As 2017 ends, Ramon Chen, Chief Product Officer at Reltio, the creator of data-driven applications, has peered into his crystal ball to decipher what 2018 will bring in data management. Find his predictions below.
2018 will be the year of AI and Machine Learning … again: There have been repeated predictions over the last couple of years touting a potential breakthrough in enterprise use of Artificial Intelligence and Machine Learning (ML). While there are no shortage of startups - CBInsights published an AI 100 selected from over 2000+ startups - the reality is that most enterprises have yet to see quantifiable benefits from their investments, and the hype has been rightly labelled as overblown. In fact, many are still reluctant to even start, with a combination of skepticism, lack of expertise, and most of all lack of confidence in the reliability of their data sets.
In fact, while the headlines will be mostly about AI, most enterprises will need to first focus on IA (Information Augmentation): getting their data organized in a manner that ensures it can be reconciled, refined and related, to uncover relevant insights that support efficient Business execution across all departments, while addressing the burden of regulatory compliance.
Enterprise data Organization, not management, will be the new rallying cry: For over 20 years, the term data management has been viewed as a descriptor, category and function within IT. The term management represented a wide variety of technologies ranging from physical storage of the data, to handling specific types of data such as Master Data Management (MDM), as well as concepts such as data lakes, and other environments. Business teams have lost patience with the speed, and efficiency in which they are able to get their hands on reliable, relevant and actionable data. Many have invested in their own self-service data preparation, visualization and analytics tools, while others have even employed their own data scientists. The common refrain is that data first has to be made reliable, and connected with the rest of the enterprise, so that it can be trusted for use in critical business initiatives, and isolated initiatives such as MDM and Hadoop-powered data lakes have not been successful.
Organizing data across any data type or source, with ongoing contribution and collaboration on limitless attributes, will be the new rallying cry for frustrated business teams as it describes a state of continuous IA (Information Augmentation) that enterprises want to achieve before they can even consider AI as a potential next step.
Data-driven organizations will expect to measure outcomes: While being data-driven continuous to be vogue, companies have had surprisingly little in the way of measurable, quantifiable outcomes for their investments in technologies and tools. Certain Total Cost of Ownership (TCO) metrics such as savings realized from switching to cloud vs.on-premises are obvious, but there hasn’t been an obvious and clear direct correlation between data management, BI, analytics and the upcoming wave of AI investments. What’s missing is a way of capturing a historical baseline, and comparing it to improvements in data quality, generated insights, and resulting outcomes stemming from actions taken.
Much of this can be attributed to the continued disconnect between analytical environments such as data warehouses, data lakes and alike where insights are generated, and operational applications, where business execution actually takes place.
