. Merging all data management efforts under a single, enterprise-wide data quality framework, organizations can scale their data standards and data quality rules across all applications and processes. As a result, they can automate metadata and lineage ingestion to automatically profile and discover patterns, recognize and resolve operational efficiencies, deliver high-quality data to business users, reduce time to market and ultimately, produce customer-centric, digital products and services.
I’ve seen companies try to stand-up a framework without an operating model. Doesn’t work. I’ve also seen companies have a great organization and operating model, but without a tool/framework. Doesn’t work. Successful data programs have all four components mentioned above.
Successful data governance is foundational for Digital Transformation.
At the end of the day, data governance can’t be a diet. It’s a lifestyle change.
Are you looking for more information on the increasing importance of Digital Transformation? Download the white paper above or below to learn more.
For additional resources on Digital Transformation, check out this article from CIO https://www.cio.com/article/3211428/what-is-digital-transformation-a-necessary-disruption.html.
For a deeper dive into this topic, visit our resource center. Here you will find a broad selection of content that represents the compiled wisdom, experience, and advice of our seasoned data experts and thought leaders.