Many business are losing millions of dollar by simply not knowing who truly they are, their capabilities, talent's skill. In order to avoid mistakes you need to understand some key fundemantals.
This is the most important question to ask yourself and your business before investing your data journey and pouring money on something you didn’t take time to plan. Data science is a very vague term in terms of each business. If we go to 100 businesses, we’ll most likely get 75 different answers about what data science is all about. This due to lack of understanding of this topic among executives.
Before starting your journey, you truly need to see where you stand in terms of your corporate culture and your technological capabilities. Some think that giving employees a laptop makes them a true technology company. This approach brings the unfortunate employee unhappiness and issues with employee retention.
Let’s look from the culture point of view, does your company have any kind of standardization for data, or is it all over the place no one really knows where it comes from, who does it, who touches it, who does any revision, any tracking of revisions. Are people aware of the importance of the data, security policies around the data, your documentation, any data governance activities, does a proper documentation of each of the collected data points exist. How often this data changes and many more about your cultural perspective about the data. As you can see, the list goes on. It does not need to be perfect but there needs to be a STANDARDIZATION so people can use these standards to continuously improve the product and services.
Your technological infrastructure is as important as the first point. Data science is a field that requires data scientists to interact with many tools and most of these tools are open sourced tools. Is your organization ready for someone to use these tools or is he/she restricted? Is your data stored in a database that can be easily accessible for the employee, is this database properly architected so there will be no issues when they are also accessed by the analytics employees who want to do heavy queries against it. Are you ready to provide all the hardware needs for the analytics team?
Our question list is growing exponentially. Unfortunately, this is the reality, and all these questions and many more need to be answered before pulling the trigger. Without knowing who you are will lead you to an analytics grave instead of monetization of your data.
Without knowing who you are will easily lead you to analytics grave like most.
Understand the differences between each analytics role so you can align the right role with the vision and your capabilities. This will not only help you but also help the candidates understand your company. For simply put, if you don’t have the infrastructure ready in place, don’t need to predict the future events, can’t provide the hardware needs, leadership with no technical understanding then most likely hiring a data scientist candidate will result in waste of time for your business as well for the data scientist itself.
Not to forget if you also need something even more tailored than you also add the other developers into the picture. Unfortunately, if you go with the hype, you also speed up your journey to advanced analytics grave.
Let’s say you planned everything the right was you put the infrastructure in place you make your culture more aware of the data and your vision everything seems perfect but you put someone who does not have the technical knowledge in a charge of your advanced analytics journey just because he/she has 20 years of experience of non-technical management role. We compare this just like Achilles heels. This person knows nothing about coding, struggles of developers, has no clue of databases, etc. This is another common mistake organizations make.
