04 Jul 2019
How to benefit from Artificial Intelligence (AI) Technologies and Machine Learning (ML) in Data Analytics

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Contrary to the popular belief that advanced technologies such as Artificial Intelligence (AI) and Machine learning (ML) are going to take employment opportunities away, leaving skilled and unskilled workers stranded, recent research into the effects of adopting AI technologies for automating business processes can actually lead to an increase in revenue and market demand, thus creating new job opportunities.

However, without proper planning and building blocks in place, companies wouldn’t be able to truly capitalize on the new technologies and absorb all the benefits for increased efficiency and growing revenue. in this article, we examine a number of important factors such as data governance, system integration and connectivity, and business process streamlining, as well as other critical aspects which businesses need to consider when implementing the new AI technologies.

how-to-benefit-from-artificial-intelligence-ai-technologies-and-machine-learning-ml-in-data-analytics

Data is the new asset

Data provides insights which help companies understand, and able to predict what is likely to happen to their businesses. But in order to derive actionable insights out of data, organizations need to have their data unlocked and analyzed, ready to be leveraged with Artificial Intelligence (AI)/ML technologies. Otherwise, such big data would be useless and locked in unusable silos. Having a thorough understanding of how business data is stored and knowing its quality state is critical for organizations to prioritize and make use of its value. 

Thus, it’s important that businesses adopt a sound strategy for data governance, enabling proper data sorting and maintenance, transforming from a system of recording and storing data into a system of analysis, capable of performing advanced analytics and information processing.

#1 Data Governance 

Before data can be effectively put into use, there has to be an agreement and understanding regarding several key requirements. Careful consideration should be given to areas such as: data should be easily accessible for everyone (with appropriate rights) and providing insights so that informed decisions can be made. 

Besides, the level of access rights and depth of information must be assigned appropriately for each individual. For instance, a sales director will have a different level of access compared to a manager in the same department.  Additionally, the depth of information should be restricted by the skill level of the individual. Employees with high level of analytical skills should be given higher level of access rights to analytics data and technical tools, while others might only need access to dashboard report or applications.

#2 Tools 

Other than that, the tool sets that user can use to generate analytics information are also just as important. For example, developing a web application with intuitive and easy to use interface allowing business users to easily query “How much was the sales revenue of the last quarter” will be useful to a variety of individual responsible for the sales revenue. 

If the tools are developed properly meeting business requirements, they can provide business users with the information needed (e.g. report, chart, etc.) to make better and informed decisions. Furthermore, once the individuals involved in the decision making has the ability to retrieve analytics data and predictive insights, companies can start to improve their process and eliminate inefficiencies, driving higher results. And without access to reliable information, key business management individuals won’t be able to make the right business decisions.

#3 Consistent business processes

Developing a consistently and repeatable model of working process will provide organizations a base framework, on top of which organizations can easily automate.  For example, if companies want to adopt automation to improve their sales process, it’s important that each office implement its sales process in the same way.

Consistency in business process eliminates repetitive work and reduces the time taken to understand how different sections of businesses can perform the similar tasks. This can also help cut down on cost and improve collaboration between different teams.

Organizations can start by identifying key business processes and evaluating which areas bringing the most return and value. If such areas can be automated or enhanced with AI technologies, business efficiency and productivity will be enhanced as a result, and will lead to better competitive edge. Also, it’s worth noting that organizations should start small and simple. As a caution, do not start by trying to automate the most complex business process areas, it’s better to work incrementally and improve and optimize along the way.

#4 Consider application integration

In order to derive maximum benefits out of AI technologies, the entire enterprise business applications systems need to be integrated and interconnected to enable automation of processes, from beginning to end. This also allows automation to take place irrespective of the enabling system. However, this raises another important question about the single version of truth, with all employees working on the same data and analytics. Otherwise, different departments might be working on different set of data resulting in each making decisions based on their own information. The goal of making better and informed decision will be lost.

Integration also helps eliminate data re-entry and minimize risks of administrative errors, ensuring data accuracy and consistency. for example, if a manufacturing company doesn’t integrate its applications system to provide real-time access to data between the sales (e.g. Salesforce CRM) and back office inventory system (ERP such as SAP), it would be very difficult to accurately fulfill sales order, or quickly respond to new opportunities. Failure to meet customer’s demand will deter them from going forward with businesses, driving customers away to competitors’ businesses.

Therefore, participation from all levels and, in particular,  leadership from executive boards are crucial to ensure the success of adopting AI technologies across businesses. Leadership team should play an active role in communicating and foster an understanding within the employees about the opportunities and benefits that AI technologies can provide.

#5 Data Science and Predictive Analytics

Business insights derived from predictive and/or data analytics are usually only accessed by a number of key employees in certain departments. However, important business decisions can sometimes be made by many different business functions, so for a business to derive the most value and truly benefits from the decision making, analytics information needs to be used by all the stakeholders.

Moreover, there must be an agreement about the accuracy of analytics data company-wide. This will vary from company to company. As a general rule, the closer to 100 percent accuracy, the more costly the data will get. And once it’s become too costly, it’s very difficult for companies to achieve the required return on investment (ROI). Most of  the times, a good enough 80 percent data accuracy will provide enough insights to allow employees to make informed business decisions. Thus, it’s important that companies work with qualified team of data scientists and machine learning engineers to develop models that provide reliable analytics information.


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