Business Analytics

Artificial Intelligence in Business Analytics: Challenges for Investors and Developers

The current state of the market of AI for Business Analytics is showing upward trends and is supported by the leaders of most companies, who are ready to increase their investments in this area. A positive attitude towards the use of AI is associated with the benefits expected: optimization of business processes, increased efficiency, and capitalization of production, logistics, and distribution schemes, as well as consumer services. Is AI the prerogative of only large companies operating in international markets? What are the areas where AI technology is helping companies build effective business schemes? We will tell you in this article.

How global players are using AI tools

There are many tools for introducing AI into Business Analytics on the market. Examples are Data Robot and Alteryx, which provide Business Analysts with a friendly interface to improve employee productivity. These solutions allow specialists to save and catalog data in a form that is universal and convenient for processing by AI algorithms so that they make the necessary decisions in the future.

Among the leading players in the AI market for Business Analytics are Amazon, IBM, Nvidia, and Microsoft; as for popular products, we can mention such systems as Dataiku, Data Splunk, H20.ai, Modzy, SignalFx, and others. Microsoft, for example, offers a whole line of applications for its cloud platform Azure, which are defined as “mission-critical” by the vendor. With the help of AI, they perform such operations in data analytics as data management and verification, search and smart sorting, work with cloud storage, etc.

How other companies are putting AI into practice

When big players in the online retail market like Amazon, AliExpress, and eBay order data processing by AI models, it sounds cut and dried. Can companies that don’t belong to the high-tech industry or don’t view data as a key priority apply AI for data analytics and strategic planning? To answer this question, let’s look through a few examples.

Restaurant businesses

The use of Machine Learning algorithms helps restaurant businesses regularly analyze electronic orders, predict the dynamics of demand, and improve the speed of service. For example, Domino’s Pizza has optimized a number of its processes by using the NVIDIA DGX-1 server, an integrated computer appliance system for Deep Learning, and a Machine Learning operational platform (MLOP) – Datatron. The latter helps to track and adjust the performance of the used models in real-time.

The introduction of these technologies has enabled the chain to make its branch and online operations better, improve client service, and distribute orders more efficiently. For example, a self-learning delivery forecasting model, trained on five million orders, can predict when an order will be completed. For this purpose, the model takes into account the parameters of the order and the service department – for example, the number of employees, managers, and clients who have made an order. Powerful servers have reduced model training time to one hour, increased order prediction accuracy from 75% to 95%, and made it possible to streamline data exchange between different departments.

Zachary Fragoso, Data Science and AI Manager at Domino’s, gave the following recommendation on the application of AI technologies for a business:

“Think about how your data scientists will work together and collaborate. In our case, the DGX-1 and our data scientists are interacting in a common workspace. It was something that our team didn’t really consider when we first acquired this product and has been a real value for us.”

Satellite map creation

An example of an outstanding application of AI tools for data analytics can be seen in the Blackshark.ai platform, which also uses NVIDIA DRIVE Sim technology to create satellite 3D maps of the planet.

Satellite imagery information is used by government agencies – for example, to assess energy consumption or tax revenues, including determining the size of buildings by the visible elements of ventilation systems. Also, analysis of data from seismic areas and places of frequent floods and tsunamis helps government agencies, private companies, and insurance organizations to better calculate risk structures and damage amounts.

Semantic reconstruction of analysis results helps businesses and individuals make critical decisions. Previously, images were usually processed manually to provide visual information with explanations, and now AI algorithms are doing this at high speed, providing the necessary semantic decoding.

The use of 3D models helps to make technological forecasts for cellular companies based on the analysis of the terrain and landscape elements. They model geospatial functions and analyze the range of coverage and engineering costs for optimal ROI when placing 3G, 4G, and 5G towers.

Healthcare

When healthcare provider organizations work with large amounts of medical data, analytics of this data is in high demand. As an example of how AI technologies are already helping to fight the coronavirus pandemic, we will cite the development of GlobalSNS under the AID-Tes brand. The project is designed for Israeli clinics that use PCR instruments for Covid tests.

Files from these instruments are imported into the system, analyzed by a shape detector, a bias detector, and a slope checker, and are normalized using the Savitzky-Golay filter. Then, the data is passed to the Ct controller and the threshold value calculator and, if possible, the system calculates its value by passing it to the result interpreter. The built-in validator checks the accuracy of the received data in comparison with the control samples, after which the interpreter gives its evaluation of the test result: positive, weak positive, negative, invalid, or requires a re-evaluation.

Mark Malko, a Business Analyst at Andersen and a solution team member, notes:

The product is already used in clinics in Israel. It helps to interpret test results more accurately and rapidly (previously, it was done by the laboratory staff), which now requires fewer people. Since the work is carried out quickly, the clinic promptly informs patients about the test results, which reduces the likelihood of infecting other people.”

Banking

The influence of AI systems on the banking services’ performance indicators is convincingly demonstrated by McKinsey’s analytics.

Conclusion

AI tools for Business Analysis services and predictive modeling are widely used in a variety of areas, being in demand among both corporations and international enterprises and small companies. At the same time, although some developments have moved from the pilot stage of implementation to commercial use, a significant part of services and applications are at the promising startup stage. These new projects require investment from businesses that are seeking to optimize the performance of their processes.

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Christophe Rude

Christophe Rude

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