Every time a car enters a parking facility, it leaves a trail. A data trail that is. A gate goes up to let a car in, a payment is made, a gate goes up again to let it exit, time and date the car entered and exited, how much the driver paid, the method of payment, and so forth were measured.

When the parking serves a huge flow of traffic, as is the case for an airport or a hospital parking lot, this data trail becomes big. How big? As big as “Big Data”, the relatively new term that refers to the gathering and storing of large amounts of information for analysis.

Of the business leaders polled, 85% said they believed big data will dramatically change the way they do business. The statistics support their perception – data volumes are expected to double every 1.2 years. Another fact, businesses using data analytics are five times more likely to make faster decisionsi.

Big data can produce big returns for businesses. Nowadays, Big Data is a “big” deal, the “new oil” of the digital economy. But big data is a challenge, because of the amount and complexity of data being mined, the high speed and complexities of data flows which can be unpredictable, and the fact that data comes from multiple sources, which makes it difficult to link, match, and transform across systems.

Data, on its own, is worthless. Many organizations are hitting stumbling blocks trying to tame it: part of the problem is the fact that the majority of companies spend 80% of their time manually collecting data for analysis, which leads to inaccuracies. Poor data or ‘lack of understanding the data’ are cited as the primary reasons for over budget projects and could cost businesses 20-30% of their operating revenue. More shocking – poor data quality alone costs US businesses up to $600 billion annually. Not a number to be ignoredii.

Big data can bring big insights, but it also requires advanced analytics for businesses to stay on top of the game and convert their raw data into revenue.


Though Big Data and Business Intelligence have distinct similarities and overlaps, they have two completely different processes that occupy unique roles.

Big Data collectively refers to the act of generating, capturing and usually processing enormous amounts of data on a continuing basis.

Business Intelligence systems combine data gathering, data storage, and knowledge management with analytical tools to present complex internal and competitive information that point towards specific decisions for planners and decision makersiii.

In other words, BI assists in strategic and operational decision-making. Merely reporting the performance of a firm and its competitors, which is the strength of many existing software packages, is not enough in today’s competitive environment.

Having the correct BI system is fundamental to organizing and analyzing Big Data. polled dozens of BI experts and
IT executives. Here are some of their opinionsiV:

  1. It provides fast answers to critical business questions.
  2. It helps align business activities with corporate strategy.
  3. It can empower employees by providing them with the ability to make data-driven, informed decisions that impact the company’s bottom line.
  4. It reduces time spent on data entry and manipulation.
  5. It provides insights into customers, allowing companies to discover patterns within customer behaviours.
  6. It helps identify areas for cost cutting.


In the UK, the most mature parking market, business intelligence in parking has been deployed effectively for around 15-16 years. Typically, on-site experts in data analytics that can accurately customize the data sets specifically for parking are considered essential.

Experts in Parking BI can provide:

  • Accuracy – This is the foundation and is fundamental in all analysis.
  • Historical data load – Reviews and analysis of past results, easily loaded correctly into a business intelligence tool.
  • Automation / Speed – Data now needs to be up to date and available 24/7. To enable this, superfast and robust ETL (Extract, Transform and Load) processes from data sources, run anywhere between real-time to once a day.
  • Dynamic Reporting and Dashboarding – The ability to view data through multiple devices like interactive dashboards. Customized reports that cannot be reviewed on dashboards should be updated at the press of a button so no time is taken to make decisions.
  • Transactional data – The ability to drill down to a transactional level detail, if needed. As data is often loaded at this level, clients often need to track behaviours on a customer-by-customer basis, by groups and/or by staff members. This can help operators to ensure there is minimal shrinkage of revenue from any location.
  • Ease of use – You can only use the tool on a daily basis if it is easy to use. No more than one day of training should enable operators to review and perform ad-hoc analysis. The business intelligence systems are almost always integrated fully with current tools such as Microsoft Excel, which ensures a faster learning curve.
  • After sales care and support – A dedicated team of analysts and technicians on call to support the day-to-day requirements. Dedicated resources should also be included on-site, or from head office, yet many organizations still do not employ full/part time analysts.

There is a strong need for parking operators to correctly handle and optimize the data available, especially in certain key operations. For airports, parking is, in most cases, the first largest non-aeronautical revenue stream, and airports are fighting hard to capture customer information which will enable them to deliver a better customer service and better understand behavioural patterns. The challenge is often converting transient customers to book on-line without diluting their revenues. An effective business intelligence system is the foundation for this analysis. In the UK, the challenge is now to drive business through direct channels and reduce costly bookings through third party consolidators. This will ensure airports with assistance from their parking partners drive to extract every dollar out of non-aeronautical revenue.

Traditionally airports have relied on parking site management and back office assistants to provide parking information and basic statistics. In most cases, this mode of collecting data still exists, but since the business has transferred to on-line reservations, more and more airports and car park operators are hiring parking analysts and yield managers. This has driven a review of the systems in place, but also of the onsite parking equipment.

  • Key data sets – specific focus on establishing data sets of key importance and specific requirements for each client.
  • Communication with operations on the ground, a crucial element to ensure what is being reported and forecasted is what’s happening.
    The operations team and parking lot staff are the front line and first to greet customers. They are also first to capture feedback on product and price.
  • Local and dedicated on-going support – without this implementation, on-going success is not possible.
  • An advanced business intelligence tool is crucial to measure the impact of on-going price changes and product launches.
  • Good understanding of the parking equipment and reservation systems back end. No matter how advanced a pricing system, if there’s no communication between the various systems it becomes pointless.
  • The accurate import process of information to parking equipment for pre booking data is essential. Reconciliation of this data source, if not performed correctly through an automated process, can lead to large sums of lost revenue and transactions.


Business intelligence is the world of descriptive analytics. It provides a retrospective analysis reporting on what has happened and what is currently happening. BI is a rear view mirror look on a given business. Predictive analytics is forward-looking analysis:  providing future-looking insights on the business – predicting what is likely to happen (usually associated with a probability) and why it’s likely to happen V.

So while business intelligence uses technology to describe the past, predictive analytics uses the same technology to predict the future and influence it. Organizations can use historical performance data to extrapolate and make predictions about the future and take actions that would affect those results Vi.

Another way to think of it is as follows: BI applications rake in mountains of new customer, market, social listening, and real-time app, cloud, or product performance data. Predictive analytics is the next step, treating all that raw information, in order to leverage to gain tangible new insights, and stay ahead of the competition.


Predictive analytics enables operators of large lots, such as airports, to offer the right product, at the right price, at the right time. Operators have the options for semi-automated, or fully automated dynamic revenue management models, with systems that can generate and distribute pricing for over 50 products at one location, for up to two years in advance, for every length of stay. In most cases, specific dashboards and forward looking reports are reviewed on a daily basis, which highlight hi-low demand vs. forecasts that can trigger a price recommendation or other actions. Our experience reveals that businesses using dynamic pricing have reported annual revenue gains between 7% in a mature environment, to 20% in locations where these methods have not previously been used.


Business intelligence software can sort through the data of a city’s parking spaces and predict and display graphically the streets with a larger number of available spots. It can also map this information based on time, pinpointing the streets where and when drivers are most likely to need a spot.

Obviously, we are entering the era of “smart parking.” The rapid advancement of BI parking technologies can generate new efficiencies, better control revenues and expenses and improve customer service.

Solutions exist that would enable motorists, through a network of communicating sensors installed in the road, to see available parking spaces in real time as well as the rates. Information will be posted on variable message signs (VMS), mobile phones and GPS to inform and guide drivers and provide additional services (e.g. to locate a parked car).

However, whatever control equipment is used – to varying degrees they all store data about parkers, their habits and needs – it must be properly analyzed in order to improve customer service and optimize operations.


When it comes to larger parking lots, data sets specifically for these types of operations can work towards an operator’s needs and deliver immediate results. However, many off-the-shelf BI systems, or BI start-up companies claim to be able to drive additional revenue with no experience in specific countries or trading environments. Many attempts to simply run the data through algorithms that are used across a wide variety of large lots such as hotels, airports, hospitals, etc., are not tailored to the individual operator’s needs. Experts in his or her specific type of off-street parking are needed to assist customization of BI tools to ensure they work effectively for the operator and produce valuable results. At the same time, an integral part of the process depends on understanding customer behavior and trends.

The combination of the holistic dynamics of a specific parking lot with customer behavior can produce accurate forecasting, and on a daily basis. The benefits are many, allowing operators to better predict:

  • Customer entry dates and times by the minute
  • Exits dates and times by the minute
  • All length of customer stays
  • Occupancy by parking location
  • Payment method to be used and at which device
  • Expected parking lot product/location including product type to be used i.e. Valet, Self-Park, Monthly and coupon usage.
  • Entry/Exit usage
  • Booking dates and times by the minute for on-line customers, web site and channel.
  • Staff and monthly parker movements are also tracked and forecasted.
  • For the Valet business, the ability to predict high and low peak periods, retrieval times/durations of vehicle times by staff members, hours worked and forecasted, vehicle parked location by bay, price paid and discount received, type of vehicles forecasted to arrive by make and model.
  • This wealth of information provides unparalleled insights and real usable business intelligence and analytics for clients.


If applying the principles of BI to parking holds out the promise of better management, and of fiscal windfalls, the implications go far beyond the integration of cutting edge technology into a business practice. BI allows for smart parking, and smart parking, in turn, is a key element in an emerging concept: the smart city.

Technological advances will be used to encourage the fluidity of movement (in many cases, with an emphasis on providing advantages for electric cars such as charging stations) and payments (via Smartphones); apps will be developed with information on available spaces in real time, allowing for reservations and more.

The smart city will be more fluid and more environmentally friendly. But to become smart, cities must by necessity integrate parking in their planning. It is imperative they have in place a global and long-term vision. The development of smart parking solutions is an emerging industry. However, already, across the globe, large urban areas are currently testing these solutions, along with airports, public transit agencies, and other organizations that manage large parking lots such as universities and hospitals.

Future parking needs must be thought out and planned today. An organization using unparalleled analytics will be able to offer customer service with a high financial return, while taking advantage of new technologies to develop the best internal management processes in accordance with best industry practices. ν




iii. Negash, Solomon (2004) «Business Intelligence,» Communications of the Association for Information Systems: Vol. 13, Article 15. Available at: 

iv. 8 Ways Business Intelligence Software Improves the Bottom Line,





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