In a world where everything from wearable technology to smart home devices is connected to the Internet, data is everywhere, and there has never been as much information about consumer habits and preferences as today. But what can this data do for and how can the automation of their analysis help your business?
Why analyze data?
Essentially, automated analytics consist of automatic decision-making based on data – not just any data: Big Data. The latter is not limited to quantity. These are data that come from multiple sources, exist in many forms and require different types of analysis to extract relevant information. Increasingly affordable data collection and storage offers substantial benefits to businesses that can leverage them to understand the state of their business in real-time, from analyzing the effectiveness of marketing campaigns to the evaluation of growth sectors.
Barriers to effective data analysis
To analyze this data explosion, companies need robust, reliable and automatable tools. Predictive analytics tools can manage Big Data, measure current trends and see where they are going, providing valuable information to business owners, as long as they can analyze this data quickly and efficiently to benefit from it. Tools like Hadoop and Azkaban are effective at creating models and running simulations, but often require strong manual intervention.
Sometimes, however, Big Data contains too much information to be analyzed manually. Data lakes contain vast amounts of information from many sources, but useful models are concealed by the amount of data and the number of potential analyzes to perform. On the other hand, the data may also be too dispersed and isolated in silos, which occurs when different departments of an enterprise have their own data sources that are not shared and constitute another obstacle to an analysis.
Automated solutions can improve your Business Intelligence
When it comes to processing so much data across a company, manual scanning is slow and prone to errors. Automated systems, on the other hand, allow the data analysis process to run smoothly, giving you direct access to data and obtaining usable information. Automating data analysis, for example, can break old barriers by automatically compiling data from different sources and converting them into the same format, preventing the formation of new silos and making the data more newer accessible to all who need it.
Automation of analytics is essential to identify the many sources of data that modern businesses use today, ensuring that data experts do not waste time working on erroneous, obsolete or incomplete data. With a more streamlined data analysis process, you can see significant opportunities, introducing agility into big data analytics, and ultimately improving business intelligence and competitive advantage.