Tag: data sources

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erwin Expert Blog

Very Meta … Unlocking Data’s Potential with Metadata Management Solutions

Untapped data, if mined, represents tremendous potential for your organization. While there has been a lot of talk about big data over the years, the real hero in unlocking the value of enterprise data is metadata, or the data about the data.

However, most organizations don’t use all the data they’re flooded with to reach deeper conclusions about how to drive revenue, achieve regulatory compliance or make other strategic decisions. They don’t know exactly what data they have or even where some of it is.

Quite honestly, knowing what data you have and where it lives is complicated. And to truly understand it, you need to be able to create and sustain an enterprise-wide view of and easy access to underlying metadata.

This isn’t an easy task. Organizations are dealing with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and with little thought for downstream integration.

As a result, the applications and initiatives that depend on a solid data infrastructure may be compromised, leading to faulty analysis and insights.

Metadata Is the Heart of Data Intelligence

A recent IDC Innovators: Data Intelligence Report says that getting answers to such questions as “where is my data, where has it been, and who has access to it” requires harnessing the power of metadata.

Metadata is generated every time data is captured at a source, accessed by users, moves through an organization, and then is profiled, cleansed, aggregated, augmented and used for analytics to guide operational or strategic decision-making.

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.

To flip this 80/20 rule, they need an automated metadata management solution for:

• Discovering data – Identify and interrogate metadata from various data management silos.
• Harvesting data – Automate the collection of metadata from various data management silos and consolidate it into a single source.
• Structuring and deploying data sources – Connect physical metadata to specific data models, business terms, definitions and reusable design standards.
• Analyzing metadata – Understand how data relates to the business and what attributes it has.
• Mapping data flows – Identify where to integrate data and track how it moves and transforms.
• Governing data – Develop a governance model to manage standards, policies and best practices and associate them with physical assets.
• Socializing data – Empower stakeholders to see data in one place and in the context of their roles.

Addressing the Complexities of Metadata Management

The complexities of metadata management can be addressed with a strong data management strategy coupled with metadata management software to enable the data quality the business requires.

This encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossary maintenance, and metadata management (associations and lineage).

erwin has developed the only data intelligence platform that provides organizations with a complete and contextual depiction of the entire metadata landscape.

It is the only solution that can automatically harvest, transform and feed metadata from operational processes, business applications and data models into a central data catalog and then made accessible and understandable within the context of role-based views.

erwin’s ability to integrate and continuously refresh metadata from an organization’s entire data ecosystem, including business processes, enterprise architecture and data architecture, forms the foundation for enterprise-wide data discovery, literacy, governance and strategic usage.

Organizations then can take a data-driven approach to business transformation, speed to insights, and risk management.
With erwin, organizations can:

1. Deliver a trusted metadata foundation through automated metadata harvesting and cataloging
2. Standardize data management processes through a metadata-driven approach
3. Centralize data-driven projects around centralized metadata for planning and visibility
4. Accelerate data preparation and delivery through metadata-driven automation
5. Master data management platforms through metadata abstraction
6. Accelerate data literacy through contextual metadata enrichment and integration
7. Leverage a metadata repository to derive lineage, impact analysis and enable audit/oversight ability

With erwin Data Intelligence as part of the erwin EDGE platform, you know what data you have, where it is, where it’s been and how it transformed along the way, plus you can understand sensitivities and risks.

With an automated, real-time, high-quality data pipeline, enterprise stakeholders can base strategic decisions on a full inventory of reliable information.

Many of our customers are hard at work addressing metadata management challenges, and that’s why erwin was Named a Leader in Gartner’s “2019 Magic Quadrant for Metadata Management Solutions.”

Gartner Magic Quadrant Metadata Management

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erwin Expert Blog

Digital Transformation Examples: How Data Is Transforming the Hospitality Industry

The rate at which organizations have adopted data-driven strategies means there are a wealth of digital transformation examples for organizations to draw from.

By now, you probably recognize this recurring pattern in the discussions about digital transformation:

  • An industry set in its ways slowly moves toward using information technology to create efficiencies, automate processes or help identify new customer or product opportunities.
  • All is going fine until a new kid on the block, born in the age of IT and the internet, quickly starts to create buzz and redefine what customers expect from the industry.
  • To keep pace, the industry stalwarts rush into catch-up mode but make inevitably mistakes. ROI doesn’t meet expectations, the customer experience isn’t quite right, and data gets exposed or mishandled.

There’s one industry we’re all familiar with that welcomes billions of global customers every year; that’s in the midst of a strong economic run; is dealing with high-profile disruptors; and suffered a very public data breach to one of its storied brands in 2018 that raised eyebrows around the world.

Welcome to the hospitality industry.

The hotel and hospitality industry was expected to see 5 to 6 percent growth in 2018, part of an impressive run of performance fueled by steady demand, improved midmarket offerings, and a new supply of travelers from developing regions.

All this despite challenges from upstarts like AirB2B, HomeAway and Couchsurfing plus a data breach at Marriott/Starwood that exposed the data of 500 million customers.

Digital Transformation Examples: Data & the Hospitality Industry

Online start-ups such as Airbnb, HomeAway and Couchsurfing are some of the most clear cut digital transformation examples in the hospitality industry.

Digital Transformation Examples: Hospitality – Data, Data Everywhere

As with other industries, digital transformation examples in the hospitality industry are abundant – and in turn, those businesses are awash in data with sources that include:

  • Data generated by reservations and payments
  • The data hotels collect to drive their loyalty programs
  • Data used to enhance the customer experience
  • Data shared as part of the billions of handoffs between hotel chains and the various booking sites and agencies that travelers use to plan trips

But all of this data, which now permeates the industry, is relatively new.

“IT wasn’t always a massive priority for [the hospitality industry],” says Danny Sandwell, director of product marketing for erwin, Inc. “So now there’s a lot of data, but these organizations often have a weak backend.”

The combination of data and analytics carries a great deal of potential for companies in the hospitality industry. Today’s demanding customers want experiences, not just a bed to sleep in; they want to do business with brands that understand their likes and dislikes; and that send offers relevant to their interests and desired destinations.

All of this is possible when a business collects and analyzes data on the scale that many hotel brands do. However, all of this can fail loudly if there is a problem with that data.

Getting a return on their investments in analytics and marketing technology requires hospitality companies to thoroughly understand the source of their data, the quality of the data, and the relevance of the data. This is where data governance comes into play.

When hospitality businesses are confident in their data, they can use it a number of ways, including:

  • Customer Experience: Quality data can be used to power a best-in-class experience for hotels in a number of areas, including the Web experience, mobile experience, and the in-person guest experience. This is similar to the multi-channel strategy of retailers hoping to deliver memorable and helpful experiences based on what they know about customers, including the ability to make predictions and deliver cross-sell and up-sell opportunities. 
  • Mergers and Acquisitions: Hospitality industry disruptors have some industry players thinking about boosting their businesses via mergers and acquisitions. Good data can identify the best targets and help discover the regions or price points where M&A makes the most sense and will deliver the most value. Accurate data can also help pinpoint the true cost of M&A activity.
  • Security: Marriott’s data breach, which actually began as a breach at Starwood before Marriott acquired it, highlights the importance of data security in the hospitality industry. Strong data governance can help prevent breaches, as well as help control breaches so organizations more quickly identify the scope and action behind a breach, an important part of limiting damage.
  • Partnerships: The hospitality industry is increasingly connected, not just because of booking sites working with dozens of hotel brands but also because of tour operators turning a hotel stay into an experience and transportation companies arranging travel for guests. Providing a room is no longer enough.

Data governance is not an application or a tool. It is a strategy. When it is done correctly and it is deployed in a holistic manner, data governance becomes woven into an organization’s business processes and enterprise architecture.

It then improves the organization’s ability to understand where its data is, where it came from, its value, its quality, and how the data is accessed and used by people and applications.

It’s this level of data maturity that provides comfort to employees – from IT staff to the front desk and everyone in between – that the data they are working with is accurate and helping them better perform their jobs and improve the way they serve customers.

Over the next few weeks, we’ll be looking closely at digital transformation examples in other sectors, including retail and government. Subscribe to to stay in the loop.

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Healthy Co-Dependency: Data Management and Data Governance

Data management and data governance are now more important than ever before. The hyper competitive nature of data-driven business means organizations need to get more out of their data than ever before – and fast.

A few data-driven exemplars have led the way, turning data into actionable insights that influence everything from corporate structure to new products and pricing. “Few” being the operative word.

It’s true, data-driven business is big business. Huge actually. But it’s dominated by a handful of organizations that realized early on what a powerful and disruptive force data can be.

The benefits of such data-driven strategies speak for themselves: Netflix has replaced Blockbuster, and Uber continues to shake up the taxi business. Organizations indiscriminate of industry are following suit, fighting to become the next big, disruptive players.

But in many cases, these attempts have failed or are on the verge of doing so.

Now with the General Data Protection Regulation (GDPR) in effect, data that is unaccounted for is a potential data disaster waiting to happen.

So organizations need to understand that getting more out of their data isn’t necessarily about collecting more data. It’s about unlocking the value of the data they already have.

Data Management and Data Governance Co-Dependency

The Enterprise Data Dilemma

However, most organizations don’t know exactly what data they have or even where some of it is. And some of the data they can account for is going to waste because they don’t have the means to process it. This is especially true of unstructured data types, which organizations are collecting more frequently.

Considering that 73 percent of company data goes unused, it’s safe to assume your organization is dealing with some if not all of these issues.

Big picture, this means your enterprise is missing out on thousands, perhaps millions in revenue.

The smaller picture? You’re struggling to establish a single source of data truth, which contributes to a host of problems:

  • Inaccurate analysis and discrepancies in departmental reporting
  • Inability to manage the amount and variety of data your organization collects
  • Duplications and redundancies in processes
  • Issues determining data ownership, lineage and access
  • Achieving and sustaining compliance

To avoid such circumstances and get more value out of data, organizations need to harmonize their approach to data management and data governance, using a platform of established tools that work in tandem while also enabling collaboration across the enterprise.

Data management drives the design, deployment and operation of systems that deliver operational data assets for analytics purposes.

Data governance delivers these data assets within a business context, tracking their physical existence and lineage, and maximizing their security, quality and value.

Although these two disciplines approach data from different perspectives (IT-driven and business-oriented), they depend on each other. And this co-dependency helps an organization make the most of its data.

The P-M-G Hub

Together, data management and data governance form a critical hub for data preparation, modeling and data governance. How?

It starts with a real-time, accurate picture of the data landscape, including “data at rest” in databases, data warehouses and data lakes and “data in motion” as it is integrated with and used by key applications. That landscape also must be controlled to facilitate collaboration and limit risk.

But knowing what data you have and where it lives is complicated, so you need to create and sustain an enterprise-wide view of and easy access to underlying metadata. That’s a tall order with numerous data types and data sources that were never designed to work together and data infrastructures that have been cobbled together over time with disparate technologies, poor documentation and little thought for downstream integration. So the applications and initiatives that depend on a solid data infrastructure may be compromised, and data analysis based on faulty insights.

However, these issues can be addressed with a strong data management strategy and technology to enable the data quality required by the business, which encompasses data cataloging (integration of data sets from various sources), mapping, versioning, business rules and glossaries maintenance and metadata management (associations and lineage).

Being able to pinpoint what data exists and where must be accompanied by an agreed-upon business understanding of what it all means in common terms that are adopted across the enterprise. Having that consistency is the only way to assure that insights generated by analyses are useful and actionable, regardless of business department or user exploring a question. Additionally, policies, processes and tools that define and control access to data by roles and across workflows are critical for security purposes.

These issues can be addressed with a comprehensive data governance strategy and technology to determine master data sets, discover the impact of potential glossary changes across the enterprise, audit and score adherence to rules, discover risks, and appropriately and cost-effectively apply security to data flows, as well as publish data to people/roles in ways that are meaningful to them.

Data Management and Data Governance: Play Together, Stay Together

When data management and data governance work in concert empowered by the right technology, they inform, guide and optimize each other. The result for an organization that takes such a harmonized approach is automated, real-time, high-quality data pipeline.

Then all stakeholders — data scientists, data stewards, ETL developers, enterprise architects, business analysts, compliance officers, CDOs and CEOs – can access the data they’re authorized to use and base strategic decisions on what is now a full inventory of reliable information.

The erwin EDGE creates an “enterprise data governance experience” through integrated data mapping, business process modeling, enterprise architecture modeling, data modeling and data governance. No other software platform on the market touches every aspect of the data management and data governance lifecycle to automate and accelerate the speed to actionable business insights.