By Neer Rama, Force Solutions Product Manager at thryve
Data can be a messy business. Bad data leads to bad results, which only amplifies when you try to use machine learning or other forms of artificial intelligence. Data should be organized in a way that fits the business outcomes you are trying to scrutinize. Bad data not only has the opposite effect, but it can lead to you getting wrong answers while thinking they are correct.
Say you want to know whether you can meet a specific order within scope, or maybe you want to see where you can consolidate policies in a complex environment. Data analytics can provide those answers, and services such as Salesforce Einstein are very good at doing so for technical and non-technical people alike. Such Data-Analytics-as-a-Service tools are profound game-changers. Yet if the bad data problem isn’t addressed, it’s all a waste of time and money. As they say, rubbish in, rubbish out.
Enter the data scientist. This person is not intimidated by data. They focus on building those clean data sets and accurate models from which AI can analyze and provide answers. Alas, data scientists are also in very high demand, and thus expensive to employ at scale. So much so that Harvard Business Review called it the sexiest career of the 21-st century. You might be able to put one or even a few such scientists on your books, but can they meet the demand of your business?
No, chances are they can’t, because data is becoming more appreciated and used across modern organizations. From finance to HR to marketing to logistics, all departments can find significant advantages from data analytics – and they know it. Beyond that, data is not only being analyzed for historical perspectives, but has become a source for current and forward-looking decisions.
My headline claims that every person can now be a data scientist. To qualify that, I don’t mean that data science is suddenly a simple discipline that anyone can just pick up. But supply doesn’t meet demand between data science and those who get business value from it. There needs to be another approach, one that can complement data science and take care of its more labour-intensive demands. And, yes, we have that answer.
Einstein Discovery was built by Salesforce to meet the more draining demands of data science. I mention it by name, as it’s a solution that thryve provides. It looks at data and helps clean it towards the business questions it needs to answer.
By automating 40% of data science tasks, this service relieves human operators from many time-intensive tasks. It also adds features such as no-code designs, enabling people not trained in technical data skills to nonetheless build and tweak models and data sets. In effect, Einstein Discovery can turn any business practitioner into a data scientist. Perhaps not a fully qualified scientist who can claim the century’s sexiest job title, but certainly enough to put analytics – historical and forward-looking – in any business professional’s hands.
Automating data discovery through AI can take the pressure off data scientists and enable your non-technical staff at the same time. Then there is a greater advantage: no matter how good any group of data professionals are, the sheer volume of data points out there means they can’t discover all the pertinent insights that are available. But AI, such as Salesforce Einstein, can detect trends and patterns like nothing else out there, then translate these into natural language reports and stories that are easy to use.
I should also mention the advantages of doing this through a cloud platform. It means better and faster integration with different data sources, easier secure access across different devices, simple deployment and scaling to keep costs low, straightforward customization for users and departments, and applying sector-specific models for the best accuracy.
Data analytics offer a lot to companies, and AI has made great strides in realizing those opportunities. Now Einstein Discovery is using AI to help clean and prepare your data, removing the last great barrier to fully embracing analytics for a data-first business. Data scientists still have a lot to contribute, but now everyone else can also get into the data game with more confidence.