data-driven transformation Archives

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 …

Part one

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.

We began a new blog series last week on business process (BP) modeling and its role within the enterprise. This week’s focus is on the connection between business process modeling and standard operating procedures. Specifically, using BP tools to help organizations streamline how they manage their standard operating procedures (SOPs).

Standard Operating Procedures: A New Approach to Organizing SOP Information

Manually maintaining the standard operating procedures that inform business processes can be a monster of a task. In most industries, SOPs typically are documented in multiple Word or Excel files.

In a process-centric world, heavy lifting is involved when an organization requires a change to an end-to-end process: Each SOP affected by the change may be associated with dozens or even hundreds of steps that exist between the start and conclusion of the process – and the alteration must be made to all of them wherever they occur.

You can imagine the significant man hours that go into wading through a sea of documents to discover and amend relevant SOPs and communicate these business process-related changes across the organization. And you can guess at the toll on productivity and efficiency that the business experiences as a result.

Companies that are eager to embrace business process optimization are keen to have a better approach to organizing SOP information to improve transparency and insight for speedier and more effective change management.

There’s another benefit to be realized from taking a new approach to SOP knowledge management, as well. With better organization comes an increased ability to convey information about current and changed standard operating procedures; companies can offer on-the-fly access to standard practices to teams across the enterprise.

That consistent and easily obtained business process information can help employees innovate, sharing ideas about additional improvements and innovations that could be made to standard operating procedures. It could also save them the time they might otherwise spend on “reinventing the wheel” for SOPs that already exist but that they don’t know about.

Balfour Beatty Construction, the fourth largest general builder in the U.S., saw big results when it standardized and transformed its process documentation, giving workers access to corporate SOPs from any location on almost any device.

As a construction company, keeping field workers out of danger is a major issue, and providing these employees with immediate information about how to accomplish a multi-step business process – such as clearing a site – can promote their safety. Among benefits it saw were a 5% gain in productivity and a reduction in training time for new employees who were now able to tap directly into SOP data.

Using Business Process Modeling to Transform SOP Management

How does a company transform manual SOP documentation to more effectively support change management as part of business process optimization? It’s key to adopt business process (BP) modeling and management software to create and store SOP documentation in a single repository, tying them to the processes they interact with for faster discovery and easier maintenance.

Organizations that move to this methodology, for example, will have the advantage of only needing to change an affected SOP in that one repository; the change automatically will propagate to all related processes and procedures.

In effect, the right BP tool automatically generates new SOPs with the necessary updated information.

Such a tool is also suitable for use in conjunction with controlled document repositories that are typically required in heavily regulated industries, such as pharmaceuticals, financial services and healthcare, as part of satisfying compliance mandates. All SOP documentation already is stored in the same repository, rather than scattered across files.

But a business process diagramming and modeling solution comes in handy in these cases by providing a web-based front-end that exposes high-end processes and how they map to related SOPs. This helps users better navigate them to institute and maintain changes and to access job-related procedure information.

To find out about how erwin can streamline SOP document management to positively impact costs, workloads and user benefits, please click here.

In our next blog, we’ll look at how business process modeling strengthens digital transformation initiatives.

Part one

Constructing a Digital Transformation Strategy: Data Enablement

Constructing a Digital Transformation Strategy: Smarter Data

The Right Tools

Standard Operating Procedures: A New Approach to Organizing SOP Information

Using Business Process Modeling to Transform SOP Management