Operationalizing and Embedding Analytics: 3 Best Practices for Taking Action
What good is analyzing data if you don't take action on it?
- By Fern Halper
- December 15, 2015
I recently completed my Best Practices Report, Operationalizing and Embedding Analytics for Action. Embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights.
The idea behind the practice is to bring analytics to the point of decision making. In that way, the analytics becomes more actionable and thus more valuable. Of course, organizations go about embedding analytics differently. Some embed analytics into interactive dashboards or applications or in different devices. Sometimes analytics are embedded into a database. Other organizations embed analytics in the form of rules or models (or both) into business processes/systems.
It can be useful to think about a continuum for operationalizing and embedding analytics which goes from static (such as in a presentation or a static dashboard) to interactive to real-time to pervasive and automated. The goal at any point along this continuum is to make sure the analytics are actually put to work.
To that end, here are a few best practices to consider as you operationalize your analytics:
Best Practice #1: Have a Deployment Team in Place
It is one thing to develop the analytics you want to operationalize. However, these analytics need to be deployed to production if they are going to show up anywhere but in a presentation or on a storyboard.
Successful organizations often have a deployment team in place. The act of putting the deployment team in place can get organizations thinking about how the analytics gets deployed and help keep a project on track. A deployment team helps to surface the issues. For example, it often takes months for a predictive model to be deployed into production because very often the models (or other analytics) need to be re-written (although vendors do provide ways to export analytics so they don't have to be re-written). A deployment team can help deal with this issue earlier in the process. Additionally, models often aren't documented. This makes them hard to update. Again, a team could help to deal with this.
Best Practice #2: Govern and Manage the Data
If you are going to operationalize analytics and put them into production in your organization, the data analyzed must o be vetted. Forty percent of respondents in my survey stated that a big challenge for embedded analytics is that the organization didn't trust the data or the results. This is a data quality issue. Trust is also a people issue (see the next Best Practice) and one reason people can say that they don't want to use the analytics. It is important that organizations take control of their data as they start to operationalize analytics. This means making these projects part of the governance structure for data and analytics.
Best Practice #3: Think Hard About the People Issues
Often, it isn't the technology that is the hardest issue to overcome when operationalizing and embedding analytics. Rather, it can be cultural and people issues. In this survey, 20 percent of respondents cited cultural issues as a challenge that is hardest to overcome when deploying new technologies. Successful organizations seek executive sponsorship, develop a proof of concept to illustrate the value of the technology, and make a point of getting everyone on board. They communicate the value of the analytics to those who will be making use of the output. They provide training to make sure everyone is comfortable. Then they continue to highlight accomplishments and evangelize.
Another aspect of people issues is skills. Close to 40 percent of respondents cited building skills as a challenge to operationalizing and embedding analytics. Although many organizations make use of third parties to help them get their analytics projects off the ground, it makes sense to think about building skills in-house. That can mean outside hiring as well as training from within. Some organizations provide in-house training either through external organizations or via their own competency center.
For more on operationalizing and embedding analytics, see my best practices report on the topic. This topic will also be discussed at our Executive Summit in Las Vegas (February 1-2, 2016) entitled Making analytics pervasive in your organization.
Fern Halper, Ph.D., is well known in the analytics community, having published hundreds of articles, research reports, speeches, webinars, and more on data mining and information technology over the past 20 years. Halper is also co-author of several “Dummies” books on cloud computing, hybrid cloud, and big data. She is the director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and “big data” analytics approaches. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for Bell Labs. Her Ph.D. is from Texas A&M University. You can reach her at firstname.lastname@example.org, on Twitter @fhalper, and on LinkedIn at linkedin.com/in/fbhalper.