Five focus areas for success in advanced analytics

When it comes to advanced analytics, the question is no longer whether the technology will impact businesses, but to what extent and how quickly. Artificial Intelligence (AI), machine learning and data science are at the top of the senior management agenda across industries, and enterprises across the globe are looking to harness the power of these technologies to maintain competitive advantage and deliver best in class customer service.

The areas that need to be addressed in order to succeed in advanced analytics are actually in place in most businesses, but organizations are still not connecting the dots to achieve overall synergy:

1. The assumption that data is not good enough:
   Most companies assume their data isn’t good enough. The fact is, there is no perfect data. The way we look at data is different, but that shouldn’t stop organizations being able to drive value from it. We worked with an organization last year and proved that it could have huge benefits from insights produced by our analytics team. From production to improvements in customer service, and a reduction in spending on parts if it had early detection of failures.
2. Time to productionize:
   Organizations are struggling to overcome the challenge of driving the insights they have gleaned from their data to the platforms that they have built. It doesn’t make a difference whether they are combining traditional data repositories with Hadoop, or pure cloud repositories – it’s how organizations drive insight on that vast amount of data that really counts.
   Think Big Analytics worked with a leading bank on an AI solution to improve fraud detection compared to traditional methods. Once the solution was put into production, the benefits of machine learning became clear. Think Big Analytics was able to increase efficiency and deliver better support for our customers.
3. Working in silos:
   There are a variety of reasons why organizations are struggling to drive insights. Sometimes companies approach data insights from a purely data science point of view when they need to consider data engineering and create an architecture for the product solution, which takes a lot of time. But too often, data science and engineering departments are working in silos. This means business critical knowledge is not shared throughout the organization and the process is hindered as a result.
4. Machine learning model management:
   Machine learning management, or model management in general, is becoming a big concern across many different businesses. In fact, how businesses can make the most of machine learning and Artificial Intelligence (AI) is at the top of most C-level agendas, and if it isn’t now, it will be by the 2020. A lot of companies are starting to employ new talent to address this issue, but one of the challenges is that coding languages vary, which makes unification testing.
5. Utilizing use case-specific models:
   There’s a clear need for general-purpose pipeline management system. Ensuring that enterprise-wide use case models are consumed by models that are specific to the individual case, such as AI applications, is crucial. However, many businesses struggle with model infrastructure. Organizations just need to make sure that they are connecting the dots to ensure profitability.Ultimately, these challenges are common, but the solution to each is much closer than it may appear. Most of the time, it’s a process or operational issue within the organization that simply needs to be identified and rectified so that the real benefits of data can be realised.