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Business Value of Built-In and Embedded Analytics

Businesses generate and receive a lot of data, which begs to be analyzed. The technology of embedded analytics embraces the latest developments in terms of data mining, processing, visualization, and storage to help the decision-making process. The very idea of the embedded analytics is to make the results as straightforward as possible so that any user or application can use it.

Forrester’s Deep Learning: The Star Of An AI Revolution For Customer Insights Professionals report, published in 2018, says:

The progress in deep learning has enhanced the accuracy of speed, textual and visual data ingestion, providing the platforms that integrate these techs with the possibilities to find patterns, connections, and themes. 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 making sure 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. As soon as the solution is released, the clients are likely to present the additional list of requirements that haven’t вbeen 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.

Embedded analytics versions

There are many ways you, as a user of such analytics, can get data and results:

  • Data visualization and dashboards, similar to what you get in Google Analytics
  • Interactive and static reports
  • Self-service analytics, when you can ask your own question regarding the data in front of you
  • Benchmarking
  • Mobile reporting
  • Visual workflows

5 best embedded analytics tools

Among the tools that are available (and not counting custom ones that you can develop for your business with the help of HUSPI developers), we think there are five that stand out the most:

  1. Sisense. It provides a single repository for your data, supports information sharing, and is very user-friendly.
  2. Tableau. It can be linked to multiple information sources, which is a convenient feature when you have a lot of data coming from all over the place. It also supports collaboration and offers numerous methods of investigating data
  3. SAP BusinessObjects. This instrument presents insights in a digestible form and has appealing visualizations. Besides, like Tableau, SAP can pull data from multiple sources.
  4. Hotjar. We use this tool to understand the behavior of the users on our website and enhance user experience. It also is convenient for visualizing your user flow. 
  5. Looker. It welcomes collaboration and features drag-and-drop functionality. Looker also comes with a LookML language, which can be used to create your own mini-apps.

Main criteria when choosing 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.

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