DATABERG
What’s deep analytics?
It’s application of complex data processing techniques to yield information from large and typically multi-source data sets that might have structured data, unstructured, and semi-structured data. Sometimes, it involves precisely targeted complex queries on data sets that may be measured in petabytes and exabytes, often with the requirements for real-time or near-real-time responses.
Considering the fact that real-time analysis of these huge data sets requires distribution of the workload over hundreds of computers, deep analytics (DA) is often associated with cloud computing. DA is exactly what brings higher value to your data by using tools and techniques to find data, mine data, collect it, organize it, and deliver it to the end users.
Strong deep analytic practices not only ensure that data is found, but also guarantee that the data has strong recency and frequency, complying with the main objective of DA: to structure the data, so that a business could obtain the most valuable insights.
So, what’s so special about DA?
Well, it’s all about connecting disparate systems and turning dark data into regular, structured data. By applying structure to often unstructured data, one gets a chance to obtain actionable insights from raw, dark data. Moreover, DA brings the power of machine learning to all data that results in engaging the business into data-science agenda and uncovering hidden potential in the underused information.
The customers, in their turn, find the information spread across data silos and start understanding trends and patterns of that information. DA analyzes unstructured information using natural language processing capabilities and allows to discover hidden patterns through cognitive mining.
What are the true benefits of DA for a business?
Customer growth, revenue growth, and expense savings - all these and other factors directly depend on the dark data and company being leveraged. DA is an applied science. With a solid DA infrastructure, teams might come to expect quarterly and even monthly improvements.
As the recent studies demonstrate, a company that invests in mining dark data can expect a 20% win once or twice a year. This is not an endless process. There are limits to how many 20% lifts you can find, but most companies have plenty of dark data and deep analytic runway.
Among the main benefits DA is offering, one can name the following ones:
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Detailed individual reports based on raw data;
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Browser-based pivot tables and visualizations;
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Seamless integration with Microsoft Excel;
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Unparalleled variations of measures, groupings and combinations;
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Automated email dispatch of reports;
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Cross-client, target group-specific analyses;
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Sales and ROI reports, etc.
What are the DA’s bonuses for marketing and sales?
Apart from increasing return on investment, DA turns the large amounts of customer and digital advertising data into predictions that increase sales, optimize advertising ROI, increase customer satisfaction, and make marketing programs more effective. DA uses Deep Learning techniques to accurately predict complex human behaviour on an individual level. Thus, one can deploy a model into production, regardless of whether you need real-time predictions or batch deployments.
It’s obvious that human behaviour is complex and constantly changing. In order to keep marketing strategy up-to-date, the company’s predictive models should be in line with the recent tendencies. DA enables the sales specialists to conduct a detailed sentiment analysis on a product, as well as to analyze data and discover valuable insights and patterns within this data.
Traditionally, companies were only able to analyse a limited amount of their internally-generated data in a timely fashion. Now, however, they potentially have access to enough capacity to draw it in from external sources, along with their own historical records that could go back decades and to submit it all to comparison and pattern matching.
Should the companies be willing to reap the benefits from DA opportunities, they will need the help of data scientists to tailor the data, rules and algorithms to their precise needs. They will also require access to substantial amounts of data and to the hardware on which this technology rests.
