Big Data, the big buzz at the moment, holds the promise of adding more meaningful insight to your data. Incorporating Big Data into your analytics may enable your hospitality organization to optimize and enhance the guest experience, and thus become more competitive and profitable. Don’t just ignore Big Data, use it.
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Big Data,the big buzz at the moment, holds the promise of adding more meaningful insight to your data. Incorporating Big Data into your analytics may enable your hospitality organization to optimize and enhance the guest experience, and thus become more competitive and profitable. Don’t just ignore Big Data, use it.
Meet Courtney; a married, middle-aged financial advisor, who has two kids, shops at designer outlets, plays tennis, and travels quite frequently for both business and pleasure. She is a wine enthusiast and is active on social media. One day, Courtney arrives at your hotel and before she could make any special requests, the front desk staff politely informs her that a bottle of her favorite wine has been reserved for her at the hotel’s bar. There’s a very personal touch to Courtney’s room; her favorite flowers, fair-trade tea only – no coffee, coupons for nearby designer outlets, and the TV programmed with her favorite shows. Slightly underneath the memory foam pillow on the king-sized bed, is a hand-written note congratulating her on her son’s wedding the previous week. She slides into the arm chair, looks around the room, smiles, and says to herself, wow! Courtney then reaches for her smartphone to post #yourhotel #wow #greatexperience on social media.
Behind this personalized guest experience, Big Data analytics did most of the heavy lifting, creating Courtney’s unique profile to make this guest service happen real time. Using and merging data from the property management system (PMS), central reservation system (CRS), various point-of-sale systems (POS), product sensors, video feeds, Internet activity and social media interactions, you were able to create this unique profile, not only of Courtney, but also of all your high-value guests. You are now able to interact and communicate with them in a highly personalized manner. This is an example of what Big Data analytics can potentially help you achieve.
Have you considered the cost of not knowing? The cost of increased exposure to risks, the cost of missed opportunities and the cost of inefficient use of resources? Ignoring Big Data and the potential value of the deeper insights it can provide to your analytics can end up being much more costly than you think. So if you have been ignoring Big Data, it’s about time you took a look at it.
Escaping the hype around Big Data these days can be quite difficult. More often than not, the term is frequently mentioned at every conference. The hype may be justified because of the rapidly expanding volumes of data that we, as a society, generate and the promise that all this data can provide valuable and profitable insights.
Almost everything that you and your guests do as suppliers or consumers generates some form of data that can be digitally captured, stored and measured in one way or another. This digitally captured data contains a wealth of information, but more often than not, organizations are challenged as to how to extract value from the data. Big Data analytics can play a significant role in tackling this challenge.
Using Big Data does not make improved performance a sure bet, but the odds are more favorable. Rather, it’s the potential value of the intelligence gathered from Big Data, the decisions you make based on this intelligence, and the actions you take based on these decisions that will drive high performance. Integrating Big Data into your analytics can help you shift your organization toward becoming the class leader within your niche of the industry.
What is Big Data?
Big Data has become a ubiquitous term in recent years with the various technical definitions being constantly refined. The term encompasses the actual data as well as the challenges and complexities associated with capturing, processing, storing and analyzing the data to enhance timely and informed decisions. Most of the technical definitions you may encounter seek to address the characteristics of the actual data as well as the complexities mentioned above.
Suffice it to say that Big Data is pure information. Consider it to be your raw ingredients out of which a lot of valuable products can be made. The raw ingredients by themselves cannot transform your business; rather, using these ingredients in the right mix and proportions can be the differentiating factor. Much depends on the questions you want answers to and the challenges you face, as Big Data may mean different things to different organizations.
The sources of Big Data, both internal and external, keep expanding by the day. Due to technological advancements, we are now able to capture data from previously untapped sources and analyze that data alongside our traditional transactional data. For instance, video feeds from hotel security systems have existed for quite a while. Applying new tracking software to the video feeds, we can now create a heat map or path analysis of human traffic within the hotel, extract various data points and metrics from it, and analyze these metrics alongside revenue generated by various POS systems during that time frame. Then, there are newer sources of Big Data like social media which did not exist a few years ago, but can now also be harvested and analyzed with data from the PMS.
You would by now have deduced that if all the fine details of every transaction, every guest service call, guest social media interaction, product sensor data, images and video feeds are captured and stored, the resulting data sets can easily become overwhelming. Don’t let this scare you. You do not need to wrap your head around the whole Big Data concept to make good use of it. Big Data analytics tools provide you with on-demand access to only extract what you need, when you need it.
Why Bother?
Whether or not you use Big Data, most of the external organizations that you deal with (such as financial institutions, insurance companies, food suppliers and utility providers) use the Big Data that your organization generates to provide you with improved and more customized products and services. Even some of your competitors might use the Big Data your organization generates to gain a competitive advantage over you. Recent research by Aberdeen Group show that best-in-class companies are about 60 percent more likely than all others to incorporate unstructured data (i.e., Big Data) into their analytics.
Appreciating the potential of Big Data may prompt you to revisit existing data sources to look for new and improved answers to questions that have persistently bothered you. As you ask new questions you may also realize the need to identify and explore Big Data sources to find the relevant answers. There is also an increased probability of finding unexpected intelligence by just exploring the ocean of available data.
In recent years there has been a growing focus on Big Data, and data in general, across all industries including the public sector. As organizations and governments seek ways to eliminate redundancies, reduce risks and improve products/services, data has become the focal point for obtaining the valuable insights needed to address these challenges.
For those in the hospitality industry, keeping up with the constantly shifting guest preferences, expectations and buying behavior remain a huge challenge. As we ponder how to provide a better overall guest experience, drive improved efficiencies in the use of our resources, develop and maintain brand loyalty, and become more competitive and profitable, we should also think about how we can make better use of the data (and Big Data) readily available to help us make better fact-based decisions.
What About the Other Data?
As previously mentioned, Big Data by itself is not very useful. However, it becomes a very powerful leveraging factor when analyzed in tandem with your other data, for example, your traditional transactional data. To get Big Data right, you need to get the other data right. It is critical that you trust the integrity of your traditional sources of data and be comfortable analyzing it, before bringing in Big Data sources. Let’s take a moment to look at how we can make good use of these other data sources, and use analytics to extract value from them.
No matter the size of your hospitality business, you probably have more data than you know what to do with. The hospitality industry can consider itself fortunate because our guests willingly give us a huge amount of data via reservations, purchases, onsite activities and interactions with our staff. This data contains a wealth of information and is readily available for analysis. Although some will make very good use of this readily available data, there are still a significant number who rarely go beyond the standard static reports to explore all the available data to gain an improved insight into our guests and operational performance.
Your PMS, CRS and POS systems for example, contain valuable collections of data that when fully utilized can enrich your insights. I use the words fully utilize because some decision-makers are either unaware or underestimate the value of the additional reports and customization options available in these systems. A few ways to fully use these systems include:
- Taking a look at the other pre-defined reports available in these systems to see if they can provide you with additional value.
- To avoid over-burdening yourself with numerous reports, you can customize certain reports to be exception reports so that these reports are only delivered if the criteria for the trigger are met. In some cases, the trigger could also send alerts via SMS to mobile devices.
- You can use the self-service customization feature to select the data you want to see in a report, as well as visualizations and the desired level of summarization.
- Most of these systems allow you to export the raw data associated with reports to a desktop application like Excel or Access. This allows you to perform further analysis using these applications.
Then there are the external sources that you might be familiar with like Web analytics, benchmark reports from research companies and data from the various channel management services and rate shopping engines. It would be worthwhile to take a look at the other reports and analytics that these sources can provide. Moreover, upon request, you can also get access to the underlying raw data.
There are also numerous external sources of data that some in the hospitality industry are either unaware of or underappreciate. These free, subscription-based data sources can provide valuable information related to: economic trends and projections, development activities, state of infrastructure, demographics, geospatial and weather, amongst others. The U.S. government, as an example, provides a huge treasure trove of free data via Data.gov. For instance, you can combine demographic and economic census data from Data.gov with data from your marketing activities to see if your campaigns are targeting the right ZIP codes.
All these data sources mentioned above can be considered as traditional data sources, and are readily available to use with very little effort on your part and at little or no additional cost to you.
To get more meaningful insights across all your data sources, you might want to consider using an analytics tool which is external to all these data sources. By using an analytics tool, you can merge, aggregate and analyze data via a single interface or application, as well as distribute valuable insights throughout the entire organization. The end-product of these analyses could be a dashboard which provides your decision-makers with the relevant metrics and performance indicators that they need to monitor and act upon, and at the same time provides access to all the data you want made available to them. People consume information differently, thus the presentation of analytics should be tailored to suit how the targeted individuals or groups process information. The dashboards and reports should be created in such a way that they effectively engage the end user. For example, instead of providing a long list of guest information, you can use a single visualization to display multiple guest-related metrics and data sets. This makes the comprehension of the data much easier and motivates the end-users to quickly take action, if needed.
Today most analytics tools have self-service features which provide your decision-makers with on-demand access to the information they need, as well as the ability to create and customize reports to meet their specific needs with minimal IT involvement. The growing demand for real-time insights puts a lot of pressure on IT personnel. This pressure can be relieved if decision-makers use self-service tools for most ad hoc analytics requests. You may also use the analytics tool to restrict access to sensitive information, allow end users to share reports/dashboards, and provide customized analytics so that, for instance, the front desk operations daily dashboard is different from that for food and beverage.
Using all the data sources mentioned above and an appropriate analytics tool, you can use your traditional and readily available data sources to reveal insights related to:
- Trends in revenue and its geographic breakdown
- Identify high-value guests in specific areas such as spa, golf, bar, outlet shops, etc.
- Monitor reservation trends and outliers based on a specific day of the week
- Compare the profitability of loyalty members vs. non-loyalty members
- Benchmark and analyze departmental expenses
You may have realized by now that there are a lot of useful analytics that can be done with traditional data sources even before you venture into Big Data. If your organization’s adoption of analytics is not matured or you are new to analytics, I would suggest that you start by analyzing and making the most out of the low-hanging fruit, the data readily available to you from your PMS, POS systems, CRS and other internal or external data sources, and getting it right before heading out into the Big Data world.
How Does Big Data Fit in?
The reduction in time it takes to get answers to key business questions is one of the most significant ways in which Big Data can drive improved performance. This is due in part to the availability of greater computing processing power, enabling insight discovery and normal operations to happen almost simultaneously. There is no need to pause operations just because intelligence gathering based on massive data sets is needed.
Another impact of Big Data is the clarity it can bring to your traditional transactional data. It was previously mentioned that to harness the power of Big Data, it needs to be incorporated into your analytics of other data. Big Data comes in and makes insights obtained from your traditional transactional data clearer, and also reveals previously unnoticed intelligence.
In the hospitality realm, Big Data can come from your reservations, GPS data, blogs, guest service calls/emails, third-party booking engines, social media feeds, mobile devices, product sensors, video feeds and the list goes on. Now that we have our other data, let’s take a look at some of the things we can do with Big Data analytics.
Improve Guest Interactions and Experience
Big Data analytics can help you develop a deeper understanding about who your guests are, where they are located, what they want, with whom they interact, how and when they want to be contacted, and so much more.
One way to improve the overall guest experience is by creating unique profiles for your high-value guests and personalizing the services and offers provided to them. The guest mentioned earlier is a simple example of what can be achieved. The personalized services could include sending targeted offers close to a birthday or anniversary, or even making sure that the guest’s favorite beverage is always available anytime they visit. Some hospitality businesses use facial recognition software to pull up the profile of the high-value guest upon arrival. This profile is made available to the front desk staff in real time. The high-velocity processing capabilities of Big Data analytics make this possible.
Guests who are active on social media use that medium to send birthday wishes, announce wedding and vacation plans, receive recommendations on products and services, etc. Thus, infusing your targeted marketing communications into their active social media lifestyle is bound to have greater effect. Social media tools also allow you to track and monitor the effectiveness of a campaign by looking at things such as click rates, likes, forwards and re-tweets. Some hotels have taken this further by allowing reservations to be made via social media. This is just scratching the surface, as there are so many other things that you can do to improve your guest experience and interaction when you adopt Big Data analytics.
Big Data enables you to tap into previously under-utilized data sources to help build unique guest profiles. For example, the numerous guest service phone calls you have recorded and stored over the years contain a wealth of data. Today, using the right technology, all that unstructured call data can be analyzed to reveal relevant patterns and information. Similarly, the same unstructured call data can be processed and converted into structured data automatically as the calls occur. This insight can reveal relevant information about your guests and also help to better respond to future calls.
Build and Retain Brand Loyalty
You can use Big Data analytics to discover what influences and retains brand loyalty to enable you to develop targeted strategies to keep your guests coming back. Providing an exceptional guest experience on a consistent basis is one sure way to build loyalty to your brand. In addition, you can leverage social media outlets to interact with your guests in a personalized manner to proactively encourage them to become strong advocates for your brand. If online reviews are used by your future guests as guidance when making purchase decisions, wouldn’t a review or recommendation from a social media friend have more influence on their plans and decisions?
Brand loyalty programs are still very relevant, and you can use Big Data analytics to make these programs even better. Loyalty cards give you access to a great deal of information about your guests, and you can use this information to personalize their experiences.
Those guests who are active on social media are likely to post something adverse on social media before calling their relatives or friends to complain about an unpleasant experience. A quick response from your hospitality business shows that someone is listening, and can help build and retain brand loyalty. On the flipside, being unresponsive to one negative hashtag or post can rapidly tarnish loyalty to your brand.
Optimize Available Resources
Big Data can be used to optimize the resource allocation within your hospitality organization and also reduce inefficiencies in your supply chain. The human resources team can use Big Data analytics to determine which factors are the best predictors of on-the-job performance or incompetence, and this can influence hiring practices and HR management.
The ability to analyze very large volumes of data very quickly enables you to use Big Data analytics to evaluate suppliers based on your desired capabilities to determine the supplier’s overall value to your organization. You could also integrate your internal supply systems with that of your suppliers to enhance just-in-time deliveries.
Performing detailed menu analysis in real time to optimize the use of available resources is another way the Big Data fits in. You can get instant analytics from your POS system and incorporate this with data received from social media, blogs, reviews and guest comments to enable you to react quickly to the changing menu preferences of your guests.
Fraud, unfortunately, is an everyday unpleasant fact of business. As the volume and sophistication of fraudulent schemes escalate, you can harness the power of Big Data analytics to quickly sift through massive data volumes to uncover hidden patterns and suspicious transactions that might imply fraud.
Predictive Analytics
While traditional analytics are still very powerful, advanced analytics techniques such as predictive analytics enable you to get the most out of Big Data. Predictive analytics involves the use of machine learning to analyze data to predict guest behavior, guest buying patterns, future risk exposure and potential profits. With today’s tools, predictive analytics can quickly create sophisticated models based on a wider variety of scenarios across large data sets. The new data types and data fields that are available because of Big Data enrich the data sets used for predictive analytics.
Predictive analytics can help identify correlations that are not immediately apparent and also predict what triggers a sale or the utilization of an amenity. Not only can you predict the likelihood of a return visit, but you can also predict the potential profits associated with that return visit. However, it’s important to note that predictive analytics is no panacea. You still need humans who have an in-depth knowledge of your business to interpret the outcomes of the predictive models and take the required actions.
It is worth noting that not all business questions are better answered by Big Data analytics. You need to start by defining the business question facing you, establish what the desired outcome should be and then figure out what data and analytics you need to help answer that question. Once you have gone through this process you would realize that the best answer might not always come from Big Data.
Big Data Infrastructure and Analytics Tools
IT Infrastructure
A new generation of tools, technologies and architectures are required to handle the large volumes of data, the wide variety of data formats and the high velocity of data flows associated with Big Data. Furthermore, the demand for high-speed capture, discovery and analysis also reaffirms the need for the newer technological infrastructure. To enable an effective Big Data initiative, you must develop improved strategies for acquiring, processing, organizing and analyzing the data. Each of these strategies may require these newer generations of hardware and software.
Apache Hadoop (Hadoop), an open-source software framework for storage and large-scale processing of data sets on clusters of commodity hardware, is most often used to handle the initial acquisition and organization of the new data types.
Hadoop, a core building block for most Big Data architectures, contains two main components; Hadoop Distributed File System (HDFS) for data storage, and MapReduce for processing and transforming large scale data. Hadoop enables you to acquire and organize raw data and transform it so that it can be loaded into data warehouses to enable integrated analytics.
Big Data comes in various forms, thus processing the data to cleanse it of the currently irrelevant data and organizing what is left into a structured format is an important step to moving the data into the analytics environment where it can be analyzed alongside your traditional enterprise data.
High-performance Analytics
High-performance analytics is a must for Big Data. Not all analytics tools can handle this demand for rapid insights and the need to solve complex computations. When planning your Big Data analytics environment, there are a few things to look for to enhance high-performance analytics:
- In-database processing: This involves shifting the query processing, computations and analytics tasks closer to where the data resides (i.e., the database) to enable faster insights, better optimization of resources and improved data governance.
- In-memory analytics: You can also enable the use of in-memory databases to improve the speed of real-time analytics.
- Wide variety of visualizations: Because you will be exploring new forms on insight and data, you need to be able use the most appropriate visualization for each analytics scenario.
- Support for Hadoop: Being able to connect seamlessly to the Hadoop framework is quickly becoming essential for all modern analytics tools. More often than not, the analytics tool should connect to Hadoop just as it would to any other data source.
Shift in Corporate Culture
The adoption of analytics and Big Data might cause a shift in your corporate culture from one that is largely intuition based to one where most decisions are supported by insights obtained from data. This shift in culture has to flow throughout the entire organization to such an extent that decision-makers at every level have confidence in their decisions. Studies have shown that the top performers are more likely to lean more toward decisions supported by data, rather than intuition. Re-defining the culture is not a quick and easy thing to do. Naturally, there will be some resistance to change, but if you persevere and adopt best practices learned from those who have successfully implemented such changes, you are bound to be successful. Fundamental to fostering an analytic culture is the need to achieve the right mix of investments in people, processes and technology.
A successful analytic culture starts from the top with leaders who not only support the use of analytics but also strive to create an open and collaborative culture. In such a culture, executives demand that all decisions be supported by analytics and not just intuition. These leaders set an example by demonstrating a desire for confidence in decisions supported by analytics. Also, if the leaders within your organization have no confidence in the decisions they make, this lack of confidence will trickle down the entire organization. As Vince Lombardi rightly put it, “Confidence is contagious, so is lack of confidence.”
Challenges to Big Data Analytics
Big Data analytics has numerous benefits, as well as a number of significant challenges. These challenges could range from determining the right IT infrastructure to privacy issues.
Privacy, Ethical and Legal Issues
Significant, but often not discussed are the privacy, ethical and legal issues related to the use of Big Data. When is it appropriate to collect and use someone’s personal or public data? What is the right way to seek the person’s consent, if at all? What data can you collect and how will that data be stored, secured and used? These are some of the questions that you should keep in mind as you pursue Big Data. To date, the laws and regulations related to the use of Big Data in a hospitality setting are not clearly defined. My suggestion would be for you tread carefully and not push the boundaries. It would be advisable to establish and frequently revise strong guidelines for the collection and use of Big Data to avoid any unintended consequences such as potential lawsuits for privacy intrusion, damaging the reputation of your brand, guest boycotts and public embarrassments.
You also need to gain the trust of your guests when it comes to using their information. Solid trust is established only when you use the data in the manner that your guest expects it to be used. You can earn this trust by ensuring that all your guests are able to easily access and understand the policies related to the use of data gathered about them.
Infrastructure and Tools
Finding the right mix of IT infrastructure and analytics tools to satisfy your Big Data needs can be very daunting. You might find yourself having to deal with challenges such as multiple data silos, the inability to integrate data due to limitations in your existing systems, insufficient storage and processing capacity to handle complex demands, and the high cost of the newer generation of Big Data tools. You therefore have to decide at a strategic level, whether to rely solely on internal resources, use external vendors, or both. With that in mind, you should look for solutions which offer:
- Scalable infrastructure
- Strong and effective data governance
- Seamless integration with other systems
- High performance
Data Quality: Garbage In, Garbage Out
What could be more frustrating than investing a significant amount of time and resources into Big Data analytics only to realize that the data is corrupt? Making the data clean and usable is critical to any analytics initiative. The integrity of your data starts from the point of entry. This requires that your front-line personnel be well trained, and adequate control measures put in place to ensure that good data gets into the system. You may also need to implement measures to audit the data as it flows through your organization to ensure its continued accuracy. In scenarios where unusable data ends up in the data warehouse, you have to clean and further audit the data before it enters the analytics environment. This data cleansing process can be very costly in terms of time and resources.
Inadequate Staffing and Analytics Skills
Despite having the right infrastructure in place, you still need human analysts to look at the resulting information to derive insights and take action. One of the major obstacles to the adoption of analytics is the lack of personnel (including IT) with the required skills. You may have situations where your staff may be stuck wondering what data to gather, how to gather the data, which tools to use, how to turn data into insight, and how to turn insight into action. The main challenge here could be that most of them may not have an analytic mindset, or that the infusion of analytics into the corporate culture has not been effective. Addressing this challenge will involve additional relevant training for your staff and the fostering of an analytic culture within your organization. Training is essential, as recent studies have shown that the top performers in any industry are about twice more likely to train their management and front-line staff on analytic methods and tools. Individuals should be encouraged to improve their analytics skills by investing in themselves through formal education, executive education or online educational programs.
Academia can also help address this challenge by preparing the future hospitality workforce to appreciate the value of analytics and Big Data, and also know how and when to use them.
The Lack of Leadership Support
No analytics initiative will be successful if it lacks support from the top. The leadership of your organization needs to appreciate and buy into the analytics and Big Data concepts, as well as the positive transformative effect they can have. Sometimes this can be a tough sell as some leadership personnel might be unwilling to change the status quo or commit resources to the initiative.
Outsourcing Analytics and Big Data
Everything discussed so far might seem very overwhelming, especially to our colleagues of small or midsized operations. An option available to such organizations would be to outsource your analytics and Big Data projects. However, just remember to raise the issues mentioned in this article when having discussions with external partners. A significant number of smaller operations may currently be using external parties for various aspects of analytics. To be more effective, you should be able to easily merge these various analytics. This will give you a more holistic view and also provide better answers to the questions you may have. It will be worth your while to start the discussions with your analytics providers on how this integration can happen.
For larger organizations that lack the internal expertise and capacity needed to do their own meaningful analytics, outsourcing (both infrastructure and analysis) can be a more viable and cost-effective option. Outsourcing can offer numerous benefits, but at the same time expose you to potential risks. A few things to ponder when deciding to outsource are:
- Always retain full ownership. Your corporate identity, values and goals should not be compromised due to outsourcing.
- Start small. Smaller projects with less impact upon failure will at the same time give you the chance to evaluate the external party.
- The external party should easily integrate with your internal resources and be capable of adding significant value to what you are already doing or planning to do.
- Put adequate control measures in place to ensure data security and the protection of intellectual property.
Next Steps
If you do not have any form of analytics in place, then a first step will be to develop your basic analytics capabilities before delving into advanced analytics and Big Data. Start with some very basic analytics using the low-hanging fruit, such as transactional data, readily available to you.
Perhaps you currently do some analytics, but it is very basic. In this case, you should start thinking about ways to encourage the increased use of analytics within your organization. This might require a shift in your corporate culture as people have to gradually develop analytics-based mindsets when making decisions. You should also consider boosting your analytics to include merging data from various sources, as well as slicing and dicing the data behind your existing reports to gain new perspectives. Take into consideration that your staff might need some additional training to make them comfortable with analytics.
What if your analytics adoption is quite mature but you are yet to venture into the Big Data realm? Consider expanding your analytics models to incorporate Big Data to enable you to create new things such as improved unique guest profiles, real-time targeted interactions with guests, and predictive analytics. You can start with some of the simpler and more familiar Big Data sources such as social media data feeds and text analysis. You should also take a look at Data.gov to see what interesting and relevant data you might find there. For starters, the U.S. Census data is interesting. There are volumes of data sets available with various levels of intelligence baked in. It’s up to you to venture into the Big Data world to extract the value you need.
For those extensively using Big Data analytics already, we send our kudos to you. Perhaps the next time we meet at a seminar or conference, the rest of us can learn something from you. In the meantime, focus on asking new and tougher questions of your data, and if you have not already done so, start thinking about doing some predictive analytics based on Big Data.
The above-mentioned still applies even if you decide to outsource your analytics and Big Data capabilities. Of critical importance is the need to be on the same wavelength with your outsourcing partners.
The top-performing hospitality businesses (large and small) are reaping the benefits of an increased infusion of Big Data analytics throughout their entire organization. As the potential value of Big Data becomes more apparent and Big Data tools become more pervasive, more hospitality businesses are likely to adopt it.
So this begs the questions:
Will you completely ignore Big Data and risk being left behind by your competitors?
Will you allow your Big Data to accumulate dust, stay under-utilized, and be buried by it?
Or, will you use this growing avalanche of valuable insights to your strategic advantage?
The ball is in your court.
Samuel Ayisi is the head of analytics with Nyanspor Analytics and can be reached at samuel.ayisi@nyansapor.com. A note of thanks for initial contributions provided by John Burns, Jon Inge and Jeremy Rock.
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