On the Value and Benefits of Spatial Data

Intuition Versus Data Analysis

A survey of 3,000 managing directors makes it clear just how important data and its analysis are for business decisions. They were asked whether they rely more on their intuition or on the analysis of data when it comes to important topics such as sales and marketing, production, strategic issues, business development, risk management, etc.

The participants were divided into top and low performers. The result is astonishing… On average, top performers use data analysis five times more often than low performers. Low performers rely on their intuition much more often.

“I can be wrong with intuition, data gives me security. It gets exciting when data gives me insights that I didn’t even have intuitively.” Thorsten Lange, Senior Manager WIGeoGIS

Image: A survey of 3,000 business leaders shows: Top performers use data analysis five times more often than low performers. (Source: LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21.)

However, just because a company has data available, it does not mean that it will automatically make better decisions. Raw data alone has no immediate value.

“Collecting data is all well and good, but only when it is used, combined and analyzed does it generate added value and thus benefit. The value of your data is determined by what you do with it.” Thorsten Lange, Senior Manager WIGeoGIS

For example, data analysis specifically helps to minimize risks in the company.

Those who find the best locations, for example, through data analysis with the WIGeoLocation software, not only avoid bad investments, but also ensure higher sales in the long term and can optimally serve their target group. Strictly speaking, the data used, such as purchasing power data, traffic data, sales figures, etc., only acquires a value through the spatial reference or the enrichment of the data with the spatial information, because only then can it serve as the basis for your decision making. As long as the data is stored independently in various databases and lists, it has a much lower value.

The Ideal Data Cycle

Ideally, data goes through a cycle over and over again:

• Data becomes information through correct use, processing and analysis
  
• Information generates knowledge, then insight
  
• Insights enable the right decisions
  
• Collecting more data allows ongoing verification of decisions and evolution of the environment, etc.

By combining the data with spatial data, also known as enriching, the learning curve can be accelerated even further in this cycle.

Example of determining regional market penetration: It is difficult to develop and implement regional sales and marketing strategies on the basis of total national market volumes. This applies above all to products or services that require explanation. By linking regional competition, target group and market data with internal company data, GIS software can be used to determine regional market volumes and market penetration. This new, small-scale information provides the necessary insights for operational decisions. Think globally, act locally!

Image: Adding value to data is done through a continuous cycle of analysis and refinement Data becomes information that is transformed into knowledge and action. This process in turn leads to new data that is used for further improvement.

Extensive use of data throughout its lifecycle results in more valuable data as it is enriched over time, consequently making it of greater value for the company. The enrichment of the data, and thus the continuous increase in value, can be reinforced by the use of external data, as these represent added value for the proposed cycle.

What is the value of data?

The value or price of the data is determined by considering four factors:

• Market price: Can I buy the data? If yes, what does it cost?
• Expenditure: What expenses do I have if I collect the data myself?
  
• Profit and added value: How does my company benefit from the data? Is the benefit measurable and communicable?
  
• Risk minimization: Can my company reduce risks through data collection and analysis (risk mitigation)?
  

An example: The opening of a new location such as a branch in the food retail trade would cost one million euros. By using the data analysis in the GIS software WIGeoWeb, I can avoid investing one million euros in a bad location. The investment in the data analysis software has already paid off, and it will also be useful for every further location decision. To determine the value of the data or to create a cost-benefit calculation, I can also include how much my data would cost on the open market.

What preserves or detracts from the value of my data?

Businesses need to decide how much data is worth to them and how much they want to invest in it. Deductions must also be taken into account when evaluating the data.

• Quality: The more complete and accurate the data, the more valuable it is. Inaccurate and incomplete databases can give a false picture of the current situation and thus lead to poorer decisions. The principle “Shit in, shit out” applies here: If you put poor quality in, you cannot expect to get high-quality results.
  
• Timeliness/mortality: Data also has a mortality date. Therefore, there is a deduction for outdated data, namely for each day that the data is not updated. What’s the use of knowing how many people passed my location a year ago if I have no idea how many there were yesterday?
• Intensity of use: Data wants to be used. Important developments often only become apparent through observation over a longer period of time. And the market environment is also constantly changing, which is why the data should be analyzed and tracked again and again. A data query that only takes place every two years cannot be as valuable as a monthly report. Low or sporadic usage of data results in lower value. Conversely, data becomes more valuable for a company the more intensively it is used.