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I continue with the third part in my series on enterprise system pitfalls and now discuss the problem of what I call the infrastructure imbalance. I have two previous posts that introduce the topic of pitfalls of enterprise systems and discuss the pitfall of over abstraction.

Today I continue my series on enterprise system pitfalls and discuss the problem of over abstraction. Be sure to read my previous post which lays the foundation for this series.

Are we getting the economic return we should be with new technology innovation? In this article, I’m starting a series reflecting on common weaknesses in enterprise systems development, and am going to try to unpack as concisely as I can these pitfalls we fall into.  We’ll analyze why we stumble into these problems, our struggle recognizing the root causes, and the results.

HU talks with Bob Diachenko, the cybersecurity expert who discovered the breach, about steps hotels can take to prevent data incidents

A groundbreaking new report by the Urban Land Institute in Washington, D.C. explores sustainability in the hospitality industry and examines ways in which hotels are incorporating eco-friendly best practices into both operations and construction. The study includes insights from leading hotel owners, developers and investors.



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Part 6: Flex Your Data Muscles: A 12-Month Challenge to Get Analytics Working For You

06/23/2015
by Samuel Ayisi

Last month we took a breather to reflect upon the various tasks we’ve undertaken so far in our 12-month “Flex Your Data Muscles” analytics challenge, and also to fine-tune our accomplishments. Hopefully, the challenge has given you a few ideas on how to kick-start your hospitality analytics journey or perhaps inspired you to improve your existing analytics efforts. If you’re just getting to know about the challenge, refer to the Hospitality Upgrade newsletter issued in January 2015 by clicking here.

Data Quality: Garbage In – Garbage Out

Your analytics is only as good as the quality of the data driving it. What could be more frustrating than investing a significant amount of time and resources into analytics only to realize that the data upon which it is based is corrupt thus making the resulting analytics…garbage? So what makes high-quality data? It depends on who you ask. However, a general consensus is that high-quality data should be; relevant and complete, clean, accurate, timely, and accessible on demand.

Making sure that data usable and of high-quality is critical to any analytics initiative. The proliferation of data sources within the hospitality vertical as well as the rapid growth of data volumes makes the continuous assurance of high-quality data a huge challenge. This is one of the reasons why data quality should be tackled at the strategic level within any hospitality business.

At the operational level, the integrity of the data starts from the point of entry. Ensuring that front-line personnel who enter data are well-trained and having adequate data control measures in place provides some level of assurance that high-quality data will end up in the analytics environment. Other measures such as auditing the quality and accuracy of the data as it flows through your organization and cleansing any corrupted data before it enters the analytics environment can help maintain the high quality of the resulting analytics.

A review of last month’s challenge: Time for a pit-stop

Last month’s task required you to pause and reflect on the tasks undertaken so far, refine the results achieved, and gather a few necessary requirements for upcoming tasks. I suggested a number of things that you could do including:

  • reviewing the various alternatives available to enable you close your identified data gaps
  • refining your list of data sources and possibly sharing it with others
  • simplifying your analytics objectives, and improving your understanding of the metrics and performance indicators for which you were accountable
  • at a strategic level, refining policies related to issues such as data quality, data security and privacy, and access control. 

I am hopeful that you were able to identify and undertake a few other tasks not mentioned above but deemed more specific and relevant to your analytics scenario.

Series Recap: What We’ve Done So Far
 
  

Month

What we did

January

Challenge #1 - Where is my data?

Identify and create a list of your various data sources (both internal & external).

February

Challenge #2 - What’s in my data source?

Figure out exactly what's in your data sources.

For example:

Source A has revenue, reservations, and guest information

Source B has revenue, expenses, and departments

OR

Revenue is located in Source A, Source D

Guest Information is located in Source A (summary), Source C (detailed)

March

Challenge #3 - What questions do I have?

Identify and list the top 5 business questions that you frequently need answered. It didn't matter whether you currently get all the required answers or it's on your wish list.

April

Challenge #4 - What data can help answer my questions?

Map your information needs to your data sources. The emphasis was to utilize the information gathered during the first 2 challenges, and identify any data gaps.

May

Challenge #5 – Time for pit-stop

Review & refine results achieved so far.

  


Challenge 6 (June): Let’s gather some data

Data gathering can be challenging

As mentioned last month, gathering relevant data for analytics can be quite challenging. Some of the reasons for these challenges include;

  • Strict data access controls: some corporate environments have such stringent data access controls to the extent that people who really need access to certain datasets are denied. In other cases, the process of getting access to data can be so intimidating and convoluted that most people are deterred from requesting access and willing to live with the consequences. This challenge is quite often encountered when dealing with external parties such as franchise owners and vendors.
  • Poor IT infrastructure: the existing IT infrastructure might make it virtually impossible to extract data with reasonable ease or without draining resources. Or perhaps, the existing infrastructure makes it very difficult for the hospitality business to handle the data received (e.g. insufficient data storage and processing capacity).
  • Obsolete software applications: the technology underlying some software applications are very outdated, thus making data extraction for analytics purposes virtually impossible. Due to their clout or dominant market position some of the vendors of such software applications are unwilling to invest in an upgrade of the outdated technology.
  • Unwillingness to share data: some people and organizations just don’t like sharing anything! It’s that simple.
  • Issues related to legal, regulatory, privacy, or intellectual property concerns.

These are just a few examples of the challenges you are most likely to encounter in your quest to gather data for analytics. Tough as they may seem, you have to explore all available options to ensure that you get the data you need for your analytics. Remember though, the need to strike a balance between the cost and consequences of acquiring the data and the benefits of the value the acquired data adds to your analytics.

Challenge
 

Basic

Advanced

·         Take any 2 of your top 5 business questions identified in Part 3 of the series, and gather all the data required to answer these questions.

·         For each of the data needs identified in Part 3 of the series, ensure all the required data is available and can be accessed by those who need it.

Comments & hints:

Basic Level:  This challenge requires you to actually gain possession of the data required for your analytics. After gaining possession of the data, it may need to be stored on your local computer or server. It is okay if the data is located in a data warehouse or in a software application (e.g. POS, PMS etc.). In this case you need appropriate access to retrieve the data. Go ahead and actually test your data retrieval process. Make sure that you can get and see the data you need. For both scenarios mentioned above, perform a quick visual scan of the data received to determine whether it is what you need and it is what it says it represents. Guesses or assumptions are not applicable at this stage. Look at the data.
 
You may encounter a few data access challenges when performing this task. Technology or skill-set related roadblocks can be resolved by seeking help from more knowledgeable colleagues or the IT team. The most difficult one to overcome may be justifying why you need access to the data, especially within organizations with very tight data access controls. Being tactful and demonstrating the value that the data will add to your analytics, as well as the value of the resulting analytics to your role and the organization as a whole would be helpful. If you cannot get access to the data you need to help answer your top priority business questions, then a discussion with the appropriate persons on what you are held accountable for may be required.

Advanced Level:  This task will enable you to test the effectiveness of your data access control policies, as well as check whether identified data gaps have been sufficiently closed. Individuals and departments should only see the data they need to do their jobs or what is needed to support accountable benchmarks, metrics and performance indicators.

Collaboration Forum

I encourage you to participate by commenting on the newsletter posts or via our forum (http://bigdataworkout.freeforums.net), to enable you to ask questions of each other, discuss how challenges were tackled, and also raise issues/problems that you encounter. Comments are meant to be interactive as well as educative, thus I’ll urge users to be respectful of each other.

About The Author
Samuel Ayisi
Head of Analytics
Leumas Solutions


Samuel Ayisi is the head of analytics with Leumas Solutions. He can be reached at sayisi@leumassolutions.com.

 
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