Traditional methods for interacting with data are changing based on the speed and complexity of doing business, thanks in part to AI and machine learning.
CIOs and senior data professionals now have the ability to use algorithms and applications to drive smarter business processes that require less manual interaction with data. In order to maximize these opportunities, organizations need to establish automation that guides business users through data interactions to yield intended business outcomes.
Over the years, business applications – whether ERP, BI, CRM or others – have largely been designed to perform transactions, e.g., users input data that is stored, and then business processes are executed on that data based on the design and configuration of the system. The problem is that this model has only allowed business users to interact with one interpretation of a programmed business process. If the interpretation needed to change, it required a multi-skilled team to understand the new requirements, design, test, and implement the improvement.
Then companies started building data warehouses for analytics, highly customized extensions to transactional systems, and ad-hoc processes to overcome limitations in the ways business users wanted to interact with data. Despite the effort and innovation put into these types of solutions, they still required individual analysis by a business analyst or departmental subject matter expert, e.g., a finance specialist would be required to generate a meaningful order-to-cash report.
CIOs and technology leaders needed the application to do more of the thinking and analysis output so that their resources could be spent driving business value rather than keeping up with the maintenance and repetitive tasks typically associated with these types of solutions.
Machine Learning = Synthetic Consulting
The concept of AI and machine learning is now addressing this application delivery gap by opening up possibilities about how an application can execute a particular business process according to evolving best practices, industry nuances, and how users interact with the system.
Acting as a guide, embedded machine learning algorithms can help steer users to specific decision-making activities within applications. These algorithms apply related information and knowledge about particular subjects and existing datasets in order to drive an intended result. This includes enabling an application to advise business users on what they need to focus on to achieve meaningful insights.
More advanced machine learning systems, especially those in Robotic Process Automation (RPA) solutions, can fully augment or even replace end-user actions by making decisions autonomously and taking actions with little to no human oversight. While not widely adopted for productive use, these type of machine learning based solutions are becoming more prevalent in quality assurance and data wrangling pipelines.
Taking it a step further, if an organization is utilizing the services of a multi-tenant cloud provider, these learning and improving concepts can be applied on a much broader scale to provide recommendations and guidance based on the aggregate actions of the global user base.. These insights can ultimately help tech leaders leverage the wisdom and knowledge of a much larger training and working set of information and offer business users more relevant, competitive and strategic analytics and processes.
Simplifying the User Experience with Stronger Innovation
Innovating with machine learning does not have to be difficult or complex. In fact, many software vendors are exploring ways to build machine learning capabilities into their software to help eliminate mundane and complicated processes in lieu of high value guidance and automated processes. The key is finding the software and solutions that translate machine learning from a buzzword into an actionable component of the solution that solves meaningful problems for your users and your business.
As an example, if a company is implementing a new ERP or CRM solution, they are likely looking to leverage new, innovative ways of implementing business processes to extend beyond current capabilities. An investment in these new technologies gives organizations a compelling event to embark on or further progress a data transformation.
As the company travels this journey, the use of machine learning can introduce smart machines to the processes formerly reserved only for smart humans. When smart humans and smart machines come together, the business benefits and competitive advantages from the digital transformation can be magnified.
In order to maximize these benefits, however, the data that is used to drive the processes executed by both the people and machines needs to be trusted today, assured for the future, and understood for clarity. Machine Learning, while needing this caliber of data to operate at its highest level, can also be used to create trusted, assured, and understood data.
As an example, finance department professionals may be guided to flag IFRS 17 as a critical compliance regulation prior to implementation. Or global businesses can be prompted to include specific policies related to export provisions. For both examples, machine learning capabilities could suggest relevant policies or rules that should be auto-configured into the data transformation process. This type of machine-driven guidance is helpful to CIOs because it can help them empower their organizations to achieve overall business goals.
While many organizations have previously approached digital transformation and data management as standalone projects with a set of structured steps to follow, the adoption of AI and machine learning is now allowing them to reap the benefits of a more holistic, fluid approach. By viewing digital transformations as a new opportunity to view data through the lens of the business and its unique needs, tech and data leaders can offer business users relevant tools with nuanced knowledge to continuously improve their data, their processes and their lives.
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