Artificial Intelligence (AI) has been shaking up business technology landscapes for more than a decade now. As its use continues to advance at a rapid pace reshaping digital transformation strategies along the way. People’s perceptions of AI are also changing. We are now seeing more industries starting to embrace the unknown, as we welcome the introduction of an explosion of advanced technology and applications into the tech space.
A new vision has come to light, which is opening our eyes to the concept that AI is far less about the theory of ‘artificial intelligence’ replacing ‘human intelligence’ altogether and is more about the powerful amalgamation of the two.
In large companies, it is the consolidation of AI technology in the form of machine learning applications, and big data that result in the biggest impact in business by driving value and automation from data. We are now at a point where we can say that both AI and big data have reached a new stage in their lifecycle, moving into the maturity phase, each powerful in its own right but, when carefully combined the two have the potential to rapidly scale operations.
AI technology and iGaming
As an industry that absorbs innovation, the iGaming space welcomes new intelligence with open arms. AI technology has been implemented within the industry for some time now. It already works to aids systems and departments with issues such as identifying players with gambling addictive behaviours, fraud detection and increasing personalisation through tailored content and communication for better engagement.
Big data analysis
Irrespective of its size, accurate data is paramount to the success of any tech business, particularly in the fast-changing iGaming industry. Today companies collect large amounts of data daily, with both structured and unstructured data streaming taking place at an unpredictable rate. This can make the extraction of insights from the data a fairly challenging task.
The demands currently placed on data engineering departments are continuously increasing, putting a strain on data teams and the cost to maintain traditional extract, transform, load (ETL) processes are continuing to rise rapidly. This has introduced a new set of problems that were non-existent before. Data processing semantics are now being pushed to their limits and traditional methods are no longer enough to process data on time. This has resulted in the need for data engineers to be more creative with data architecture to solve a new problem — humongous data sets, i.e. Big Data.
Since the introduction of Hadoop, data processes have significantly changed the way organisations look at and store data for the better. The big data field has already helped to improve business intelligence, refine target marketing and enrich proactive customer service. When implemented effectively into a business department, it has the power to significantly reduce department costs.
Big data has resulted in traditional ETL processes being replaced by more intelligent and scalable paradigms with the streaming of events and microservices. This is mainly down to the fact that most data platforms are no longer primarily being used for reporting purposes, but instead for quantitative analysis and data science. In addition, complex regulatory requirements now demand flexibility in architecture, something that was close to impossible in achieving traditional textbook data warehouses.
But, it’s not a one-sided relationship. Advances in intelligence will always bring fresh challenges that need to be overcome if a full force revolution is to come into play. We are still a long way away from AI being able to create miracles on its own. Although reaching maturity, it is still at a stage where it is very much in need of a channelling partner for it to succeed the way it is intended—that is where big data comes in.
Artificial Intelligence and quality assurance
Incorporating methods of artificial intelligence; driving automated actions can certainly be beneficial, however not having proper change management and quality assurance in place can lead to irrecoverable damage to a brand’s trust. Regression testing with data sets tied with the environment that the model is running on is key in order to assert the results.
Part two of our data blog will take a look at Artificial intelligence and human intelligence working together to create a successful AI-powered business.