Analytics spending is anticipated to top $200 billion by 2020, so organizations better make sure they are spending that money in the right ways. It is not enough to just toss money at new technology, but it is certainly not enough to just trust people to figure it out for themselves either. In an article for Harvard Business Review, KPMG’s Carl Carande, Paul Lipinski, and Traci Gusher discuss how to apply analytics strategically across the full organization.
The authors begin with an example of the benefits of good analytics in action. They discuss an unspecified major American sports league (but it is obviously basketball) that used data and analytics to reduce “instances of teams playing four games in five days by 26%” and “instances of teams playing five games in seven days by 19%,” among other statistics. This was achieved by building a clear data strategy that incorporated everyone from fans to players to TV networks. The authors continue to say this:
Companies can follow the league’s lead by first understanding that successful D&A [data and analytics]starts at the top. Make sure leadership teams are fully immersed in defining and setting expectations across the entire organization. Avoid allowing strategy setting and decision making to occur in organizational silos, which can produce shadow technologies, competing versions of the truth, and data analysis paralysis. Before starting any new data analysis initiative, ask: Is the goal to help improve business performance? Jumpstart process and cost efficiency? Drive strategy and accelerate change? Increase market share? Innovate more effectively? All of the above?
Your data science team should be actively involved in feeding insights into whatever areas the business deems most important. The other side of that equation is that leaders must be receptive to those insights; if leaders ignore the data or disregard its accuracy, then there is no point to doing analysis. Incidentally, only 51 percent of C-suite executives fully support their business’s analytics strategy according to KPMG research from 2016. This does not mean half of executives are dinguses though. It could also mean that the strategy is seriously flawed and/or not properly aligned with what the organization needs.
If analytics are to be ultimately successful, then people, process, and technology all need to fall into place roughly simultaneously: The business case for technology must be understood. The people with the skills to use the technology effectively must be in place. And processes should ensure that every relevant business function is reaping the benefits of the data science team’s insights. Yes, as usual, this all falls under the category of “easier said than done”—but hey, somebody had to say it.
You can view the original article here: https://hbr.org/2017/06/how-to-integrate-data-and-analytics-into-every-part-of-your-organization