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Getting The Best Data For Optimal Decision-Making

Few words have become bigger in the business world than data, and few companies are better known for “all things data” than search engine giant Google, which has produced two dramatic quotes about data-driven decision-making — from its CEO, “Every two days we now create as much information as we did from the dawn of civilization until 2003,” and from its chief economist, “Data are widely available; what is scarce is the ability to extract wisdom from them.”

From site selection to facility planning, industrial business expansion executives are quite aware of data’s value to their objectives, but how do they get the best data for optimal decision-making that produces the best results?

Data Must Be Trusted

Since businesses are so inundated with, and have access to, virtually unlimited data, executives must have data they can trust or that mountain of information is essentially useless. Data must be reliable by being rich enough that all the facts are there to be analyzed. A key problem occurs when executives or middle management have data and make decisions based upon a universe they inaccurately believe is a complete data set.

Not knowing that they often only have an incomplete data set and with outliers they do not recognize, the outcome tends to be skewed. Consequently, having a rich data set (a full data set), having data that can be trusted, and having it readily available in such a way that analytics can be conducted are all critical. Conversely, having too much data instead of too little, causes a potential “data home run” to figuratively get lost in the noise.

To the surprise of many who hold the opinion that simply having tons of data is actually the ultimate solution, too much data is proving to be the larger problem. Or, as a real estate expert says, “It really is the difference between data and information — being able to take all that data that’s being collected so efficiently and stored, then turning it into the kind of information that delivers the best decisions. Focus on the analytics you need for problem-solving and let the ‘noise’ fall away.” Otherwise, not finding the necessary pieces is as bad as not having them.

Actual Examples

As expansion/relocation executives know, certain dominant factors play into decision-making about facilities, with economics such as tax incentives being very important. Yet a market’s talent pool, which can sway site decisions, was pivotal in a major insurance company’s search about where to locate a new call center.

Several potential sites met all the criteria except for a couple of the markets not having a sufficiently deep talent pool for the call center’s expected growth. The unemployment rates were too low, and the cost of labor would be too high because there would be too much competition. So, the site selectors conducted analytics on a data layer that provided them information about locations with a ready, qualified labor pool but where the cost of labor was low, and they made a successful decision based on the results.

A major online florist’s business/strategic planning group decided to build a new location next to a shipping company’s major hub. With a business product that does not work well when encountering shipping delays, the florist’s standard approach revolves around ready access to shipping hubs “…because we can basically walk across the street and hand the box off, which quickly puts it on a plane with no long delays.” In addition to the obvious adjacent location appeal, florist executives backed up their decision with market analytics.

Looking at data from another perspective, a multinational communications and information technology company was creating a seamless network with another multinational conglomerate. At the outset, questions abounded at each company: “How many work in our networking group that we’ll spin off — and where are they located? How much of our space is dedicated to the network’s business?”

Because the lead company had the most robust data, including real estate, financial, and HR, within a couple of hours of the announcement the real estate group took decisive action. It was able to identify 30,000 people and approximately 10 million square meters of space that would be shifting from one company to the other. Although the conglomerate had similar data, it was scattered across too many disparate systems. That would have forced them to spend months and months looking up data, pulling it together, and doing analytics.

More About Sites And Expansions

When executives are doing analytics on potential locations, it’s not only about costs of building and maintaining a facility, but many other pieces. Besides the talent pool, is it near mass transit? What are all the critical data layers to be evaluated? With geographic information systems (GIS) available for data sets, this tool has become a significant factor in location analysis.

Using GIS, today’s facility scenarios tend to begin with determining which locations may be a good fit based on internal data from the company’s own operations, requirements, staff, and projections. Next is the challenge of merging that data into the wealth of data on the market about demographics and other key factors to make logical decisions, and that can be overlaid on geopolitical considerations. It’s all ideally based on a game plan of best covering a business’ operational parameters so that decision-makers have a richer data set than strictly real estate.

Heavy-industry assembly plant facility issues revolve around big data, with long lead-times a prominent feature. One such company, which gets its global plan from the business line, was planning millions of additional square feet for the manufacture of a pending new product. Data sets — e.g., for electric, steam, and pressurized air, as well as underground requirements — were critical well in advance of build out on an existing industrial site or the viability of a completely new plant.

Complicating the analysis was that the group working with the business side was not the group that would handle construction. So, different analytics had to be generated in order to keep the budgeting error gap between the different groups as low as possible. Typically, at large corporations such as this, the finance group is always involved, from the cost of materials and services to tax breaks, all feeding on data sets.

Looking Ahead

A major turning point for real estate professionals, which is still evolving depending on the industry, was their no longer being reactionary maintenance people, with spreadsheets included in that role as well. Instead it has become necessary for them to “have a seat at the decision-making table.” And to be a strategist requires having access to data well beyond how many work orders were closed.

A recurring theme in today’s facility relocation or expansion is the necessity of understanding how data adds value to the corporate mission and how corporate real estate organizations optimize their portfolios to ensure good decisions are being made. Data is more than raw facts and figures; it is an asset that embodies critical information that goes beyond operational efficiencies. Having that information upfront will not make the corporate decision, but it will certainly help determine the decision’s soundness, including the return on investment (ROI).

The story originally appeared on the Area Development website.


About The Sundance Company
Established in 1976, The Sundance Company has the experience to help you with your commercial real estate needs throughout the Boise Valley. If your requirements include property management, leasing, real estate development, project planning, construction or space planning then look to us. The Sundance Company has more than 1.5 million square feet of office and industrial space available in prime locations in the Boise metropolitan area. More information is available at www.sundanceco.com or 208.322.7300.

 

 

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