Having a clearly defined digital transformation strategy is an essential best practice for successful digital transformation. But what makes a digital transformation strategy viable?
Part Two of the Digital Transformation Journey …
In our last blog on driving digital transformation, we explored how business architecture and process (BP) modeling are pivotal factors in a viable digital transformation strategy.
EA and BP modeling squeeze risk out of the digital transformation process by helping organizations really understand their businesses as they are today. It gives them the ability to identify what challenges and opportunities exist, and provides a low-cost, low-risk environment to model new options and collaborate with key stakeholders to figure out what needs to change, what shouldn’t change, and what’s the most important changes are.
Once you’ve determined what part(s) of your business you’ll be innovating — the next step in a digital transformation strategy is using data to get there.
Constructing a Digital Transformation Strategy: Data Enablement
Many organizations prioritize data collection as part of their digital transformation strategy. However, few organizations truly understand their data or know how to consistently maximize its value.
If your business is like most, you collect and analyze some data from a subset of sources to make product improvements, enhance customer service, reduce expenses and inform other, mostly tactical decisions.
The real question is: are you reaping all the value you can from all your data? Probably not.
Most organizations don’t use all the data they’re flooded with to reach deeper conclusions or make other strategic decisions. They don’t know exactly what data they have or even where some of it is, and they struggle to integrate known data in various formats and from numerous systems—especially if they don’t have a way to automate those processes.
How does your business become more adept at wringing all the value it can from its data?
The reality is there’s not enough time, people and money for true data management using manual processes. Therefore, an automation framework for data management has to be part of the foundations of a digital transformation strategy.
Your organization won’t be able to take complete advantage of analytics tools to become data-driven unless you establish a foundation for agile and complete data management.
You need automated data mapping and cataloging through the integration lifecycle process, inclusive of data at rest and data in motion.
An automated, metadata-driven framework for cataloging data assets and their flows across the business provides an efficient, agile and dynamic way to generate data lineage from operational source systems (databases, data models, file-based systems, unstructured files and more) across the information management architecture; construct business glossaries; assess what data aligns with specific business rules and policies; and inform how that data is transformed, integrated and federated throughout business processes—complete with full documentation.
Without this framework and the ability to automate many of its processes, business transformation will be stymied. Companies, especially large ones with thousands of systems, files and processes, will be particularly challenged by taking a manual approach. Outsourcing these data management efforts to professional services firms only delays schedules and increases costs.
With automation, data quality is systemically assured. The data pipeline is seamlessly governed and operationalized to the benefit of all stakeholders.
Constructing a Digital Transformation Strategy: Smarter Data
Ultimately, data is the foundation of the new digital business model. Companies that have the ability to harness, secure and leverage information effectively may be better equipped than others to promote digital transformation and gain a competitive advantage.
While data collection and storage continues to happen at a dramatic clip, organizations typically analyze and use less than 0.5 percent of the information they take in – that’s a huge loss of potential. Companies have to know what data they have and understand what it means in common, standardized terms so they can act on it to the benefit of the organization.
Unfortunately, organizations spend a lot more time searching for data rather than actually putting it to work. In fact, data professionals spend 80 percent of their time looking for and preparing data and only 20 percent of their time on analysis, according to IDC.
The solution is data intelligence. It improves IT and business data literacy and knowledge, supporting enterprise data governance and business enablement.
It helps solve the lack of visibility and control over “data at rest” in databases, data lakes and data warehouses and “data in motion” as it is integrated with and used by key applications.
Organizations need a real-time, accurate picture of the metadata landscape to:
- Discover data – Identify and interrogate metadata from various data management silos.
- Harvest data – Automate metadata collection from various data management silos and consolidate it into a single source.
- Structure and deploy data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.
- Analyze metadata – Understand how data relates to the business and what attributes it has.
- Map data flows – Identify where to integrate data and track how it moves and transforms.
- Govern data – Develop a governance model to manage standards, policies and best practices and associate them with physical assets.
- Socialize data – Empower stakeholders to see data in one place and in the context of their roles.
The Right Tools
When it comes to digital transformation (like most things), organizations want to do it right. Do it faster. Do it cheaper. And do it without the risk of breaking everything. To accomplish all of this, you need the right tools.
The erwin Data Intelligence (DI) Suite is the heart of the erwin EDGE platform for creating an “enterprise data governance experience.” erwin DI combines data cataloging and data literacy capabilities to provide greater awareness of and access to available data assets, guidance on how to use them, and guardrails to ensure data policies and best practices are followed.
erwin Data Catalog automates enterprise metadata management, data mapping, reference data management, code generation, data lineage and impact analysis. It efficiently integrates and activates data in a single, unified catalog in accordance with business requirements. With it, you can:
- Schedule ongoing scans of metadata from the widest array of data sources.
- Keep metadata current with full versioning and change management.
- Easily map data elements from source to target, including data in motion, and harmonize data integration across platforms.
erwin Data Literacy provides self-service, role-based, contextual data views. It also provides a business glossary for the collaborative definition of enterprise data in business terms, complete with built-in accountability and workflows. With it, you can:
- Enable data consumers to define and discover data relevant to their roles.
- Facilitate the understanding and use of data within a business context.
- Ensure the organization is fluent in the language of data.
With data governance and intelligence, enterprises can discover, understand, govern and socialize mission-critical information. And because many of the associated processes can be automated, you reduce errors and reliance on technical resources while increasing the speed and quality of your data pipeline to accomplish whatever your strategic objectives are, including digital transformation.
Check out our latest whitepaper, Data Intelligence: Empowering the Citizen Analyst with Democratized Data.
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