The proliferation of big data and the Internet of Things (IoT)
The digital transformation of a business has created a huge amount of data about customers, competitors, market trends and other key factors affecting financial success. Since this data comes from many sources and can be unstructured, it is not easy to work with. Managing and using them can be challenging or even impossible for internal teams such as conventional business analysts and IT teams working with legacy systems.
A new level of availability of solutions based on artificial intelligence (AI)
Artificial intelligence and machine learning, which were previously only science fiction topics, are now commonplace. This happened very well in time given the growing importance of big data issues. As the volume of data, their variety and dynamics grow exponentially, the ability to identify patterns in them and make predictions has long gone beyond the capabilities of the human mind and traditional statistical methods. AI and machine learning are needed today for reliable data classification, analysis and forecasting.
Huge growth in computing power
Modern methods of data analysis and processing would not have been possible without the recent significant increase in computer performance. One of the most important advances was the realization that computer processors designed for rendering images in games are also well suited for machine learning and artificial intelligence.These chips are able to efficiently process extremely complex statistical and mathematical algorithms and quickly find solutions to even the most complex problems, making them ideal for use in data analysis.
New ways of storing data, including cloud technologies
Analyzing and processing data also requires the ability to efficiently store all types of data at a reasonable cost. Businesses can now store petabytes (millions of gigabytes) of data, whether internal or external, structured or unstructured, using a hybrid on-premises and cloud storage system.
Integration of systems
Analyzing data unites all parts of an organization, so tight and dynamic systems integration is essential. Technologies and systems designed to move data in real time must be seamlessly integrated with automated modeling capabilities that use machine learning algorithms to predict outcomes. Then, to consolidate the advantage, the results must be delivered to client-centric applications with minimal latency.
A utility company can optimize the smart grid to minimize energy consumption according to real-time usage data and cost structure.
A retailer can use analytics and data manipulation on point of sale information to predict future purchases and better match product mix.
Automakers are actively using data analysis and processing to collect real-time vehicle traffic information and develop autonomous systems through machine learning.
Industrial plants use analysis and data processing to minimize waste and increase equipment uptime.It is data analysis and processing, as well as artificial intelligence, that have become the foundation for advances in text analysis, image recognition and natural language processing that are driving innovation in a wide variety of industries.
Most companies today are overloaded with data and are probably not fully exploiting their potential. This is where data analysis can help to transform information into meaningful strategic insights and real competitive advantage.
By using data analysis, your organization can make decisions and act with confidence because you rely on facts and scientific method rather than guesswork and intuition.
Analyzing and processing data can dramatically increase productivity in almost any area of your business through the following capabilities:
The analysis and processing of data is becoming more and more automated, and this process will continue. For example, today a technician can set up an automatic grid search of all possible combinations of thousands of data parameters to find the best solution to a specific problem in real time.
In the past, statisticians had to manually design and tune predictive models, drawing on their experience but being creative at the same time. But today, as the volume of data and the complexity of business problems has increased, the task has become so mathematically complex that it requires resorting to artificial intelligence, machine learning and automation to solve it. As big data gets bigger, this trend will only intensify.