How to increase the impact of data analysis without increasing budgets

Recently, data analysts have often called the date Satanists, and in principle, there is something about the truth. From a business point of view, the work with big data is perceived as something complex, unclear, fashionable and probably necessary. Companies are building data science staff, fighting for graduates of prestigious universities, trying to buy a «star» and drag her to their staff. From the perspective of the budget, the first «data satanist» is a person with non-anal tasks, very interesting work and costs around $5000 per month. And most importantly - with absolutely no guarantee of the result of his work. The average specialist will read and envy.
And now let’s try to calculate. In order for the data analyst to have the opportunity to work, you spent at least $40000 on the construction of EDWH, as much on building an image of a progressive employer and preparing a beautiful infrastructure of the workplace, and there is no guarantee of the result.
In fact, the main part of the labor cost in solving almost any problem of data analysis and construction of small model - to pre-work the data, to find what is connected with the desired target metric and to choose such configuration of the model, which will evaluate/forecast the target metric with a suitable result.
At the same time, in the work of each analyst there are repeated patterns, and if you embed this user experience in an automated system, you will get several new cases at once:

1. All preliminary analysis (exploratory) can be made continuous and automated. And this access of expensive analysts right up to the map of data significance and hidden interdependencies, which means reducing labor costs and accelerating the result;
2. The entire model construction cycle can be performed by any business user without coding knowledge. With automated analytics, building a model is like calculating margins on a calculator. In addition, the implementation of the tool will allow continuously and without expectations and explanations to external specialists prototype the models, and attract analysts already having a clear business case.

Thus, introducing an automated date analytics system, we immediately solve several tasks

1. We build a continuous process of data analytics;
2. Reduce the burden on expensive and scarce specialists on the market;
3. Increase speed of ML-solutions development;
4. Increase the transparency of the modelling process;
5. Improve availability of data analytics for business units;
6. And ultimately, we stop investing in data and start earning from it, thereby reducing risks and increasing business resilience.

And now a nice bonus - the ML automation system does not need to come up and develop from scratch, we have for you a ready solution, the support of which is comparable with the costs per ONE internal specialist.