Tag: data maturity

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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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Data Governance: Your Engine for Driving Results

In my previous post, I described how organizational success depends on certain building blocks that work in alignment with common business objectives. These building blocks include business activities, data and analytics.

Governance is also one of the required building blocks because it provides cohesion in the standards to align people, processes, data and technology for successful and sustainable results. Although it has been somewhat of an abstract concept, data governance is foundational to helping organizations use data as a corporate asset.

Assets are acquired and used to help organizations execute their business models. Principles of asset management require that assets be cataloged, inventoried, protected and accessible to authorized people with the skills and experience to optimize them.

Assets typically generate more value if they have high levels of utilization. In the context of data, this means governed data assets will be more valuable if they strengthen existing operations and guide improvements, supported by analytics.

As organizations seek to unlock more value by implementing a wider analytics footprint across more business functions, data governance will guide their journeys.

 A New Perspective on Data

Becoming a data-driven enterprise means making decisions based on empirical evidence, not a “gut feeling.” This transformation requires a clear vision, strategy and disciplined execution. The desired business opportunity must be well thought out, understood and communicated to others – from the C suite to the front lines.

Organizations that want to succeed in the digital age understand that their cultures and therefore their decision-making processes must become more proactive and collaborative. Of course, data is at the core of business performance and continuous improvement.

In this modern era of Big Data, non-traditional data sets generated externally are being blended with traditional data generated internally. As such, a key element of data-driven success involves changing the long-held perspective of data as a cost center, with few if any investments made to unlock its value to the organization.

Being data-driven, based on analytics, changes this mindset. Business leaders are indeed starting to realize that making data more accessible and useful throughout the organization contributes to the results they want to achieve – and must report to their boards.

If traditional asset management concepts are applied to data, then objectives for security, quality, cataloging, definition, confidence, authorization and accessibility can be defined and achieved. These areas then become the performance criteria of the new data asset class.

So transforming an organization’s leadership and the rest of its culture to perceive and treat data as an asset changes its classification from “cost” to “investment.” Valuable assets earn a financial return and fuel productivity. They also can be re-invested or re-purposed.

Data governance is key to this new perspective of data as an asset.

Data Governance Definition and Purpose

Data governance is important to the modern economy because it enables the transformation of data into valuable assets to improve top- and bottom-line performance. Well-governed data is accessible, useful and relevant across a range of business improvement use cases.

But in the early stages of implementing data governance, organizations tend to have trouble defining it and organizing it, including determining which tasks are involved.

At its core, data governance is a cross-functional program that develops, implements, monitors and enforces policies that improve the performance of select data assets.

Implementing data governance ensures that “asset-grade” data is available to support decision-making, based on advanced analytics. Using this rationale, potential objectives to meet the strategic intent of the organization can be defined to derive value.

Following is a list of possible objectives for a data governance program:

  • Improve data security
  • Increase data quality
  • Make data more accessible to more stakeholders
  • Increase data understanding
  • Raise the confidence of data consumers
  • Increase data literacy and determine the data-driven maturity level of the organization

Building a Data Governance Foundation

The scope and structure of a data governance program are important to determine and include responsibilities, accountabilities, decision rights and authority levels, in addition to how the program fits into the existing corporate structure in terms of virtual or physical teams.

Structural options include top-down command and control and bottom up collaborative networks. Executive accountability also should be outlined.

It’s common for a data executive, such as the chief data officer, to be identified as accountable for overall data governance results. Data owners are business leaders who manage the processes that generate critical data. They’re responsible for defining the polices that support the program’s objectives.

Data stewards report to the data owners and are responsible for translating data policies into actions assigned to data specialists. The data specialists execute projects and other workflows to ensure that the governed data conforms to the intent of the policies.

Data stewards form the backbone of a data governance initiative. They influence how data is managed by assigning tasks to the specialists. Data stewards are responsible for cataloging, defining and describing the governed data assets.

These roles may be full-time or part-time, depending on the scope of the work.

Key processes carried out by the data governance team include:

  1. Defining and planning the program’s scope
  2. Data quality improvement
  3. Data security improvement
  4. Metadata creation and management
  5. Evaluating the suitability of new data sources
  6. Monitoring and enforcing compliance to data policies
  7. Researching new data sources
  8. Training to improve data literacy of staff at all levels
  9. Facilitating and finding new data-driven opportunities to improve the business
  10. Leading and managing cultural change

Data governance is based on a strategy that defines how data assets should look and perform, including levels of quality, security, integration, accessibility, etc. The design and implementation of a data governance program should start with a limited scope and then gradually ramp up to support the overall business strategy. So think big, but start small.

The next post in the series explores how data governance helps implement sustainable business processes that produce measurable results over time. Click here to continue reading on.

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SQL, NoSQL or NewSQL: Evaluating Your Database Options

A common question in the modern data management space involves database technology: SQL, NoSQL or NewSQL?

But there isn’t a one-size-fits-all answer. What’s “right” must be evaluated on a case-by-case basis and is dependent on data maturity.

For example, a large bookstore chain with a big-data initiative would be stifled by a SQL database. The advantages that could be gained from analyzing social media data (for popular books, consumer buying habits) couldn’t be realized effectively through sequential analysis. There’s too much data involved in this approach, with too many threads to follow.

However, an independent bookstore isn’t necessarily bound to a big-data approach because it may not have a mature data strategy. It might not have ventured beyond digitizing customer records, and a SQL database is sufficient for that work.

Having said that, the “SQL, NoSQL or NewSQL” question is gaining prominence because businesses are becoming increasingly data-driven.

In 2019, an IDC study found 85% of enterprise decision-makers said they had a time frame of two years to make significant inroads into digital transformation or they will fall behind their competitors and suffer financially. Furthermore, a Progress study showed that 85% of enterprise decision-makers feel they only have two years to make significant digital-transformation progress before suffering financially and/or falling behind competitors.

Considering these statistics, what better time than now to evaluate your database technology? The “SQL, NoSQL or NewSQL question,” is especially important if you intend to become more data-driven.

SQL, NoSQL or NewSQL: Advantages and Disadvantages

SQL

SQL databases are tried and tested, proven to work on disks using interfaces with which businesses are already familiar.

As the longest-standing type of database, plenty of SQL options are available. This competitive market means you’ll likely find what you’re looking for at affordable prices.

Additionally, businesses in the earlier stages of data maturity are more likely to have a SQL database at work already, meaning no new investments need to be made.

However in the modern digital business context, SQL databases weren’t made to support the the three Vs of data. The volume is too high, the variety of sources is too vast, and the velocity (speed at which the data must be processed) is too great to be analyzed in sequence.

Furthermore, the foundational, legacy IT world they were purpose-built to serve has evolved. Now, corporate IT departments must be agile, and their databases must be agile and scalable to match.

NoSQL

Despite its name, “NoSQL” doesn’t mean the complete absence of the SQL database approach. Rather, it works as more of a hybrid. The term is a contraction of “not only SQL.”

So, in addition to the advantage of continuity that staying with SQL offers, NoSQL enjoys many of the benefits of SQL databases.

The key difference is that NoSQL databases were developed with modern IT in mind. They are scalable, agile and purpose-built to deal with disparate, high-volume data.

Hence, data is typically more readily available and can be changed, stored or handle the insertion of new data more easily.

For example, MongoDB, one of the key players in the NoSQL world, uses JavaScript Object Notation (JSON). As the company explains, “A JSON database returns query results that can be easily parsed, with little or no transformation.” The open, human- and machine-readable standard facilitates data interchange and can store records, “just as tables and rows store records in a relational database.”

Generally, NoSQL databases are better equipped to deal with other non-relational data too. As well as JSON, NoSQL supports log messages, XML and unstructured documents. This support avoids the lethargic “schema-on-write,” opting to “schema-on-read” instead.

NewSQL

NewSQL refers to databases based on the relational (SQL) database and SQL query language. In an attempt to solve some of the problems of SQL, the likes of VoltDB and others take a best-of-both-worlds approach, marrying the familiarity of SQL with the scalability and agile enablement of NoSQL.

However, as with most seemingly win-win opportunities, NewSQL isn’t without its caveats. These vary from vendor to vendor, but in essence, you either have to sacrifice familiarity side or scalability.

If you’d like to speak with someone at erwin about SQL, NoSQL or NewSQL in more detail, click here.

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