More and more companies are embarking on Big Data projects. As a result, Data Science now plays a significant role for many of them. Do you know that 62% of American companies use Data Science at least once a week? And that in the United States, 89% of companies have collaborated with Data Scientist? A study conducted in 2019 by Dresner Advisory Services confirmed the trend towards adopting Big Data. According to it, 55% of companies have already exploited Big Data to date, compared to only 17% in 2015.
This discipline is one of the priorities of companies. It represents an increasingly important lever in terms of economic performance. Whether you are new to the field or have a knack for advanced Big Data projects, here are three criteria that always guarantee the success of your Data Science initiatives.
Relevant and Quality Data
It’s not a scoop, but data is the backbone of any Data Science project. Recovering data is not a problem. E-commerce, media, telecommunication. Whatever your sector of activity, you probably have a vast amount available: Digital Analytics data, transactional data, CRM data, offline data, social media data, etc.
But don’t be fooled by the “Big” of “Big Data”: having access to a whole stock of data is not enough. For your Data Science project to bear fruit, you need quality data, adapted to the problem you are trying to solve. When we talk about Big Data, people often imagine, wrongly, that they will need a lot of data. But it is not always a question of volume. It is, in fact, preferable to have relevant data, which delivers information on the users or the transactions concerned, rather than a whole mass of data.
Your analytical data is generally an excellent source of relevant and quality information (especially in the case of collaboration with an Analytics editor, which guarantees unsampled and utterly reliable data). Their specificities: they are complete, consistent, and incredibly detailed on each user’s interactions with the brand, whatever the platform and the device used. Tools like Data Flow facilitate the extraction of these events by the millions, as well as the cleaning and enrichment of data.
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Technology that is Both Powerful and Accessible
You have the correct data. Now you need to activate a second lever: the technologies and artificial intelligence that will do the heavy lifting for you. But before that, a little flashback with a straightforward question: why entrust your data sets to a computer? We have created machines, and now these technologies are more trustable and beneficial than the traditional approaches, says S. Foulard, who quotes the stock market and weather reports. Two typical cases, according to him, where “historical data are used to identify trends and models to predict the future.” The technologies like deep learning are not just in movies, and we are seeing them in our routine now. The perfect illustration is the high precision of facial recognition, unlocking a phone, or quickly identifying someone on a video surveillance system.
Another aspect on which the machines surpass us is the optimization of complex problems. Today, computers are used continuously for optimization purposes, for example, road or logistics. Whenever you have to reach a specific goal most efficiently or economically, artificial intelligence always wins. Computers are, therefore, competent in pattern recognition and optimization (and so much the better.) But why are these technologies so crucial in an Analytics or Data Science project?
Thanks to them, we can go beyond the:
- Descriptive Analysis (the diagnosis of what has already happened)
- To access the Predictive Analysis (what will happen next?)
- Or even the Prescriptive Analysis (how to influence future events?)
Naturally, the more effective the levers detected by the machine to improve performance, the more value can be gained from the data. Machine Learning and the associated algorithms have become much more accessible over the past fifteen years, which has had the effect of simplifying the implementation of Data Science projects. AppStudio is an app development agency having complete command over data science. We have transformed different industries and always guarantee results with our unique development team and the world’s best data scientist.
A Data Scientist with the Right Profile
A successful Data Science project is not just about quality data and robust technologies. The third main factor which should never be ignored is the human touch. Here, we are discussing the planning, intelligence, and strategy of companies like us that help enterprises grow and prosper. It is a fact that most companies find it extremely hard to get the right talent in the data science niche. The demand had skyrocketed when the profession was declared “the sexiest job in the 21st century! But this is not just a fad, and the task is not always distinct.
To make matters worse, Data Scientists sometimes have quite different specializations, which means that not all candidates are equal in the face of a Data Science project. “There are several profiles of Data Scientists,” explains S. Foulard. The difficulty for companies is, therefore, to determine their need, then to know which Data Scientist profile may be suitable. Know that there are Marketplace experts in this area, as AppStudio, to find the competent specialist according to needs. We have our regional offices in different cities in Canada. We are regarded as the best company on the radar of data science Toronto.
It doesn’t matter if you are planning for a new data science project or upgrade it. We have got the right strategy for your company that will help boost perfect results. Talk to us! We can help you effectively reprogram your business.