5 Predictions for Analytics, AI And Data Management
Originally published at http://www.information-management.com/gallery/5-predictions-for-analytics-data-management-and-ai-10030637-1.html
Information Management published Reltio’s Top 5 predictions for 2017
1. AI and analytics vendor M&A activity will accelerate — There’s no doubt that there’s a massive land grab for anything AI, machine learning or deep learning. Major players as diverse as Google, Apple, Salesforce and Microsoft to AOL, Twitter and Amazon drove the acquisition trend this year. Due to the short operating history of most of the startups being acquired, these moves are as much about acquiring the limited number of AI experts on the planet as the value of what each company has produced to date. The battle for AI enterprise mindshare has clearly been drawn between IBM Watson, Salesforce Einstein, and Oracle’s Adaptive Intelligent Applications. What’s well understood is that AI needs a consistent foundation of reliable data upon which to operate. With a limited number of startups offering these integrated capabilities, the quest for relevant insights and ultimately recommended actions that can help with predictive and more efficient forecasting and decision-making will lead to even more aggressive M&A activity in 2017.
2. Data lakes will finally become useful — Many companies who took the data lake plunge in the early days have spent a significant amount of money not only buying into the promise of low cost storage and process, but a plethora of services in order to aggregate and make available significant pools of big data to be correlated and uncovered for better insights. The challenge has been finding skilled data scientists that are able to make sense of the information, while also guaranteeing the reliability of data upon which data is being aligned and correlated to (although noted expert Tom Davenport recently claimed it’s a myth that data scientists are hard to find). Data lakes have also fallen short in providing input into and receiving real-time updates from operational applications. Fortunately, the gap is narrowing between what has traditionally been the discipline and set of technologies known as master data management (MDM), and the world of operational applications, analytical data warehouses and data lakes. With existing big data projects recognizing the need for a reliable data foundation, and new projects being combined into a holistic data management strategy, data lakes may finally fulfill their promise in 2017.
3. Data monetization strategies will start to mature — For enterprises to tap into the data they use to run their businesses as a potential new revenue stream, the data must be reliable, relevant, segmented, secure, anonymized, if necessary, and audited to guarantee ownership of data. One would think that data providers who already sell data for a living would have the technologies in place to meet such requirements, as well as a way to automate and enforce the governance and licensing of their data, but that’s often not the case. However, data vendors are starting to incorporate distribution through Data as a Service (DaaS), opening up a more efficient way to track and license their data directly to enterprises, but also to offer data at the point of engagement. Companies who are managing their data as a strategic asset, and have hired a Chief Data Officer (CDO) to report directly to the CEO, find themselves in a position of being able to monetize their data assets in data lakes made reliable. Last year, Gartner highlighted that only 10% of CEOs said they monetize information assets by bartering with them or selling them outright. That number, fueled by modern data management technology, is sure to grow in 2017.
4. Cloud and data security agility will gain further importance — This is a rather obvious prediction, given the phobia of data breaches and the reticence of industries such as the financial sector to use public cloud technologies. Meanwhile, life sciences and retail, to name two industries, continue to forge ahead, realizing efficiencies while adhering to some of the strictest privacy and governance requirements set forth by regulators. With requirements such as the General Data Protection Regulation (GDPR) now in effect, companies not only have to ensure that their data is physically housed in the right geographic centers, but that the access complies with the most stringent regulations related to personal access and approvals for use of that data. Many vendors are now taking steps to provide the most secure, validated and agile infrastructure possible. Partnerships and use of Amazon Web Services, Google Cloud, and Microsoft Azure go a long way to providing the confidence and flexibility that many companies are looking for. In 2017, vendors offering Platform as a Service (PaaS) and tools themselves must also do their part in complying to Service Organization Control (SOC) types, as well as in the case of healthcare data, HITRUST (Health Information Trust Alliance), that provides an established security framework that can be used by all organizations that create, access, store or exchange sensitive and regulated data.
5. Systems of Record will have a path to Systems of Engagement, and beyond – In 2011, celebrated author Geoffrey Moore first defined the term Systems of Engagement (SoE), contrasting how Systems of Record (SoR) needed to evolve in order to focus on people, not processes. Forrester Research described it as “the perfect storm of mobile, social, cloud, and big data innovation to deliver apps and smart products directly in the context of the daily lives and real-time workflows of customers, partners, and employees.” In 2012, a wave of SaaS applications replacing legacy offerings, such as Workday for HR, brought forth new engaging user interfaces that were lauded for their ease-of-use and ability to drive collaboration among workers — the era of SoE had arrived. But the conversion to SoE has not happened overnight — many workers today remain on legacy systems that are siloed and unable to “join the party.” The apps are replaced at a methodical pace within large organizations, anchored by a complex network of bootstrapped integrations, which move information to solve problems on a one-off basis. 2017 may be the year more companies finally shift to SoE, in part due to increased use and adoption of AI. Last year, Geoffrey Moore extended his thinking towards Systems of Intelligence (SoI), combining AI with the big data scale of Internet of Things (IoT). In order to achieve true SoI, companies are now pushing to accelerate SoE in the form of data-driven applications for their workers.
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