In our latest article we introduced you to strategic data acquisition and emphasizing the value of it. If you haven’t read the article yet, you will find it here. Now it’s time to stress the importance of the next steps of implementing data related aspects to your organisation. Most importantly, after you gather data – ANALYSE it. Acquiring data is completely and utterly useless if nothing comes after (obviously). Shockingly, that’s precisely what most people do.
A study conducted by Digital Universe published in The Guardian showed that in 2012 only 0.5% of all data was being put through analysis at that time. And according to Analytics Week, in 2017, this percentage still hasn’t changed.
These statistics provide us with quite a perspective. The potential here is incredible. Imagine what breakthroughs are ahead if all the gathered data is put through analysis in real-time. This way, the solution will come the same moment a problem appears. To achieve that, we must know how to manage big data successfully.
To introduce strategic data acquisition into your business, it is necessary to know how to act on vast volumes of data. It’s also crucial to understand the reasoning in starting a product even with a small amount of data. This brief text is an introduction to basic knowledge everyone should possess about the means and importance of storing, analysing, and managing data.
Data science is a study that focuses on the means of data processing.
Data scientists search for value and information in data and generate a hypothesis about the final results. Some data can disrupt the process of analysing data, and it’s necessary to spot this and make an adjustment (skip the redundant piece of data) to stop this disruption. That task falls behind data scientists’ responsibilities. It’s a job that requires highly predictive abilities and excellent analytical skills.
The virtuous circle of AI
The virtuous circle of AI presents a cycle of product development with the use of artificial intelligence. It aims to show the simplicity behind the logistics of positive feedback of data gathering and the product’s continuous improvement.
Andrew Ng, a computer scientist and a professor of computer science at Stanford University, explained this cycle and managed to provide us with a simple explanation. A launched product, created even with a limited amount of data, eventually attracts some users. When users interact with the product, it generates more data. This newly gathered data may be used (processed by machine learning) to improve the product, and then this entire process repeats.
By that means, the product continually improves as the data is analysed in real-time, and improvements based on the gathered data are constantly implemented. An originally released product must be only “good enough” and then users, with time, will provide you with vast data assets. Over time, the process begins to drive itself. The more users we have, the more data we acquire. And the more data we have, the more effectively we improve our product or service.
Unified Data Warehouses
They are needed when organisations gather vast volumes of diversified data from multiple sources. Unified data warehouses (DWs) are required when dealing with big data, amounts of data that cannot be processed by traditional means. DWs are also known as unified databases and are places where all sets of data of an organisation are stored and are widely accessible (within the company). That’s their biggest advantage – they enable locating all data in one place. And that results in a more efficient decision-making process as there is no need for analysing data separately.
Unified data warehouses allow to store large amounts of data from diverse sources
Their other significant advantage is the increased reliability of made choices, as they provide information that is always up-to-date. They also improve any decision-making by other means. Any outcome extracted from data is of higher quality as DWs allow omitting redundant data. Furthermore, DWs enable the drawing data from various, not connected sources.
Overall, they store enormous amounts of data with various parameters drawn from completely unrelated sources. DWs help combine, what seems like, incomprehensible data to build value. They create the perfect environment for data storage management and data analysis.
The volume of data vs. performance
This graph shows how model performance changes with an increasing amount of data. The trend shows that with the increase in data size, the performance increases when using more complex machine learning (ML) models and neural networks (NN). However, with smaller volumes of data, it presents a slightly different correlation. Simple ML algorithms and small NNs can process less significant amounts of data more efficiently than bigger NNs.
Therefore, it is unnecessary to use medium or large NNs for projects that have little structured data where traditional ML algorithms will do a much better job. It’s vital to make conscious decisions when choosing a model to analyse various sets of data. It doesn’t only influence the reliability of the results but also the taken time to process it and money invested in that particular method. Simpler means of analysis are significantly cheaper and less time-consuming.
In conclusion, after getting familiar with strategic data acquisition and implementing that into a company, the next step would be to manage that gathered data correctly. Unified data warehouses are the perfect place to store all types of data. Since they allow access to it all across the company, they are ideal for further data mining and analysis. After gathering data (and during that process), data scientists decide on value and process the information to acquire maximum functionality. Data science usually applies to big data, and with such enormous amounts of information, it’s absolutely inevitable.
Despite the general importance of big data, it’s possible to start with a modest amount of data and launch a product generated off it. That’s because while its put to use, users will create more data, and that will fuel generation of higher volumes of data and thus constant improvement of the product. Having said that, while deciding on the model performance that will be used, it’s vital to know what are your resources and desired outcome, to acquire maximum efficiency and shortest time while picking the possibly most economical option.