2018 is Big on Organizing Data with AI

In Data Governance, Data Management, Master Data Management, Reference Data Management by IRM UKLeave a Comment

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With the first quarter of 2018 behind us, we now have a taste of the top trends in data management. And by all accounts, the predictions are spot on! The GDPR (General Data Protection Regulation) going effective in EU from May 2018 has been a big mover for data organization and governance. The strict laws and heavy penalties of GDPR have made companies sit up and take stock of their data management strategies. To meet the stringent requirements of GDPR, data management must leverage Artificial Intelligence and Machine Learning, and that begins with organizing your data correctly.

Ramon Chen, Chief Product and Marketing Officer, Reltio
Ramon will be speaking at the Master Data Management Summit & Data Governance Conference Europe 14-17 May 2018 on the subject “Future-proofed Cloud MDM – Organise ALL your data for continuous self-learning.”

Building a reliable data foundation with the help from AI (Artificial Intelligence) leads the current top trends in Data Management:

1. Enterprise Data – from Management to Organization

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 other environments. But Business teams have trouble getting their hands on reliable, relevant, and actionable data with those isolated initiatives. 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.

Organizing data – any data type and from any source -, with ongoing contribution and collaboration on limitless attributes, is the new rallying cry for frustrated business users – ensuring a stronger focus on continuous data organization leading to continuous self-learning.

2. IA for AI

Over the last few years, a breakthrough has been expected in enterprise use of Artificial Intelligence (AI) and Machine Learning (ML). While there are no shortage of startups and innovations, the reality is that most enterprises are yet to see quantifiable benefits from their investments. 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 datasets.

The key here is that while the headlines speak mostly about AI, most enterprises really need to first focus on IA (Information Augmentation), getting their data organized so that it can be reconciled, refined, and related; to uncover relevant insights that support efficient business execution across all departments, and address the concern of regulatory compliance.

3. Measurable Results and Clear ROI

While being data-driven is still in vogue, companies have had surprisingly little in the way of measurable, quantifiable outcomes for their investments in technologies and tools. Total Cost of Ownership (TCO) metrics such as savings realized from switching to cloud vs on-premises are obvious, but there hasn’t been a clear direct correlation between data management, BI, analytics, and the 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. Today’s Modern Data Management Platforms as a Service (PaaS) seamlessly power data-driven applications which are both analytical and operational, delivering contextual, goal-based insights and actions. The insights are specific and measurable, allowing outcomes to be correlated, leading to a clear assessment of ROI (Return on Investment) and forming a foundation for AI to drive continuous improvement.

4. Multi-cloud is the New Normal

Multi-cloud means choice and opportunity to leverage the best technology for the business challenges companies face. Unfortunately, multi-cloud is not realistic for all, only the largest corporations who have the IT teams and expertise to research and test out the latest and greatest from multiple providers. Even those mega-corporations are finding that they have to stick to a single IaaS (Infrastructure as a Service) Cloud partner to focus their efforts.

Today’s Modern Data Management PaaS are naturally multi-cloud, seamlessly keeping up with the best components and services that solve business problems. Acting as technology portfolio managers for large and small companies who want to focus on nimble and agile business execution, these future-proof platforms are democratizing the notion of multi-cloud for everyone’s benefit.

5. Offense Strategies Bring Defense for Free

Effective from May 25, 2018, the GDPR has forced organizations to meet a very high standard of managing data. They must evaluate how they collect, store, update, and purge customer data across all functional areas and operational applications, to support “the right to be forgotten.” And they must make sure to have valid consent to engage with the customer and capture their data.
Meeting regulations such as GDPR often comes at a high price of doing business not just for European companies, but multinational corporations in an increasingly global landscape. With security and data breaches making high-profile headlines regularly, it has become an increasingly tough business environment, as the very data that companies have collected in the hopes of executing offensive data-driven strategies, weighs on them heavily, crushing their ability to be agile.

The solution lies in actively pursuing offensive strategies with the Modern Data Management PaaS that seamlessly incorporates transactions and interactions to deliver a 360 view. When coupled with AI, Machine Learning, and predictive analytics with pre-built algorithms, it gives continuous awareness of the quality and business value of your data with scores and metrics.

Organizing data for the benefit of Machine Learning results in clean reliable data that is connected and forms a trusted foundation. With relationships uncovered across people, products, places and organizations; a natural byproduct is a defensive data strategy to meet regulations such as GDPR, ensuring secure compliant access by all parties to sensitive data. This is an amazing bundle from which regulatory teams and business management teams can both benefit.

For all the industry or business needs, organizing data has now become an absolute top priority for companies big and small. And the rest of the 2018 is heading to reinforce that strongly.

Ramon Chen is the Chief Product and Marketing Officer for Reltio, a Cloud MDM platform that delivers continuous Self-Learning Data Organisation. Reltio is used by companies of all sizes and industries including the largest companies in the world to organise billions of multi-domain profiles, relationships, and interactions at petabyte-scale, and for day-to-day operational activities by thousands of IT and business users globally. Prior to Reltio, he was VP of Product Marketing for Commercial, which encompassed Veeva CRM, Veeva Approved Email, Veeva Vault and Veeva Network at Veeva Systems. He has over 25 years of experience running marketing and product management teams at RainStor, Siperian, GoldenGate Software, MetaTV, Evolve Software, Sterling Software and Synon Inc. He holds a BS in Computer Science from Essex University. Ramon was recently named one of the most influential people in healthcare.

Copyright Ramon Chen, Chief Product and Marketing Officer, Reltio

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