Making sense of big data in business
While collecting data is hugely important, it's how you handle it that really matters.
Big data is big news, no matter what industry you’re in.
Over the past decade, it has exploded on to the scene and can be used for everything from getting a better idea of meeting customer needs to analyzing how well space is being used in an office at real time to help maximize space and minimize energy consumption.
And while collecting this data is hugely important, it’s what you do with it that really matters.
For years, although data was being gathered, people weren’t really exploiting full value out of what they collected. In more recent times, with the explosion of data, companies are feeling pressured to invest in new technology out there to gather even more data.
“It was a reactive response instead of a proactive one” says Vijay Rajandram, JLL’s Global Chief Data Officer. “The key here is to be disciplined, understand and plan upfront on an approach to build up a strategy which applies the right analytics which are then converted into specific actions which delivers value to clients. That discipline is vital as there are many big data projects that fail without that focus. ”
Many companies also struggle with fully distinguishing between big data, traditional analytics, information and data management. “What is the difference between traditional structured data warehouses and unstructured data solutions?” says Rajandram. Both of these are needed in organizations today for different purposes and they interact closely. A lot can be leveraged from the existing traditional analytics too so it’s important to really understand what issues you are looking to address.
Introducing the data officer
The role of a data officer is a relatively new position in the business world. A 2015 report from PricewaterhouseCoopers found that of the 1,500 companies surveyed only 6 percent have a Chief Data Officer (CDO) with the largest number of these new positions concentrated in the communications, media and entertainment sector.
On a regional level, Europe emerged as the leader with 13 percent of companies employing a Chief Data Officer and dropping to 2 percent in the Middle East and North Africa. Although the exact role of a CDO varies between organizations, Gartner predicts that 90 percent of large companies will have one in place by the end of 2019, driven by the race to drive competitive advantage and improved efficiency through better use of information assets.
Within real estate, CDOs are the champions of a huge variety of information, which extends beyond managing data on clients and properties to broader macro-economic data used to inform business decisions and the digital footprints on social media. They also have responsibilities around how data is used and ensuring the right governance is in place.
Such data enables tenants and landlords to see exactly what is happening on the ground with and around their property through actual data. And combining actual data along with market data and predictive data builds up a more complete picture of an asset or portfolios past, present and potential future.
“The key is to capture the data in a form that is usable,” says Rajandram. “Sophisticated data capture needs sophisticated systems and many businesses still rely on more rudimentary methods such as excel spreadsheets to collate and share business intelligence.”
Putting data to work
Central storage, management and stewardship of data means companies can actually convert it into something usable, in a form that is easy to digest. Not just for staff but for also for their business clients.
“Business executives don’t want to have to talk to five or six different people each with their own views and sets of data – they want one person coming in talking about the joined up approach,” explains Rajandram.
“Today’s real estate data officers can overlay external factors on to company specific data using advanced data collation methods and predictive analytics to help them work out priorities.”
Take the final stages of selecting a building, for example. A building houses hundreds of separate pieces of data, making real estate investment decisions difficult. But by using sophisticated analytics tools, data scientists can individually analyse each data set to see how important these factors are in regards the property’s price and attractiveness. Insight into things investors may not have considered often arise from demographics of the area to the key points of interests around a building such as schools, social amenities and transport links.
As for the future, Rajandram sees the role of data scientists throughout the industry becoming even more embedded in business strategy.
He says: “Data analytics gets really exciting once you’ve built some solid foundations but organizations often try to run before they can walk and that’s where things go wrong. There’s a lot of hard work to do before you can get into the good stuff. The information has to be reliable, correct and most of all, relevant to the target audience.”