Why mid-sized companies are behind the #datascience curve: A thread—
Mid-sized companies are behind in terms of being data-driven when compared to startups and large entities. There are several reasons for this which puts them at a disadvantage.
Mid-sized companies are behind in terms of being data-driven when compared to startups and large entities. There are several reasons for this which puts them at a disadvantage.
1/ The average age of a mid-sized company is c years. X years older than the average startup and large entities such as Facebook or Apple. Overall, their workforce is older as well which translates into greater domain knowledge and also little technical capability.
2/ There is often a greater majority of non-technical works which places a heavy burden on the few technical workers often leading to burnout, limitations and ultimately churn.
3/ The tools and technologies include monolithic databases that take hours to run simple reports, Excel as a main tool for #datascience, #dataviz, #reporting with #bigdata! The amount of time and effort it takes to even collect data and thus share is highly inefficient.
4/ In addition to limited and outdated tools, the ignorance of higher ups can quickly downplay the opportunity of #datascience. They either are not informed enough to make a decision or believe every project should have a high ROI.
5/ For this reason, they rely on gut instincts versus data. Sometimes data tells a different story, but if it isn’t what someone wants to hear then some *magic* is applied to appease the execs.
6/ This can be highly misleading and often are vanity metrics. Meaning they do not provide actionable insights since they always seem to be in good standing. Instead the focus should be on identifying actionable metrics that clearly identity areas of weakness.
7/ In addition to developing actionable metrics, they should also be leading versus lagging. Lagging metrics show historical trends, which can be insightful, but don’t allow for any action to be taken in order to prevent loss.
8/ Leading metrics actually give a heads up of potential loss and thus signals that a change should be made. Reporting and optimizing the right metrics is critical for any company’s growth and success, gut instincts alone won’t do that!
9/ TLDR; #datascience is a great asset to any company. But it won’t save a sinking ship. Many mid-sized companies do not encourage continued education or prioritize data-driven decisions or advanced tools and skill sets.
#DataScientists are a bit of a luxury but can be instrumental in the success and direction of a company. That being said, hiring a swarm of DS in a last ditch effort will not yield great results.
