Insight Automation Platforms have the work cut out for them: delivering on the promise to fully automate the process of going from data to insight requires a significant engineering effort and close collaboration across multidisciplinary teams that rarely work together.
Present and Future of Data Science in the Enterprise
Data Science, Artificial Intelligence (AI) and Machine Learning (ML) technologies are being adopted in the enterprise faster than ever before. This adoption is possible largely thanks to lower hardware costs and the maturity of the underlaying technologies. To implement these solutions, engineers combine multiple tools from different vendors, customize data ingestion, develop machine learning models and customize the integration of data outputs with Business Intelligence (BI) and other downstream tools and processes. To help simplify this process, a new breed of tools called Data Science and Machine Learning (DSML) platforms have emerged. DSML platforms enable data science teams to combine these disparate solutions in a coherent, all-in-one tool.
The Holy Grail is Still Elusive
Businesspeople and subject matter experts (SME) are the “end users” of insight. Ultimately, they use insight to make domain-specific decisions. Despite the efficiency gains enabled by DSML tools, SMEs must continue to rely on data scientists to build the pipelines needed to process an organization’s data. This approach creates a separation between SMEs and the methods through which the insight is created. This absence of domain-expert input into the decisions made by data scientists while building the pipelines—for example, while deciding which data points should drive predictions—can lead to inaccurate insights. Frustration only increases when the outcomes are the result of a drawn-out period of interaction between SMEs and data science teams.
According to Gartner, As data complexity increases, businesspeople across the enterprise are awash in data, struggling to identify what is most important and what best actions to take […] Across the analytics stack, tools have become easier to use and more agile, enabling greater access and self-service. However, many processes remain largely manual and prone to bias. […] Using current approaches, it is not possible for users to explore every possible combination and pattern, let alone determine whether their findings are the most relevant, significant and actionable.Source: Augmented Analytics Is the Future of Analytics.
Insight Automation: A Step in the Right Direction
An insight automation platform offers built-in machine learning functionality that automates many aspects of the DSML and BI pipelines. Their primary goal is to enable businesspeople to access insights in a self-serve manner enabled by an intuitive user experience. These tools obfuscate complex engineering behind a deceivingly-simple interface. The technology packed into these tools provide out-of-the-box functions that automate different aspects of the traditional data science and machine learning processes. This enables SMEs to have more direct involvement in deciding how insight is derived. The results not only benefit the business, but also have the potential to free up data scientists to focus on higher value-adding tasks by automating most of their grunt work.
Gartner goes on to say: By automating many aspects of DSML development, management and deployment of augmented analytics in DSML platforms (augmented DSML), expert data scientists become more productive. This also extends DSML model building to a broader range of less skilled users including new citizen data science roles (business analysts, developers and others).Source: Augmented Analytics Is the Future of Analytics.