The value-adds of built-in analytics
Lately the technology of embedded analytics, providing for incorporation of data ingestion, analytical processing and visualization opportunities in the business apps, has started embracing the other emerging techniques in order to enhance the exactness and to broaden the areal of reporting along with making it simpler for software developers and those who are far cry from development to adopt these possibilities in their applications.
As provided by the Forrester’s Deep Learning: The Star Of An AI Revolution For Customer Insights Professionals report, which was published not long ago, the progress in deep learning have enhanced the accuracy of speed, textual and visual data ingestion, providing the platforms, that integrate these techs, with the possibilities to derive aim, themes, entities and connections. All this data is crucial for the enterprises that want to learn their clients’ requirements and to fulfill them fittingly.
However, before the enterprises start integrating progressive attributes into the in-built analytical products, the experts recommend ascertaining that the company is ready on the primary stage in the first place.
Usually, when the customers generate the applications, they deliver the range of requirements. After production, as soon as the solution is released, the clients are likely to present the extra list of requirements, that haven’t been stipulated in the first place. Therefore, the customers want to be able to improve the application after it is finally produced. Providing them with the capability to participate in application development and enhancement process will be a remarkable achievement for the entire software development industry.
The vendor’s development teams can theoretically declare that they can generate application one hundred percent but it is highly unlikely that it will really happen or at least it will be done within a reasonable period of time. However, if the developers produce about 60 percent of the application and the rest 40 percent are built by the citizen developers who, obviously, have better insight regarding client’s anticipations, the product will be delivered not only quicker but it will provide the customer with better results.
It is presumed that AI-driven forecasting solutions are a proper tool to engage more users and customers beyond major development ecosystem into the product lifecycle. However, this is not the only way to accomplish that.
Indeed, Artificial Intelligence is an effective instrument to make aware the people with the possibility to create extra fragments of the applications, especially when they are completely unfamiliar with this job. Nevertheless, other methods to achieve the same goals comprise streamlined experiences and customization tools to fine-tune the reports, information and forecasting analytics.
The experts say that due to the huge volume of computing efforts needed and knowledge shortage regarding Artificial Intelligence, more conventional approaches are required.
Certainly, for large-scale businesses which are capable of sourcing a contractor to launch extensive cloud-based Artificial Intelligence application, this is not a big deal. At the same time, there are lots of companies which can hardly afford that, and the question is how these companies can explore the advantages of the latest computing approaches.
Key techniques to distinguish analytic solutions
According to the State of Embedded Analytics report, released by Business Intelligence and analytics company Logi Analytics, there are several key areas the embedded analytics ventures have to concentrate on in order to stay afloat and supply relevant features to their clients. These segments include:
- Artificial Intelligence implementation;
- Forecasting analysis;
- Natural language processing;
- Business processes navigation;
- Database reverse entry.
In order to understand what has to be done in the first place, it is worth determining the major business challenges the customers will face and whether technologies are helpful when attempting to solve these issues. Indeed, all development teams are aspiring to adopt those features and cutting-edge technologies which deliver more innovative performance. However, when overlooking the entire situation, the question you’d probably ask yourself is whether this state-of-the-art application really fulfills the requirements established by the customer and whether those comprehensive tools used in application development are value-adding.
In experts’ opinion, the first thing where everything has to start in this case is predictive analytics. Using the vast volume of previously collected data and employment of Machine Learning algorithms to process this data will apparently allow detecting the business problems that the people will be able to solve immediately. There are also issues that the businesses will encounter no matter in which industry they work: equipment sometimes fail, business processes may be interrupted and so on. Solving such problems will improve the company’s key performance indicators and allow building long-term strategic plans to accomplish.