How to Get More Value Out of Your Data Analysts

Organizations succeed with analytics only when good data and insightful models are put to regular and productive use by business people in their decisions and their work.

Actionable data is a buzzword that I have used and heard for many, many years.  However, in practice it is much harder to produce actionable insight for business users.  C-level executives are always trying to dissect old information which can be very useful, but only in cases where the answer will have some actionable insight.  If the answers to question have interesting insight, but don't provide any action, it creates a time consuming chase in how to make something out of the data.

Organizations have to educate themselves of what is capable of their data.  It's no use to ask questions that the data will not be able to turn into actionable insight.  Organizations should spend time on questions that can be solved with the current capabilities, in other words the low hanging fruit.  Once there is no longer any low hanging fruit, enhance the analytics with new tools and modeling capabilities to find the next round of low hanging fruit.

If you want to put analytics to work and build a more analytical organization, you need two cadres of employees:

• Analytics professionals to mine and prepare data, perform statistical operations, build models, and program the surrounding business applications.
• Analytical business people who are ready, able, and eager to use better information and analyses in their work, as well as to work with the professionals on analytics projects.

Very true.  An organization can hire all the analysts they can handle, but if the analysts have no business acumen and the business has no data acumen, there will always be a disconnect.  

Organizations need to have both the business and analysts working together to find the best answers.  Data Scientists can find very interesting items for them, however the business side may provide insight that shows the data scientists work is a known insight.  The business may be working very hard to solve a problem that the data scientist can solve, taking intuition out of play and using data in the place of trial and error.

In my history I have always liked business and data analysts reporting to the same group.  Many organizations don't like this structure because it can lead to a group "grading their own paper".  However, the tight integration of the teams produces results more efficiently.  Analysts are not wasting time chasing problems that don't exist and business people can bounce ideas off of analysts for quick insight before making decisions.

Manage Data with Organizational Structure

Article on who should manage data...​

most people management is actually done in the course of day-in, day-out work, by managers and employees. HR may very well define the semiannual performance review process, provide the needed forms, and make sure it is carried out. But performance assessment is completed by employees and managers.

This last point strikes at the heart of the federated model. Corporate HR sets policy; department HR may modify it in accordance with specific needs; and departments, managers, and employees carry out these policies. Most have a certain degree of latitude in how they do so.

 I am a big proponent of moving data management out of IT.  The HR model is exactly the model that works.  The business is closer to the data and very few IT department can handle the pace of the business when it comes to data management.  IT designs the network, builds the hardware and manages updates, while the business manages the ETL, data model and governance of the data.  

Best Time To Send Email

Interesting article on times to send emails.  The research included 21 million different emails and the findings are similar to what I have seen in my career.​

One of the most important conclusions is that sending newsletters during readers’ top engagement times of 8 a.m. – 10 a.m. and 3 p.m. – 4 p.m. can increase their average open rates and CTR by 6%.

However, optimizing email timing takes more than awareness of top engagement times. As our research points out, it’s a combination of many factors, including knowledge of time zone differences, your subscribers’ daily routines and the practices of other marketers. Find out more for yourself:

Remember to test on your own data.  I have seen different optimal open rate times between properties in the gaming industry, so test many different times and days and determine what the optimal time for your organization.

Emails have the best results within the 1st hour after delivery. This is when 23.63% of all emails are opened. But 24 hours after delivery, the average open rate is close to zero.

Almost 40% of all messages are sent between 6 a.m. and noon. This can result in inbox clutter, and significantly decrease results for these emails.

Messages sent in the early afternoon have a better chance of being noticed and consequently achieve better results: up to 10.61% open ratio and up to 2.38% CTR.

Subscribers’ top engagement times are 8 a.m. – 10 a.m. and 3 p.m.- 4 p.m. with up to 6.8% average open rates and CTR.

​I like the afternoon hours.  It has a high engagement rate and most emails are sent in the morning hours.  The more you can stand out, without being too late to be stale, the better success your email campaigns will have.  

Remember, once an email sits for 24 hours, there is hardly any chance you will get a conversion.  ​