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Flex Your Data Muscles: Take the 12-month Big Data Challenge Part 5 in a Series

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June 12, 2015
Big Data Challenge
Samuel Ayisi - sayisi@leumassolutions.com

We are nearly halfway through the 12-month “Flex Your Data Muscles” analytics challenge, and so far so good. If you’re interested in learning more about the challenge or would like to participate, refer to the Hospitality Upgrade newsletter from January 26, 2015 or the Spring 2015 edition of this magazine for further details.

How relevant is the data you gather?
Quite often, organizations and individuals interested in analytics are tempted to gather all the available data prior to deciding what analytics needs to be done. However in most hospitality settings, this should be an exception rather than the norm. If your analytics initiative is not fully matured, then a more prudent approach would be to precisely define the business questions that need answers, identify the data needed to help answer those questions, and then map the data to the questions to ensure that the data gathered is relevant to the analytics objectives.

Some may argue that it’s getting much easier and less expensive to gather data; so why not collect as much data as possible and then later sift through it to find the panacea? I will counter that argument by stating that the amount of time and resources spent sifting through oceans of irrelevant data could be better utilized analyzing relevant and better focused data sets, evaluating and improving the quality of the relevant data, and determining how the resulting insight from the analytics can be used to improve business outcomes. Most organizations and individuals in the hospitality industry may already be overwhelmed by volumes of data and reports, thus reducing the information overload could be beneficial in many aspects. My counter argument doesn’t diminish the fact that with the advent of Big Data concepts and tools there might be scenarios in which the sifting has to be done after data collection. But for most hospitality businesses, these scenarios are currently rarely applicable. I also want to emphasize that meaningful analytics is all about relevance and value added. Your decision makers could be provided with very insightful analytics, but if it is irrelevant to their roles or the decisions they make, and does not add any value to what they do, then you may have been ineffective and inefficient in the use of your time and resources.

From a strategic perspective, the relevance and quality of the data being made available for analytics should be a priority and it must be formally incorporated into your data governance. In many organizations across all industries, data is now considered as one of the most valuable assets, thus making its relevance and quality all the more important.

A final comment on data relevance is that in the same way that flexing your body muscles requires relevant and targeted exercises, the ability to effectively flex your data muscles also requires relevant and better focused data sets, not necessarily a lot of data.

A Review of Last Month’s Challenge:

What data can help answer my questions?
For our fourth challenge, the task was to map the types of information available (and data sources) to your priority business questions that frequently needed answers. The types of information available were identified during the first two stages, and the identification of your priority business questions was the focus of the third challenge. The goal of last month’s challenge was to help you come up with a list of the relevant data you need for your analytics.

The intent was for you to rely as much as possible on your list of data sources and information types. As you went through the challenge, you may have encountered a number of data gaps, i.e., you do not have the data you need to help answer the questions. I hope you took the initiative to categorize the data gaps into critical, required and nice-to-have. The identification of data gaps is critical to any analytics endeavor, as it helps you determine whether or not you are ready to conduct any meaningful analytics. The question becomes what do you do about these gaps? It is incumbent upon you and your organization to decide whether the effort and resources required to obtain the data to fill these gaps are worth the value added to the resulting analytics.

For those taking the advanced challenge, you may have also encountered similar data gaps related to your benchmarks and performance indicators. A similar onus lies upon you to determine whether obtaining the additional data is worth the effort. If it is not worth the trouble, then your performance indicators and benchmarks will have to be revised to conform to the available data and this will most likely affect performance management and other strategic initiatives within your organization.

Challenge No. 5:
Time for a Pit Stop

Tougher challenges lie ahead in your analytics journey

So far we have completed a few of the fundamental steps required to undertake a successful analytics journey. These fundamentals provide us with a solid foundation and are critical to the remainder of the journey. However, tougher challenges lie ahead. That’s why it is a good idea to make a pit stop at this juncture.

No matter the level at which it is undertaken, every analytics journey encounters significant challenges when it comes to gathering the relevant data, data quality, analyzing and visualizing the data, obtaining and sharing insights, and most significantly, deciding on how to convert the resulting insights into positive transformative actions, not to mention the change management challenges associated with fostering an analytics culture. You may end up using the latest technology to analyze the heck out of relevant data sets, but if it does not result in positive transformative outcomes for your business, or your corporate culture doesn’t embrace analytics, then your efforts may have been futile.

We are making a pit stop at this stage to refine what we’ve completed up to this point, gather a few necessary requirements, and brace ourselves for what lies ahead. A number of things we could do during this phase include:

  • Schedule time with the relevant people to have discussions on issues related to your data gaps. It might be a case of justifying access to a particular data set, reaching out to other departments or external parties to request access to data, or determining whether the additional data really adds value to your analytics objectives. If you’re unable to fill your data gaps, then you might also need to discuss your performance management and accountability criteria to enable you to refine your priority business questions.
  • Take the opportunity to refine the list of data sources along with the types of information they contain. Remember the new data you found that you didn’t know existed in your data sources and which prompted you to think about the value they could add to your analytics? Now might be a good time to evaluate that data to determine whether it would really add value to your existing analytics or perhaps enable you to perform new insightful analytics. Also make the effort to format your list such that it becomes an easy-to-use reference that can be shared with others.
  • Make a conscious effort to refresh or improve your understanding of the metrics and performance indicators for which you are accountable. Improving your knowledge of how the various data sources relate to each other would also be helpful.
  • Simplify your analytics objectives, especially if you are just getting started. Analytics do not need to be complicated to be effective.
  • At a strategic level, a review and refinement of policies related to data quality, access control, data security and privacy, external data sources and the general availability of analytics-ready data would be beneficial. Considering your corporate culture and the resources available, do you think your organization is ready to become more analytics-driven? Do you have adequate data to support your corporate-level metrics, benchmarks 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.

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

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