Increasing numbers of mid-size and larger companies have seen the worth of analytics and are trying to build in-house data science teams to capitalize on the value of their data assets. But, hiring effective data scientists is a challenge due to the competition for this rare and expensive talent. Further, beyond the task of finding and retaining data scientists and analysts, building a working in-house analytics team is fraught with organizational challenges such as securing buy-in from business unit leaders, IT, and executive decision-makers.
We Have Seen This Happen Too Many Times
As an example, the CFO of a Fortune 500 Corporation recently wanted to build an in-house analytics group. He approached Elder Research to develop:
- An effective analytics strategy for executives to align around
- A roadmap for building an internal analytics team
- A process for data-driven decision-making
He approached Elder Research after executives at their (Top-3 US) bank, revealed that several earlier attempts to start their own analytics team had failed.
- First, they had tried to build an internal team by hiring data scientists. The data scientists were tough to get to join the company, expensive, and a challenge to retain once hired because they were heavily recruited by others, and needed challenging well-defined projects to keep them interested and engaged.
- Second, they had engaged a large consulting firm to do an analytics assessment. For several months the firm conducted interviews with executives and staff, and then delivered a report with recommendations on how to build their analytics team. The senior bank executive revealed that they had paid millions of dollars for this engagement and had received only a PowerPoint presentation with hundreds of convoluted slides that were useless.
- Finally, they partnered with a small, specialized data science consulting firm who provided the guidance, expertise, and support they needed to “jumpstart” their in-house analytics team.
The specialized firm offered more data science expertise for a fraction of the price of the larger, generalized consulting firm. With mentorship and guidance from data science consulting experts they quickly grew their in-house skills, which improved morale and retention within the team.
Experienced Coaching & Mentoring
Coaching and mentoring by data science experts quickly builds technical capabilities within your team. Use outside experts to obtain guidance on choosing the questions to ask, methods to try first, software to obtain, techniques to verify models with, etc. Learn from their technical expertise how to apply industry accepted data modeling practices to your specific projects.
There are many pitfalls in data science, as our Founder and Chairman Dr. John Elder outlines in his “Top 10 Data Mining Mistakes.” A few mistakes Dr. Elder highlights are:
- Relying on one technique
- Accepting leaks from the future
- Asking the wrong question
- Listening only to the data
In addition, there are many business mistakes that we have seen in more than two decades of data science consulting which our VP of Operations Jeff Deal describes in his “Top 10 Data Mining Business Mistakes”. For example:
- Failure to clearly define objectives
- Tackling too much too fast
- Failure to get the support of the data owners
By hiring data science experts, you can avoid these common mistakes rather than building an analytics team from scratch and learning these lessons the hard way. An experienced consultant will operationalize your models in a way that people can understand and take action on, and make sure your model results hold up once deployed.
How Can I Learn More?
To learn more about how to build your analytics team, download the free chapter 3 of our CEO Gerhard Pilcher and VP of Operation Jeff Deal’s book written to executives “Mining Your Own Business.”
If you’d like to talk more about how we could help with building your internal analytics team, give us a shout! We’d be glad to discuss how to grow your analytics opportunities and overcome business challenges.
Read the blog Building a High-Functioning Analytics Team
Read the blog Choosing the Right Analytics Problem