An introduction to a tough section of any science is always a tricky job. More so with advanced analytics. And new age buzz-words like business intelligence or big data can be difficult to explain to many employees. Figuring out how this data will eventually help you also becomes difficult with new and emerging technology.
Dave Sawdey, JLL’s Americas Director of Business Intelligence, dives into the importance of analytics and business intelligence when making real estate decisions.
On analyzing the resources needed – hardware, software, time, skills – attempts at analytics can be cumbersome. Here are some steps you can take to reduce failure risk and also increase chances of succeeding with analytics.
1) Forget ‘New Tools’ – Additional tools and software’s only add to expenses before anything has been achieved. As any analytics program expands, updating the tool-set available should follow. The lack of a clear plan or execution methodology would cause an analytics exercise to fail. So a new tool would not help much with inaccurate data or unavailable data, and your problems remain unsolved.
2) Start Small, Grow Fast – Starting a big project includes launching off without a unified vision or plan. Start a project that will be easy to focus on and has low costs. Choose an area in the enterprise you feel has a lack of insight. Ideal situation would be one where complete and accurate data are already available. Once the issue has been solved and a positive ROI is possible, present your plans to the management.
3) Shift The Organizational Thought Process – Always include your ROI calculations while presenting a successful analytics project. This would help the group understand analytics better and realize it being a source of revenue, rather than just an additional expense. Ideal outcomes include the leadership trusting you and going ahead with re investment into other projects. Dozens of tools keep appearing all over the market, but it is better to stick to the basics. Tools will be helpful in understanding and analyzing data, but they won’t solve all your challenges. These are only small steps to reduce the risk of analytics failure in your operations. Share your thoughts on how you would approach this scenario.
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